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SNNAP TUTORIAL - Neurobiology and Anatomy

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1. SNNAP On Line Simulation CASNNAPISNNA P3itutorialitutorialExamples hhM odel hh smu Tue Dec 03 16 54 29 GMT 06 00 The University of Texas Health Science Center at Houston Figure 3 14 Hodgkin Huxley model oscillations with reduced maximal potassium conductance Exercise Starting from the original Hodgkin Huxley values the model can exhibit oscillatory behavior if the sodium conductance is increased Can you find the minimum maximal sodium conductance to achieve oscillatory behavior 37 Tutorial Manual for Version 8 of SNNAP CHAPTER 4 BURSTING NEURONS AND CENTRAL PATTERN GENERATORS Many neurons show cyclic behavior of repetitive spiking activity followed by a period of inactivity Such behavior is called bursting Numerous types of cells exhibit bursting behavior for example central pattern generator CPG neurons in invertebrates thalamic neurons in mammals and pancreatic beta cells In this chapter we will develop a neuron model which exhibits bursting behavior starting with a single spiking neuron model You will learn how to incorporate new ion currents to a model as well as intracellular second messengers This chapter is divided into the following sections Introduction to Bursting Neurons Neurons which exhibit periods of high frequency activity followed by inactivity play an important role as central pattern generators The Morris Lecar Spiking
2. I x channel number 1 first variable Cion Ca ML lt sw 7 second variable NULL 7 number of ticks 10 minimum 0 0000 2 maximum 1 8604754 2 color name E red Figure 4 20 Declaring a second output variable The variable we are interested to display is the Ca variable of the ion equation Note that the value can not decrease below zero but in order to shift the display we are using 2 as the lower bound After closing the variable window by clicking Ok save the output window File gt Save and rerun the simulation 58 Tutorial Manual for Version 8 of SNNAP EE a cc Fa SNNAP On Line View of Simulation ioj x File Graphic Output Data Output Report Help 20 30 0 _ Cion CaML lt ive VML lt ivr kiwhite 0 06 18 24 3 0 ee time ivr fee NNAP On Line Simulation CASNNAP SNNAP8itutorialitutorialExamples bursterCPGiml smu Fri Dec 13 16 31 32 GMT 06 00 21 The University of Texas Health Science Center at Houston Figure 4 21 Bursting model displaying membrane potential V and intracellular calcium Ca The model simulation of three seconds displays three full burst periods The calcium concentration oscillates as well and is responsible for ending the burst at its peak value by activating the calcium dependent potassium current MULTIPLE OUTPUT GRAPHS There are numerous system variable
3. Figure 4 3 Add Node dialog box to add new conductance to model 43 Tutorial Manual for Version 8 of SNNAP The next step is to establish the equations for this new conductance But first we must create a file named m1KCa vdg SNNAP is provided with a formula template library folder formulaTemplates where you can access all the equations that may be used with SNNAP In the main SNNAP window click button Edit Formula In the window Formula Editor click the button vdg The file chooser dialog box will be displayed The template file we need is default vdg in the folder SNNAP8ltutoriallformulaTemplate Click on the file name to select it and click the button Open The window Editor for vdg File will be displayed as in Fig 4 4 Click the Edit button to display the menu of equations SNNAP Editor for vdg File i vdg Equations xi CASNNAPISNNAPStutorialiformulaTemplateside Open Save Save As DAI Ga E R ANB Joya Gy V E TIER Ga B R m Ne Jog Gog V E TL ABR Gya Z R AY Lua Gyg V E TIER Goa g R m t Log Gyg V E TIER Gad ZF R SG Lua Gug E TIER Figure 4 4 Voltage dependent gated channel equations 44 Tutorial Manual for Version 8 of SNNAP Poe Spe ews The menu of equations provides you with various conductance models to choose from The first four equations models use voltage dependent activation Our calcium
4. CONDUCTANCES ale f a ION gt TRI eal c K a ro IONS POOL SM gt TRF ION gt SM SM POOL E GY t 5M gt 5M j E a The University of Texas Health Science Center at Houston Figure 4 7 Adding an ion element in the Edit Neuron window To add an ion pool click Edit gt Add Node gt lon which will open an Add Node dialog box Figure 4 8 shows the dialog box with the corresponding parameter values 47 Tutorial Manual for Version 8 of SNNAP re o O OOO SOON O COA CT x lon name Ca File name mica ion E Color name 7 E navyblue r Figure 4 8 Declaring a new ion in the model Edit the dialog box and enter the name of the ion Ca and the file name which will contain the equation for this ion mlCa ion Choose a color for the display of the ion and click Ok The Edit Neuron window will be updated to display Cain the IONS POOL pane left side and the an icon with Ca in the color you selected will be displayed in the center pane see Fig 4 9 The next step is to connect the processes that will be using this ion pool The calcium current will be the source of Ca ions and the ion concentration will activate the current K Ca To indicate to SNNAP that a current is an ion source click Edit gt Add Connection gt Current_to_lonin the Edit Neuron window as shown in Fig 4 9 48 Tutorial Manual for Version 8 of SNNAP
5. 3 n 8 fee ON NAP On Line Simulation CASNNAPVSNNAP8MtutorialttutorialExamplesthhN etwork hhN etwork smiiri Jan 03 09 24 16 GMT 06 00 20 The University of Texas Health Science Center at Houston Figure 5 1 Three neuron network connected in a ring architecture Figure 5 1 shows the behavior exhibited by the three cell network while Fig 5 2 presents the network architecture The first neuron to fire blue colored trace was stimulated by a current injection from time 0 to 0 003 sec 3msec with an intensity of 5uA cm2 This neuron then causes a synaptic current not shown which stimulates the second neuron red colored trace The neuron shown in red after firing an action potential causes a synaptic current which stimulates the third neuron shown in color green which fires in turn Now the third neuron green is connected to the first neuron blue and thereby stimulates it with a synaptic current so that the firing cycle begins again 69 Tutorial Manual for Version 8 of SNNAP seen swore EF SNNAP Edit Network 5 x File Edit ColorSetting Print Help Neurons CSNNAPISNNAPS tutorialitutorialExamplesihhNetworkihhNetwork ntw Connections Neuron 1 Neuron 1 lt Neurorl Neuron 2 Neuron 2 lt Neuror Neuron 3 Neuron 3 lt Neurorl The University of Texas Health Science Center at Houston Figure 5 2 Architecture of three neuron network The three neuron network is
6. Detailed instructions can be found at http java sun com j2se 1 4 install html ii Download SNNAP a Open the website http snnap uth tmc edu b Click download and click the version you desired c Save SNNAP in a directory of your choice e g tmp d Go to the directory and issue unzip SNNAP8 zip for Version 8 it will create a SNNAP8 directory e Move SNNAP8 to desired places e g usr local with mv tmp SNNAP8 usr local iii Run SNNAP in Linux a Login as root and edit etc profile adding a line at the end alias snnap java jar usr local SNNAP8 SNNAP8 jar b Login as a normal user and type snnap SNNAP8 will be launched SQ D OTD 18 Tutorial Manual for Version 8 of SNNAP STRUCTURE OF TUTORIAL EXAMPLES DIRECTORY The tutorial contains several examples which are provided to you in specific folders The directory structure is as follows initialModel archive hhModel tutorialExamples cpgBurster hhNetwork Figure 2 2 Folder structure of tutorial examples SNNAP is provided with three tutorial examples You can find the three examples in folders hhModel cpgBurster and hhNetwork These are the working folders as described in the chapters The folder archive contains copies of the initial model in folder initialModel as well as the final model in folder finalModel which corresponds to the final figure shown for each model in the corresponding chapter Thus you can work on a
7. blue Next add two variables one for the alpha function variable fAt see Fig 5 3 aad one variable x channel number 2 first variable fAt Neuron 2 gt Neuron 1 exc Jefnr ad second variable NULL v number of ticks 5 minimum 0 0000 0 maximum 0 2031603 5 color name E blue v Figure 5 3 Synaptic current variable from neuron 1 to neuron 2 and a second for the total synaptic current variable Ics see Fig 5 4 71 Tutorial Manual for Version 8 of SNNAP Figure 5 4 Synaptic current in neuron 2 We can now rerun the simulation and view the trace of the synaptic current and alpha function from neuron one to neuron two 72 Tutorial Manual for Version 8 of SNNAP File Graphic Output Data Output Report Help 600 _ 60 0 VNeuron 2 Jest V Newron 3 lt ivr V Neuron 1 svr ao ao 4 20 20 oo oo J 200 200 swo so 400 oo 4 300 200 J 0b 0 004 go 0012 0 016 082 time vr 50 05 Ics Neuron 2 gt Neuron 1 exffst RNetron 2 gt Neuron lexce e n 04 4 03 02 4 0 1 0 0 A 0 008 time 17 fee ONNAP On Line The University of Texas Health Science Center at Houston Figure 5 5 Three neuron network with synaptic input and current in neuron 2 Figure 5 5 presents the synaptic current red curve in lower trace from Neuron_1 blue to Neuron_2 red When th
8. value 1 respectively and a driving force for further detail see Section Hodgkin Huxley Model The potassium activation variable w is defined by a steady state activation function w a sigmoid curve and a time constant function 7 a bell shaped curve 1 w V 12 1 h V el w 2 5 e V h 2 s t The calcium current also uses a steady state activation function m which is defined as 1 mT Simulation of the Morris Lecar Model The simulation of the Morris Lecar model is similar to running the Hodgkin Huxley model see Section Running a Simulation in Chapter 3 Open the main SNNAP window 40 Tutorial Manual for Version 8 of SNNAP Click the button Run Simulation to open the simulation window On Line View of Simulation as shown in Fig 4 1 Click the menu File gt Load Simulation Locate and select the file SNNAPhome SNNAP amp tutorial tutorialExamples bursterCPG ml smu in the file chooser dialog box and click Open Click the button Start Fa SNNAP On Line View of Simulation ioj x File Graphic Output Data Output Report Help 300 _VOML J lt i 20 0 10 0 10 0 20 0 30 0 40 0 50 0 0 0 0 2 06 0 8 10 Sac time ivt fee ONNAP On Line Simulation CASNNAPWSNNAPS8itutorialitutorialExamples bursterCPGiml smu Fri Dec 06 14 15 44 GMT 06 00 21 The University of Texas Health Science
9. 79 Tutorial Manual for Version 8 of SNNAP EF SNAP Edit Network 5 x ColorSetting Print Help CASNNAPISNNAPSitutorialtutorialExamples hhNetworkihhNetwork ntw FR Neuron 1 lt Neuror Neuron 2 lt Neuror Neuron 3 lt Neuron Add Connection gt Add I amp F Cell n2 A The University of Texas Health Science Center at Houston Figure 5 11 In the Edit Network window add a neuron The Add H amp H Neuron dialog box will open Enter the values as seen in Fig 5 12 you may choose any cell name you prefer as well as color The important parameter is the file for the neuron description which should be Neurons hhNeuron neu Note that we must provide the path from the folder where the smu file resides using Unix like slash 7 rather than Windows like back slash W Note For Linux users you must provide the pathname from the folder where snnap8 jar IS located i e tutorial tutorialExamples hhNetwork Neurons hhNeuron neu 80 Tutorial Manual for Version 8 of SNNAP add H amp H Neuron Ex Cell name Neuron_4 File name sihhNeuron neu Color name gold Figure 5 12 Dialog box to enter neuron name and respective equation file Once we are satisfied with the parameter values click the button Ok The fourth neuron will now appear as part of the network as shown in Fig 5 13 Adding a Synaptic Connection in a Network We no
10. Modify Simulation Exit Clear Print S Print Color Fg p Background Exit The University of Texas Health Science Center at Houston Figure 3 3 Main simulation viewing window Click File gt Load Simulation to open the file chooser window x Lookin SNNAP8 l ft co e C examples Exe FileName Files of Type File type smu v Open Cancel Figure 3 4 File chooser window 20 Tutorial Manual for Version 8 of SNNAP eee ea The file chooser window opens at the top level of the SNNAP directory providing you with the available folders directories Double click the folder icon next to the folder named tutorial The file chooser will open the tutorial folder double click the folder named tutorialExamples The folder tutorialExamples contains the example folders for this tutorial one folder per chapter Double click the folder named hhModel Open the file hh smu either click once and click the Open button or double click the file icon The file will be loaded into the simulator Note If your model is a large file the loading process may take several seconds Once the left hand buttons are enabled they will display their title in black If the button titles are in grey they are not functional Click the Start button to begin running the simulation 26 Tutorial Manual for Version 8 of SNNAP Fa SNNAP On Line
11. CAI cy y E SNNAP Edit Neuron File ColorSetting Print Help Undo C SNNAP Tutorial_examples burster_test ML neu CURRENT gt ION Thres cut Spikd Modify tn MIN et Cmq AddNeuron gt teak ma aid 1ON gt CURRENT ee TT Trnsmter_by_lon TRNSMIR POOL Trnsmter by Sm Neuman Sm by lon EET Sm by Sm CONDUCTANCES IK Ca ION gt TRI leak ooe Ca K IONS POOL SM gt TRF Ca p S i EE SM POOL 9 J Ca SM gt SM The University of Texas Health Science Center at Houston Figure 4 9 Converting current to ion concentration in Edit Neuron window The Add Connect dialog box will be displayed see Fig 4 10 Set the Conductance Name to Ca by using the pull down menu click in the box to the right of the text Conductance Name Since there is only one ion declared in SNNAP the Ion name is correctly set KE jasacomet ned Conductance name Ca v lon name Ca v Color name E blue y By areal Figure 4 10 Select current source for ion 49 Tutorial Manual for Version 8 of SNNAP gree gt gt gt gt 0500 Click the button ok when you are done you may also choose a color using the pull down color menu An arrow will be displayed in the Edit Neuron window connecting the Ca conductance icon circle and the Ca ion icon brackets see Fig 4 12 To set the current which will be affec
12. b Save SNNAP in a folder of your choice but the directory path may NOT contain a blank character e g SNNAP Files is NOT a valid folder c Unzip it with Aladdin Stufflt Expander freeware program ii Install Java SDK for Mac a MacOS X bundles Sun s Java 2 Standard Edition J2SE version 1 3 or higher 3 Downloading and installing Java and SNNAP on computers with the Linux UNIX operating system i Download Java 2 to the computer or the most recent version Go to website http java sun com Select Standard Edition J2SE Select J2SE Downloads Select J2SE 1 4 1 or latest version Select from table Linux RPM or simply Linux self extracting files Downloads Follow the instructions and save Java 2 SDK on the directory of your choice for example tmp The file has a name like j2sdk 1_4_1_01 linux bin Change to the directory you saved downloaded file e g with cd tmp do chmod 774 j2sdk 1 4 1 01 linux bin to change the permission then do j2sdk 1 4 1 01 linux bin For RPM format you will get a file 2sdk 1 4 1 01 linux rpm Login as root or su to root then use rpm ivh tmp rpm to install j For GNUZIP tar ball you will get a directory containing JDK like jdk1 4 1 01 Login as root or su to root and do mv jdk1 4 1_01 usr local h Edit etc profile and add usr local idk1 4 1_01 at the end of the PATH line e g PATH usr bin bin usr X11R6 bin usr local bin usr local java jdk1 4 1_01 bin
13. i e tutorial tutorialExamples hhNetwork Synapses hhSyn cs 82 Tutorial Manual for Version 8 of SNNAP bx add Chemical Pre Neuron 3 Post Neuron 4 Receptor type exc File name apses hhSyn cs E Figure 5 15 Chemical synapse dialog box After providing the parameters for the two connections and deleting the connection Neurons 3 gt 1 the network window should look like Fig 5 16 Click the menu item File gt Save to save the network changes in SNNAP 83 Tutorial Manual for Version 8 of SNNAP rer SS EEE lp File Edit ColorSetting Print Help Neurons CASNNAPISNNAPS tutorialttutorialExamples hhNetwork hhNetwork ntw Connections Neuron 1 Neuron 1 lt Neuror Neuron 2 Neuron 2 lt Neuror Neuron 3 Neuron 3 lt Neurorl Neuron 4 Neuron_4 lt Neuron i enn The University of Texas Health Science Center at Houston Figure 5 16 Four neuron network The last step is to add the fourth neuron to the variable list of the output screen Following the procedure in section Adding New Output Variable Chapter 4 Add a new variable V Neuron 4 to the channel 1 trace with number of tick marks set to 7 and min max values equal to 80 60 Set the color to gold or any color you desire Confirm the changes by clicking ok and confirming Yes and now rerun the simulation Figure 5 17 shows the behavior of our four neuron network The firing freq
14. tutorialltutorialExamplesthhN etwork hhN etwork smirri Dec 20 09 47 04 GMT 06 00 2 The University of Texas Health Science Center at Houston Figure 5 9 Three neuron network with synaptic conductance of 2mS cm2 Notice that the network oscillates at a higher frequency than previously The neurons increased their firing frequency from 125Hz Fig 5 5 to 167Hz Lets change the maximal synaptic conductance to 5mS cm2 Repeat the process described above and set the g value to 5 MS cm2 Rerun the simulation You should also changed the synaptic output variable to a min max 80 40 range TT Tutorial Manual for Version 8 of SNNAP File Graphic Output Data Output Report Help 600 _ 60 0 _ Neuron_2 J vr V Newron 3 lt ivr V Neuron 1 svr ao ao o xo oo oo J 2001 20 swo swo s0 oo soo soo J ab 0 004 0008 0012 0 016 082 time vr 40 0 0 5 Ics Neuron 2 gt Neuron 1 exdffst Neguon 2 gt Neuron lexe n 7 04 4 oF 02 4 0 1 00 J 0 008 times vr The University of Texas Health Science Center at Houston Figure 5 10 Three neuron network with synaptic conductance of 5mS cm What has happened The increased synaptic conductance causes the oscillations to extinguish For the blue neuron the first interspike interval is 4ms 250Hz while the second interspike interval is even shorter because of the increase in synaptic cur
15. Center at Houston Figure 4 1 Simulation of the Morris Lecar model Parameters are Cm 20uFlcm g c 4 0mS cm gx 8 0mSlcm g 2 0mS cm2 Ve 120mV Vg 84mV V 60mV 1 15 h 1 2 s 9 h Ww Description of the Morris Lecar Model The Morris Lecar model consists of three currents a calcium current a potassium current and a leak current The calcium current is the inward current similar to the sodium current in the Hodgkin Huxley model The activation of the calcium current is a fast process in comparison to the activation of the potassium current and is therefore 41 Tutorial Manual for Version 8 of SNNAP ER RT modeled as instantaneous using the steady state activation function rather than a time dependent variable This means that for any value of voltage V the steady state function m V is calculated This calcium current does not incorporate any inactivation process The potassium current has an activation variable w similar to the Hodgkin Huxley variable n though with no power exponent Finally the leak current is similar to the HH model Note The original Morris Lecar model described the activation and time constant functions using hyperbolic tangent and cosine functions We have converted those functions to the more commonly used sigmoidal functions using Boltzmann like exponential functions ADDING IONIC CURRENTS We will now add a new ionic current to the Morris Le
16. File gt Open and then click HH smu and the button Open 34 Tutorial Manual for Version 8 of SNNAP ES SNNAP Edit Simulation File ioj x File Load Help CXSNNAPISNNAP8 tutorialtutorialExamples hhModelihh smu Logical name HH Time_to_start 0 0 Time_to_stop 0 1 Step size 1 0E 5 Integration_method 1 O I amp F step size 0 0020 Network HH ntw Output Setup HH ous Treatment HH trt On_line_graph v Store results The University of Texas Health Science Center at Houston Figure 3 12 Window to edit simulation parameters Change the entry Time to stop to 0 1 seconds 100ms Click button Ok Click button Yes in the window Saved Modified File The simulation will now run for 0 1 seconds rather than 0 01 seconds as before But if you rerun the simulation now you will not see any different in the output window The output window also needs to be changed to reflect the new simulation time duration Changing Output Display The output display is controlled through the Edit Output Setup window 35 Tutorial Manual for Version 8 of SNNAP Click the button Edit OutScreen in the main SNNAP control window to open the Edit Output Setup window see Figure 3 13 Open the hh ous file that is in folder hhModel Click on the time variable in the channels pane on the left which will highlight the selected variable with a b
17. Model Conductance Parameter To change model parameters click the button Edit Formula in the main SNNAP window to open the formula editor 31 Tutorial Manual for Version 8 of SNNAP re a O O EF SNNAP Formula Editor 5 xf The University of Texas Health Science Center at Houston Figure 3 8 Formula Editor provides you with buttons to change model parameters The Hodgkin Huxley model is a neuron model that uses voltage gated channels In order to edit such channels click the button vdg The file chooser window will open Click on the folder named tutorial followed by the folder named tutorialExamples and finally on the folder named hhModel The model contains three channels we are interested in the potassium channel so either double click on the hhK vag file or click once and then click the button open This will open the window Editor for vdg File 32 Tutorial Manual for Version 8 of SNNAP i eee SNNAP Editor for vdg File xi CSNNAPISNNAPS tutorialtutorialExamplesihh Open Save Saveas Edit P Gu B Rk mo ibr Lua Gag V MD ABR a 3 TE 1 A W SA mv n by 3 X Figure 3 9 Editor for voltage dependent currents The parameters that you may change are marked by a grey background Click on the maximal conductance parameter g Parameter g 16 0 my aa Figure 3 10 Window for modifying maximal conductan
18. Neuron Model The Morris Lecar neuron model is a minimal biophysical model which exhibits single action potential Adding lonic Currents How to add additional currents to a neuron model in SNNAP Adding lon Pools How to add intracellular ions to a neuron model in SNNAP Adding Output Graphs How to overlay output displays with SNNAP Central Pattern Generator Modulation Multiple model parameters allow modulation of bursting behavior 38 Tutorial Manual for Version 8 of SNNAP INTRODUCTION TO BURSTING NEURONS Many invertebrate as well as mammalian neurons are bursting cells They exhibit alternating periods of high frequency spiking behavior e g 100Hz followed by a period with no spiking activity quiescence period The rich dynamic behavior of such neurons attracted both neuroscientists and mathematicians in an effort to understand the underlying mechanisms of bursting behavior and its modulation There are numerous neural circuits that contain pacemaker neurons which provide a driving force for the network Such pacemaker neurons contain bursting mechanisms which endow them with the ability to fire independently of external stimulation Other neurons have bursting ability which may require a transient input to initiate their burst cycle Central pattern generators may be of either pacemaker type endogenous burster or requiring a transient input to initiate a burst conditional burster Neuronal bursting behav
19. View of Simulation File Graphic Output Data Output Report Help 600 VIKH lt m Start the simulation RUN Bata 200 4 20 0 40 0 J 60 0 80 0 J 00 0 001 0 002 0 003 0 004 0 005 0 006 0 007 0 008 0 009 0 01 time lt ivt SNNAP On Line Simulation CASNNAPISNNA PS itutorialitutorialExamples hhM odelihh smu The University of Texas Health Science Center at Houston Figure 3 5 Single action potential in response to transient stimulation Parameter as in Hodgkin and Huxley 1952 with V VuH 60 Cm 1uFicm2 gy 120mS cm gpg 36mS cm g 0 3mS cm2 Vya 55MV Vk Figure 3 5 shows an action potential which was initiated at time 0 0005 seconds 0 5ms by a current injection of 75uA cm for a duration of 0 1ms The action potential spike lasted for approximately 1 ms and at 0 01 seconds 10ms the membrane potential was approaching the stable resting potential of 60mV Printing Simulation Results To print the simulation results it is best to first change the color image to black on a white background Select the button on the left pane of the viewing window This button toggles the display between color and black white Now select the Print button and your normal print window should open 27 Tutorial Manual for Version 8 of SNNAP eee OOGO CA Once the image has been changed to black on white you can restore the black background by selecting the button Background A color cod
20. adjunct to their empirical studies As experimental data continue to amass it is increasingly clear that physiological and anatomical data alone are not enough to infer how neural circuits work Researchers are recognizing the need for a quantitative modeling approach to explore the functional consequences of neuronal and network features Computer simulations are an increasingly important tool for neuroscience research SNNAP Simulator for Neural Network and Action Potentials was designed as a tool for the rapid development and simulation of realistic models of single neurons and small neural networks With SNNAP all aspects of developing and running simulations are controlled through a user friendly graphical interface Thus no programming skills are necessary to develop and run simulations The electrical properties of individual neurons are described with either Hodgkin Huxley type voltage and time dependent ionic currents or integrate and fire models The connections among neurons can be made by either an electrical modulatory or chemical synapse The chemical synaptic connections are capable of expressing many forms of plasticity such as homo and heterosynaptic depression and facilitation SNNAP also includes descriptions of intracellular second messengers and ions which in turn can be linked to ionic conductances or mechanisms regulating synaptic transmission Thus you can use SNNAP to simulate the modulation of cellular and synaptic
21. choice within braces or brackets The following vocabulary is used in this manual to explain how to navigate the menus of SNNAP Right click click the right mouse button Left click click the left mouse button Click click either the right or left mouse button Menu gt SubMenuo gt gt SubMenun gt Iltem menu item identified by the path to access it SNNAPhome is the location where you have installed the SNNAP software All locations presented in the manual start from SNNAPhome Tutorial Manual for Version 8 of SNNAP OVERVIEW OF THE CAPABILITIES OF SNNAP A brief list of some of the capabilities of SNNAP version 8 is provided below v SNNAP can simulate networks of up to 10000 neurons with electrical chemical and modulatory synaptic connections v SNNAP can simulation networks containing both Hodgkin Huxley type neurons and Integrate and Fire type cells Moreover the synaptic contacts among integrate and fire cells can incorporate learning rules that modify the synaptic weights The User is provided with a selection of several non associative and associative learning rules y SNNAP can simulate the flow of current in multi compartment models of neurons which in turn can be incorporated into neural networks v SNNAP can simulate intracellular pools of ions and or second messengers that can modulate neuronal processes such as membrane conductances and transmitter release Moreover the descripti
22. model in the appropriate working folder and always be able to copy the initial model if needed Or use the final model to further investigate aspects of SNNAP A Linux folder is provided with initial and final folders for hhNet work The network model uses subfolders which require different pathnames under Windows and Linux Tutorial Manual for Version 8 of SNNAP HITEN TTT TS SST NN TTL TT EE 20 Tutorial Manual for Version 8 of SNNAP CHAPTER 3 THE HODGKIN HUXLEY NEURON MODEL The Hodgkin and Huxley model was first published in 1952 but still remained the modeling formalism of choice by most neuroscientists Using the voltage clamp technique that was developed in that day Hodgkin and Huxley estimated activation and inactivation functions for the sodium and potassium currents This allowed them to devise a mathematical model which described an action potential that resembled their experimentally recorded action potentials from the squid giant axon We will use the Hodgkin and Huxley model to learn how to run a SNNAP simulation and learn how to alter simulation and model parameters HODGKIN HUXLEY MODEL The space clamped version of the Hodgkin Huxley model consists of four ordinary differential equations The model describes the change of membrane potential V with respect to time Three equations describe the activation m and n and inactivation h variables of the sodium and potassium currents The total membrane
23. raie ant 24 Printing Simulation Results agere 27 Changing Simulation PALA LIS sa iscsi ashes caslsnsceccen usc sccctncaces na cdeabeacselensetaescetqtyicetned cmedusnaaseniencoennticoss 28 Adding urene ee 28 Changing Model Pr 24 30 Changing Model Conductance Parameter ccccscesscsesssestsscssessenenesssesesneensenesessnenensneneesenecaenees 31 Changing Simulation Duration EE ERE EEE 34 Changing Qutpu t DES 444244 35 Chapter 4 Bursting Neurons and Central Pattern Generators ueervvvvevvvvvrvsvrverrrrrrererererererrrrrsnsenennn 38 Introduction to Bursting Neurons sansene 39 The Morris Lecar Spiking Neuron Model scsccscscssscsssscesecssensesnseansecseecsenteeesntesssseneneeniesneenteeeans 39 Simulation of the Morris Lecar MOdel ccccccccscscsssssesseeecescecseseseseseseacacssscssseceeesesescenacansnesesesanesees 40 Description of the Morris Lecar Model as nenene 41 Adding OE GB cacti a een cases oeestseg nec tecevaceic gona rE Raes E r Sisa EESTE TEER Es raS E TETIERE ELETE Eent 42 lOni 0 EN EE TT ea ere 46 Seting lon Eee 52 Changing UU SNC enrica RE EE 57 Adding New Output Variable uagesre esne Gees 58 ME NN 59 Displaying GT EEE 62 2 Tutorial Manual for Version 8 of SNNAP Central Pattern Generator MOdulation scsssssssesssssssesecececesesesesrsesescscacecseeeecseseeeeeeseseeeseseseseeseneeees 65 Chapter 5 Modeling Small Neuronal Networks ccccccscssssssssssssssessscesesscese
24. shown in Figure 5 2 The colors of the neurons correspond to the color of the traces in Fig 5 1 The synaptic connections are marked by the brown lines with triangles at the ends showing the direction of the connection Therefore we see that Neuron 2 is stimulated by Neuron 1 which in turn is stimulated by Neuron 3 and Neuron 3 receives stimulation from Neuron 2 SYNAPTIC CONNECTIONS The three neuron network exhibits oscillatory behavior due to the synaptic connection between the neurons When a neuron fires an action potential it causes a synaptic current which stimulates the connected neuron The synaptic current is modeled by an alpha function multiplied by a maximal conductance and a driving force as follows LDV 5 1 70 Tutorial Manual for Version 8 of SNNAP OOS CA Eon t t exp t u 5 2 where is the time that has passed since the trigger of the synaptic current uis the time to peak is the synaptic reversal potential and the maximal synaptic conductance is g In order to display the synaptic current and alpha function lets add a second graph to the output display Click the button Edit OutScreen in the main SNNAP window to edit the output display Click File gt Open and select the file hhNetwork ous Following the instructions in section Changing Output Screen to add a new channel with time parameters min max from 0 to 0 02 with 5 tick marks gain equal 1 and color set to
25. 34 8 s2 34 8 Figure 4 18 To change the maximal time constant the A file and tA file are edited Set the t a parameter value to 0 008695 in the dialog box and click ok The parameter will be updated as shown in Fig 4 18 Click Ok to close the A tA window and click Yes in the Save Modified File window Click Ok to close the A file editor window The input current parameter must be changed in the treatment file In the main SNNAP window click on the Edit Treatment button Follow the procedure in section Adding Current Injection see also Fig 3 6 and change the injection current to the value 2 25 Now we can run our model and see the result In the main SNNAP window chick on button Run Simulation to open the simulation window If the window is already open click in the window to make it active Click on File gt Load Simulation locate and select the simulation file SNNAPhome SNNAP8 tutorial tutorialExampeles busterCPG ml smu and click Open Click the button Start in the main SNNAP window The simulation display should resemble that in Fig 4 19 56 em dl Tutorial Manual for Version 8 of SNNAP Fo SNNAP On Line View of Simulation File Graphic Output Data Output Report Help 300 _VOML J lt i Start the simulation 50 0 gt gt 7 gt 0 0 0 2 06 0 8 10 is times vr SNNAP On Line Simulation CASNNAPISNNAP38 tutorialitutorialExamples bursterCPGiml smu F
26. SNNAP SIMULATOR FOR NEURAL NETWORKS AND ACTION POTENTIALS Tutorial January 2003 The University of Texas Houston Medical School Center for Computational Biomedicine Department of Neurobiology and Anatomy Houston TX 77030 http snnap uth tmc edu 1993 2003 The University of Texas Health Science Center at Houston Tutorial Manual for Version 8 of SNNAP Table of Contents Chapter 1 Introduction 0 arsen 5 Introduction to Tutorial EE OE AEENEE AENA EE EEEE AEAEE Ennen EE rE n 6 EN EN 6 Typographic Conventions Lanes deneeenuenGbhendebnabsev 7 Overview of the Capabilities of SNAP sasunaaereasesekmaneeGkdkenuasjonsakntuen 8 Some Example Simulations to Illustrate the Capabilities Of SNNAP cceseseseeseesesescscsestsesteteseseeeteees 9 SNNAP Parameters NS 13 Chapter 2 Getting Sta MOU EEE REE ER 14 Instructions for Installing SNNAP ae ciciscca ce cccac sed tcetaceentea as ches tactcatsrtadelacoeshduanteactvarathceteaepitaneialaranceeces 15 Structure of Tutorial Examples DirectOry cccccecscscssecssscececssesessesessesasssssseeeeeesseseseessaesisseseneseseeees 19 Chapter 3 The Hodgkin Huxley Neuron Model 2 s ccccccscsssesesessseseseseeseeeeeetseseesesestensenesassneeeeeteasseens 21 Hodgkin Huxley Model aa 21 Running the Hodgkin Huxley Model With SNNAP s sssssssssesssessressrssrrssresresrrssressresrennrsrrnnresnennrnnrrssresne 23 Launching MR 23 R nning SS 1016 110 EE Eein rina EE EEEn Eaa EEan
27. addition to the example simulations other supplemental information and software that is included with SNNAP including an electronic version i e pdf of the Users Manual numerous Excel spreadsheets illustrates of simulations and new program called CellMatrix CellMatrix is a data management tool for organizing empirical data that describe synaptic connections among identified neurons This tool can be useful in developing models of small neural networks that are based on a large body of published literature v SNNAP was implemented in Java and can run on virtually any computer and under most operating systems v SNNAP is freely available and can be downloaded via the internet The software example files and User s manual are available at http SNNAP uth tmc edu SOME EXAMPLE SIMULATIONS TO ILLUSTRATE THE CAPABILITIES OF SNNAP Detailed descriptions of the capabilities of SNNAP are provided in Chapter 3 and Appendix B which describe the equations incorporated into SNNAP and the example simulations that are distributed with SNNAP To briefly illustrate some capabilities and potential uses of SNNAP a few example simulations are presented below Figure 1 illustrates how SNNAP can be used to simulate the biophysical properties of neurons including relatively simple models of the action potential and neuronal excitability e g the Hodgkin Huxley model of the squid giant axon and more complex models of autonomous bursting intracellular se
28. ance y SNNAP can simulate asymmetrical electrical coupling between cells v SNNAP can simulate a number of experimental manipulations such as injecting current into neurons voltage clamping neurons and applying modulators to neurons In addition SNNAP can simulate noise within any conductance i e membrane synaptic or coupling conductances and SNNAP can simulate a novel procedure for clamping a state variable in which the magnitude of a specified membrane current s can be clamped to a specified value at any given time v SNNAP includes a Batch Mode of operation which allows the User to assign any series of values to any given parameter or combination of parameters The Batch Mode automatically reruns the simulation with each new value and displays prints and or saves the results v The on screen display can plot any combination of state variables in either the time domain and or as a phase plane i e one state variable vs another Moreover the results of a simulation can be printed Tutorial Manual for Version 8 of SNNAP stored as a postscript file or stored as data file The data file is in ASCII format and can be used by other software packages and or by the of f Line Viewer that is provided with SNNAP v SNNAP includes a large suite of example simulations that illustrate capabilities of SNNAP and that can be used as a tutorial for learning how to use SNNAP or as an aid for teaching neuroscience v In
29. ar Click on the second equation and it will appear in the Editor window as shown in Fig 4 13 SNNAP Editor for ion File xi C SNNAP SNNAPSttutorial formulaTemplateside Open Save Saveas Edit 18 ha fio Figure 4 13 Equation for intracellular calcium ion concentration 52 Tutorial Manual for Version 8 of SNNAP atta COo oot eoe There are two parameters that need to be set the parameter k1 to 5 0 and parameter k2 to 0 4 To set a parameter click on the parameter marked with a grey background to open a dialog box where you set the appropriate value Click the button Ok to close the dialog box When all three parameters have the desired values click the button Save As in order to save the file under the name m1Ca ion in the desired folder Remember that we declared that file name when updating the Edit Neuron window see Fig 4 8 In the Save dialog box change the folder to SNNAPhome SNNAP8 tutorial tutorialExamples burstercCPG and click the button Save Once saved close the Editor for ion File window by clicking the button ok or the upper right button x We will now turn to the second file that needs to be established the fBR file This file will contain the equation for the activation of the potassium conductance that is dependent on intracellular calcium concentration In the Formula Editor window click the button fBR to open the Editor of fBR File window Click the butt
30. car model a calcium dependent potassium current Such currents are known to play a role in bursting neurons providing a mechanism for ending the bursting period and influencing the duration of quiescence In the main SNNAP window click the button Edit Neuron This will cause SNNAP to display the Edit Neuron window Next click File gt Open to open the file chooser dialog box The file we need is m1 neu in the folder SNNAP8ltutorialltutorialExamples bursterCPG Click the file name to select and click the button Open Figure 4 2 shows the resulting display of the Edit Neuron window Next click the menu Edit gt Add Node gt V D Conductance to open the add Node dialog box 42 Tutorial Manual for Version 8 of SNNAP E SNNAP Edit Neuron la xi File Edit ColorSetting Print Help _CURRENT gt 110N Tdi Ca ION gt CURRENT Jonsen vg Pe ION gt TRI leak SM gt TRF ION gt SM Ye mer und SM gt SM Tees sar The Untversity of Texas Health Science Center at Houston Figure 4 2 Use the window Edit Neuron to incorporate new conductances In the Add Node dialog box you must provide the name of the conductance K Ca and the file name m KCa vdg which will contain the parameter values as shown in Fig 4 3 Click Ok to close the dialog box and update the model 4 Add Node x Conductance name K Ca File name mIKCa vdg Color name E blue X
31. ce parameter Change the value of 36 to 16 and click the button ok Click the button ok on the window Editor for vdg File and click Yes on the Saved Modified File question box that will appear Now you are ready to run the simulation with the new potassium conductance value Go to the window Simulation View click File gt Reload Simulation and then click the button Start 33 Tutorial Manual for Version 8 of SNNAP Eee Fa SNNAP On Line View of Simulation File Graphic Output Data Output Report Help 60 0 VIEH lt tm 40 0 80 0 0 0 0 001 0 002 0003 0004 0 005 0 006 0 007 0 008 0 009 0 01 time ivr HS NNAP On Line Simulation CASNNAPVSNNAP8MutorialltutorialExamplesthhModelihh smu Tue Dec 03 16 08 05 GMT 06 00 The University of Texas Health Science Center at Houston Figure 3 11 Oscillation with low maximal potassium conductance displaying a single action potential Figure 3 11 shows a single action potential We would like to extend the time duration of the simulation in order to observe several action potentials Lets see how to do that next Changing Simulation Duration It would be helpful to see the oscillatory behavior of the model for a longer period than 10ms To change the duration of the simulation there are two SNNAP windows that need to be altered In the main SNNAP window click the button Edit Simulation In the Edit Simulation window click menu
32. cond messengers and modulation e g the Butera et al model of the R15 neuron in Aplysia Tutorial Manual for Version 8 of SNNAP eee o SS COA A Simulation of Squid Giant Axon B Simulation of Cell R15 in Aplysia A1 Components of the Model B1 Components of the Model Extracetlular Extracellular Intracellular A2 Simulated Action Potential Pan I x NGT cAMP sje B2 Membrane Potential Jao mv 5 sec B3 Intracellular Concentration of Ca Sy A Fae Pe FX B4 Slow Inward Current lg Figure 1 1 Using SNNAP to model neurons with relatively simple properties e g the squid giant axon or to model neurons with more complex properties such as autonomous bursting second messengers and modulation e g R15 The equivalent circuit diagrams for both models are illustrated A1 and B1 In addition the intracellular regulatory pathways of the R15 model are illustrated B1 A Simulation of the Hodgkin and Huxley model of an action potential in the squid giant axon This model has only two voltage and time dependent conductances A2 The SNNAP simulation illustrates the Batch Mode of operation In the Batch Mode simulations are repeated automatically while systematically varying parameter values In this example the magnitude of the injected depolarizing current pulse arrows was increased with each simulation and the results of each simulation were superimposed The SNNAP input files used to ge
33. current is the sum of a capacity current and an ionic current In Gag dt The ionic current is the sum of three individual components sodium potassium and leak currents LE Lyle FL The sodium current equation is the product of a maximal conductance g activation variable m inactivation variable h and a driving force V V Iya Enam hV Vya 21 Tutorial Manual for Version 8 of SNNAP pO eT The potassium current equation is the product of a maximal conductance g activation variable n and a driving force V V Ik Exn V Vi The leak current equation is the product of a maximal conductance g and a driving force V V I g V V The activation variables m and n and inactivation variable h which vary from 0 to 1 follow a standard form with forward rate functions and backward rate functions 2 and a temperature dependent factor dm gla MA m 8 Bm gla 0A h AB VA gla MA n A Mn The forward and backward rate functions dependent on voltage where estimated by Hodgkin and Huxley based on their experimental results a V 235 Y Bees 1 B V 4e 018 m a V 0 07e0 1 B V 23070 Tutorial Manual for Version 8 of SNNAP 0 01 50 V a V Sor B V 0 125e 00 80 The forward and backward rate equations have been converted from the original HH version to agree with present physiological practice where depolarization of
34. d by the SNNAP simulator Parameter Unit Time Seconds Conductance microSiemens uS Current microAmps uA Capacitance microFarads uF Cell diameter microMeter uM Membrane Potential milliVolts mV Tutorial Manual for Version 8 of SNNAP CHAPTER 2 GETTING STARTED In order to run the SNNAP application you must have a Java virtual machine installed on your computer The SNNAP software is provided as a Java jar file You can therefore place it in any folder or directory you wish and easily launch the application The SNNAP software provides you with a tutorial that covers several computational neuroscience examples The tutorial manual and examples are provided in the folder tutorial This chapter describes how to install SNNAP as well as a Java environment if you do not already have one The directory structure for the tutorial examples are also described This chapter consists of the following sections e Instructions for Installing SNNAP describes how to install the software on machines running Windows MacOS or Linux e Tutorial Examples Directory Structure presents the example files of the tutorial and their structure Tutorial Manual for Version 8 of SNNAP INSTRUCTIONS FOR INSTALLING SNNAP Hardware and Software Requirements About 25 megabytes of hard disk space are needed to accommodate SNNAP and its associated files These additional files include items such as this manual and exte
35. d two outward potassium currents As a first step lets return the calcium concentration plot to the graph with the membrane voltage and use the second graph for the ionic currents Next we need to declare three new output variable in SNNAP for the three currents 62 Tutorial Manual for Version 8 of SNNAP Open the Output Screen window and click Edit gt Add gt Add Variable to open the Add One Variable dialog box Set the parameters as shown in Fig 4 25 for the three currents CT x channel number 2 channel number 2 channel number 2 first variable tvd Ca ML lt fvt x first variable Wd K ML lt ivr lt first variable IVDXREG K Ca ML Jsfur Y second variable NULL bud second variable NULL ba second variable NULL x number of ticks 10 number of ticks 10 number of ticks 10 minimum 18 1780657 50 minimum 0 0976201 0 minimum 0 0000 1 maximum 0 7667411 0 maximum 15 8914464 50 maximum 0 789245 2 color name gold color name E green p color name E red Coy cance ER mes EN caveat Figure 4 25 To declare three new output variables three active currents Repeat this step three times for variables vd Ca ML Ivd K ML and IVDxREG K Ca ML The final Output Screen should look like Fig 4 26 Click File gt Save to save the changes in SNNAP 63 Tutorial Man
36. dependent potassium current uses intracellular calcium concentration for activation The last equation is needed for such a conductance Figure 4 5 shows the equation with the required parameter values SNNAP Editor for vdg File x C SNNAP SNNAPSttutorial formulaTemplateside Open Save Bagel Edit i 75 g R Lug Gog V E TI R Figure 4 5 Equation for calcium dependent potassium conductance in vdg editor There are two parameters which must be edited the maximal conductance value g and the reversal potential E Parameters which have grey backgrounds may be edited To edit the value of such a parameter point the mouse over the grey area and click This will cause a dialog box to appear similar to Fig 3 10 permitting you to edit the value After entering the desired value in the dialog box click the button ok to close the box The value you have entered will now appear in the vdg window Once you are done editing the two parameters click the button Save As in the vdg editor to open the file chooser dialog box see Fig 4 6 We do not want to save the equation file in the folder formulaTemplates but rather in the appropriate folder so that SNNAP can locate the file when loading the simulation The file name m1KCa vdg was declared in the window Edit Neuron see Fig 4 2 and should be provided The 45 Tutorial Manual for Version 8 of SNNAP rie pga path for
37. e membrane potential of a neuron passes OmV it triggers the synaptic potential and the alpha function lower blue trace is calculated with time t increasing from 0 the moment of trigger Note The threshold for triggering the alpha function is a SNNAP parameter which is set in the neuron file neu edited by clicking the button Edit Neuron in the main SNNAP window The synaptic current in our case is an excitatory current which produces an inward current that depolarizes the neuron The current having a reversal potential of OmV has an outward component when the neuron membrane potential is above OmV Similarly the two other neurons have synaptic connections as show in Figure 5 2 and follow the same time course as displayed in Fig 5 5 but with a delay corresponding to the time of activation 73 Tutorial Manual for Version 8 of SNNAP II 7 230 spe The synaptic current is represented by two equations in SNNAP corresponding to equations 5 1 and 5 2 SNNAP Editor for cs File xj CASNNAP SNNAPS tutorialitutorialExamples hhN Open Save saveas Edit Gs 6 5 0 Les Gs V E 0 7 0 0 JAt Synapses hhSyn JAt R filename Browse Ok Figure 5 6 Equation for chemical synaptic current The synaptic equation is represented by a cs file To edit this file click the Edit Formula button on the main SNNAP window and then click the button cs in
38. ectory contains simulations similar to those illustrated in this figure Im INa IK 0 5 mA cm2 2ms Vo 10 mV Vh 60 mV Figure 1 4 SNNAP can simulate many common experimental manipulation such as voltage clamping In this example the Hodgkin Huxley model of the squid giant axon as held at 60 mV and stepped to 10 mV The total membrane current black trace sodium current red trace and potassium current blue trace that were elicited during the step are illustrated The files necessary to run a simulation such as this are in the Examples HH_type_neuron Biophysics_01 subdirectory Tutorial Manual for Version 8 of SNNAP As illustrated in Figure 1 4 SNNAP also allows the User to simulate many common experimental manipulations such as injecting current into neurons voltage clamping and applying modulatory transmitters SNNAP also offers several rather unique manipulations including clamping a given membrane current to a specified value injecting complex signals into neurons e g Sine waves ramps exponential waveforms and introducing noise into membrane and or synaptic conductances The User sets the magnitude and frequency of the stochastic fluctuations and can specific whether there is a single source of noise i e all fluctuations occur in unison or there are multiple and independent sources of noise SNNAP PARAMETERS AND UNITS The following table presents the parameters and units use
39. ed window will open and you may select any background color you want the original was black Note For the rest of this tutorial we will use a white background for the simulation window CHANGING SIMULATION PARAMETERS So far we saw how to launch and view a simulation We will now learn how to change simulation parameters First we will add an additional current injection to cause a second action potential during the simulation Then we will edit model parameters the maximal potassium conductance to create a neuron model that exhibits oscillatory behavior with no external stimulation Adding Current Injection Lets change parameters and rerun the simulation The first parameter change will be in the treatment file which contains the various modes of applying stimulation to the model We will add a second stimulating current at time 7 5 ms In the main SNNAP window click the button Edit Treatment In the Treatment window click the menu item File gt Open In the file chooser window double click the file hh trt or click once and click the Open button The open treatment window should look as follows 28 Tutorial Manual for Version 8 of SNNAP HH 0 0005 0 00061 75 00 Add MINJ Add VCLMP Add ICLMP minj start t end t mgn vclmp start t end tmgn The University of Texas Health Science Center at Houston Figure 3 6 Treatment window with single current in
40. eine induced oscillations of the membrane potential in Ap ysia neurons Neurophysiology 32 77 84 86 Tutorial Manual for Version 8 of SNNAP 10 Pelz C Jander J Rosenboom H Hammer M and Menzel R 1999 I A in Kenyon cells of the mushroom body of honeybees resembles shaker currents Kinetics modulation by K and simulation J Neurophysiol 81 1749 1759 Steffen M Amini B Feigenspan A Seay C Feigenspan A Cai Y Baxter D A and Marshak D 2002 Spontaneous activity of dopaminergic neurons in the mouse retina a computer model Biophys J under review Susswein A J Hurwitz l Thorne R Byrne J H and Baxter D A 2002 Mechanisms of pattern generation underlying fictive feeding in Aplysia The initiation and maintenance of protraction via coupling between a large neuron with only plateau like activity and a small conventional neuron J Neurophysiol in press White J A Ziv l Cleary L J Baxter D A and Byrne J H 1993 The role of interneurons in controlling the tail withdrawal reflex in Aplysia a network model J Neurophysiol 70 1777 1786 White J A Baxter D A and Byrne J H 1994 Analysis of the modulation by serotonin of the voltage dependent potassium current in sensory neurons of Aplysia Biophys J 66 710 718 Ziv Baxter D A and Byrne J H 1994 Simulator for neural networks and action potentials description and app
41. ey Neuron Model presents the classic neuronal model of Hodgkin and Huxley and shows how to run a simulation using SNNAP This chapter also describes how to change basic simulation parameters and model parameters e Chapter 4 Bursting Neurons and Central Pattern Generators describes a more complex model than the Hodgkin Huxley model while introducing the concept of an ion pool and second messenger and how such elements can be used to modulate neuronal behavior with SNNAP e Chapter 5 Small Networks presents a three neuron network of Hodgkin Huxley models and shows how to build a network with synaptic connections using SNNAP The network model can display oscillatory behavior because of the network architecture and synaptic connectivity Tutorial Manual for Version 8 of SNNAP Typographic Conventions The following typographic conventions apply throughout this manual Code extracts and file names are written inthis typeface Italic type is used to indicate user specific information Also important ideas are emphasized like this Values that you must fill in for example a file name or a path name also appears in the same typeface as code extracts but slanted to indicate you must supply an appropriate value for example SNNAPhome indicates that you must fill in a value for SNNAP home Square brackets indicate optional items Ellipsis indicate that you can repeat the information A vertical bar indicates a
42. for Version 8 of SNNAP Windows MacOS and Linux UNIX These instruction can also be found on the SNNAP web site i e http snnap uth tmc edu 1 Downloading and installing Java and SNNAP on computers with the Microsoft operating system i Download and install latest version of Java Java is necessary to run SNNAP a Go to the website http java sun com b Select Standard Edition J2SE c Select J2SE Downloads e Select J2SE 1 4 1 or latest version f Select from table Windows SDK Downloads g Save Java 2 on the directory of your choice for example C program files JDK2 h Open the directory with the Java download find and click the Java JDK2 icon to install Java ii Download WinZip A zip program is necessary to uncompress SNNAP You may already have a zip program and can skip this step a Go to the website htto Avww winzip com b Download and install WinZip iii Download SNNAP a Open the website http snnap uth tmc edu b Click download and click the version you desire c Save SNNAP in a folder of your choice but the directory path may NOT contain a blank character e g Program Files is NOT a valid folder d Find the downloaded SNNAP file in the directory e Click the SNNAP icon f Save the unzipped SNNAP files in a directory e g C iv Run SNNAP on Windows 95 98 NT 2000 XP There are two methods for initiating a SNNAP simulation session The first is the simplest Usi
43. h1 Neur 40 00 40 00 40 00 Mar3 lt Ch1 V Neur Var4 lt Ch2 fAt Neu 20 00 2000 2000 ar5 lt Ch2 Ics Neu 000 000 0 00 20 00 _ 20 00 j 20 00 40 00 4000 40 00 60 00 6000 60 00 80 00 J 3000 J 30 00 0 80 T od T oo 002 T 006 ofa time vr Variable to File 36 0 50 Ics Neuron 2 gt Neuron 1 ex64tHNenron 2 gt Neuron 1 exc 200 J 0 40 1 00 _ 0 30 400 0 20 700 J 0 10 10 00 J 0 00 odo Toa om TT of opie ona time vr M M The University of Texas Health Science Center at Houston Figure 5 8 Output screen window with selected variable Now rerun the simulation by clicking File gt Reload Simulation in the Simulation window Click the Start button in the simulation window Figure 5 9 shows the display you should see on your screen 76 Tutorial Manual for Version 8 of SNNAP EE ee RT SY Fa SNNAP On Line View of Simulation lel xj Fie Graphic Output Data Output Report Help 600 60 0 _YNewon_2 lt ivr V Newon_3 J lt ivr VNeuron 1 lt svr YX 4 A 400 1 40 0 Start the simulation J Run Bate 20 200 oo oo 4 200 J 20 0 4 400 400 60 0 60 0 800 80 0 0 0 0 004 s 0 008 0 012 0 016 002 times ivr 150 _ 0 5 Ics Neuron 2 gt Neuron 1 exffst Retron 2 gt Neuron lexc e n J 30 4 0 4 50 1 03 150 J 0 2 250 1 0 1 4 350 J 0 0 0 0 0008 2 time ivr SNNAP On Line Simulation CASNNAPWSNN APS
44. hich has a grey background In the parameter window which opens change the value of g to 2 MS cm2 Click Ok in the parameter window and in the cs editor window you will be asked to confirm the changes made click Yes Now rerun the simulation by clicking File gt Reload Simulation in the Simulation window Click the Start button You will see that the synaptic current has a range larger than the range we provided for in the output screen Lets increase that range to be min max 35 15 15 Tutorial Manual for Version 8 of SNNAP OOO GOS CA In the main SNNAP window click the Edit OutScreen button to open the Edit Output File window In the output window Click File gt Open and select the file hhNetwork ous in the folder SNNAP8 tutorialtutorialExampes hhNetwork The output window should look like Fig 5 8 Click in the Variables pane the entry Var5 lt Ch2 Ics to select the synaptic output variable Click Edit gt Modify to open the Modify Variable Properties dialog box Set the minimum and maximum values to 35 and 15 respectively Click Ok to close the dialog box Click File gt Save in the output window smar tait output setup rne Te File Edit Help Channels CASNNAPISNNAPB1tutorialtutorialExamplesihhNetworkihhNetwork ous Variables Ch1 time lt fvr 6000 6000 6000 V Newron 2 ivr V Neuron 3 lt ivr V Neuron 1 lt svr Var 1 lt Ch1 VINeurd Ch2 time lt ivr I Var2 lt C
45. individual compartments of a multi compartment neuron To develop a multi compartment model the properties of the neu files are adjusted to match the morphological features of the neuronal compartments These neu files are then linked together so as to reflect the branching structure of a given neuron SNNAP incorporates several tools that help the User develop multi compartmental models For example the User can enter geometric parameters e g the diameter and length of 11 van mr 01230200 Tutorial Manual for Version 8 of SNNAP lt a a compartment and SNNAP calculates the membrane area and appropriately scales the ionic conductances for each compartment 25 mV 250 ms Figure 1 3 Simulating a neuron with multiple compartments SNNAP can be used to model the spread of current in a multi compartment model of a neuron In this example the postsynaptic neuron neuron A was modeled as a branching structure with 11 compartments of progressively decreasing size i e diameter and length The properties of the two presynaptic inputs i e neurons B and C were identical One input however was further from the soma than the other The voltage of the postsynaptic neuron was monitored in the soma compartment Vm and the two presynaptic neurons were stimulated The EPSPs for the more distal synaptic input i e from neuron C were attenuated and their kinetics appeared slower The Examples Compartmental_models subdir
46. ior may result from various mechanisms One ubiquitous mechanism leading to bursting is the oscillation of a slow calcium wave that depolarized the membrane and causes a series of action potentials on top of the calcium wave When the slow wave ends the quiescent period begins Such calcium waves have also been implicated in the rhythmic behavior of cardiac muscles In this chapter we will develop a bursting neuron model Starting with a neuron model that exhibits single action potentials we will convert this model to a neuron model that exhibits bursting behavior by incorporating calcium dependent processes which will allow for the formation of a slow calcium wave THE Morris LECAR SPIKING NEURON MODEL The Morris Lecar model was developed to describe the behavior of barnacle muscle cells The model exhibits single spiking behavior similar to the Hodgkin Huxley model The Morris Lecar ML model though is simpler than the Hodgkin Huxley HH model in that it consists of only two variables the membrane potential V and potassium 39 Tutorial Manual for Version 8 of SNNAP activation w rather than four variables The two ordinary differential equations are described as follows dV F OG 1 am VW Va EWV Vi EV V dv w V w dt t V where the three ionic currents calcium Ca potassium K and leak L are products of a maximal conductance g activation component function m variable w or constant
47. ive the synthesis of second messengers in the postsynaptic neuron not shown and synaptic connections that are both voltage and time dependent not shown For example SNNAP can simulate NMDA type synaptic responses A Homosynaptic Plasticity B Heterosynaptic Facilitation Facilitation of an EPSP a NK ehh te Depression of an IPSP 100 msa C Synaptic Connection with Multiple Components D Decreased Conductance Synaptic Connection Fast Ci Component 1nA 1 nA li Figure 1 2 Simulating synaptic connections and synaptic plasticity with SNNAP Many different types of synapses and plasticity can be modeled For example homosynaptic facilitation or depression A can be simulated By including a second messenger system that modulates transmitter release heterosynaptic plasticity B can be simulated SNNAP can simulate synaptic responses that have multiple components C such as fast and slow potentials and synaptic response that induce conductance decreases D The Examples Synaptic connections subdirectory contains simulations similar to those illustrated in this figure Although SNNAP was designed to simulate neurons as a single isopotential compartment it can also simulate neurons as multi compartmental structures Figure 1 3 The fundamental computational unit of a SNNAP simulation is the neu file i e neuron file The neu files can be used to represent a single neuron or to represent
48. jection The window shows the time axis and the various types of stimulation that may be applied to the model We will add a second current injection Click Edit gt Add Treatment gt Add CINJ as seen in Figure 3 6 The Add CINJ dialogue box will open Enter the values as shown in the Figure 3 7 gt xd E Add CIN x Neuron name HH Start time 0 0075 Stop time 0 0076 Magnitude 75 0 eg a Figure 3 7 The Add Current Injection parameter window And click the button ok to close the window 29 Tutorial Manual for Version 8 of SNNAP The current injection stimulation we have entered is 75uA cm for a duration of 0 1ms starting at 7 5ms 0 0075 sec To rerun the simulation click the menu File gt Reload Simulation and then click the button Start If you have previously closed the simulation window you must open it and load the hh smu file as was described in Running a Simulation Fa SNNAP On Line View of Simulation File Graphic Output Data Output Report Help 60 0 _ VHH J lt ivt 40 0 20 0 0 0 00 0 001 0 002 0 003 0 004 0 005 0 006 0 007 0 008 0 009 0 01 time ivr CASNNAPVSNNAPS tutorialitutorialExamples hhM odel hh smu Mon Dec 02 11 22 28 GMT 06 00 The University of Texas Health Science Center at Houston Figure 3 8 The simulation window displays two action potentials initiated by current injection at 0 5 and 7 5 MS Exercise Cha
49. lication J Neurophysiol 71 294 308 87 Tutorial Manual for Version 8 of SNNAP ENE 2 20000E LITERATURE CITED IN MANUAL Hayes RD Byrne JH Baxter DA 2003 Neurosimulation Tools and Resources in The Handbook of Brain Theory and Neural Networks 2rd ed Arbib MA MIT Press Cambridge Massachusetts Hodgkin AL Huxley AF 1952 A quantitative description of membrane current and its application to conduction and excitation in nerve J Physiol 117 500 544 Morris C Lecar H 1981 Voltage oscillations in the barnacle giant muscle fiber Biophys J 35 193 213 Rinzel J Ermentrout B 1998 Analysis of neural excitability and oscillations in Methods in Neuronal Modeling From lons to Networks Koch C Segev Eds 279 edition Cambridge The MIT Press 88
50. lue background Click menu Edit gt Modify to open the Modify Channel Property window In the Modify Channel Property window change maximum value to 0 1 and number of ticks to 10 Click the Ok button Select menu File gt Save to save the parameter changes you have made JSNNAP Edit Output Setup File ET ol x File Edit Help Channels C SNNAPISNNAPB tutorialitutorialExamplesihhModelihh ous Variables Ch1 time lt fnr OK Var1 lt Ch1 VIHH lt channel variable times da channel gain 1 number of ticks 10 minimum 0 000000 0 00 Variable to File maximum 0 010010 0 1 color name H blue 0 002 0 0 005 0 006 0 007 J O1 003 0 004 time lt ivr The University of Texas Health Science Center at Houston Figure 3 13 Window to edit output screen parameters with time axis modification dialog window overlay 36 Tutorial Manual for Version 8 of SNNAP Now you can rerun the simulation and see the oscillator behavior of the Hodgkin Huxley model with reduced potassium conductance Click File gt Reload Simulation in the window On Line View of Simulation and then click the button Start Fa SNNAP On Line View of Simulation ioj x File Graphic Output Data Output Report Help ey 40 0 VHH lt iv 20 0 40 0 1 600 J Vi 00 odi 082 om oda 085 0 06 007 ode 0 09 oi time ivt
51. ndow the pane Current gt calcium current is the source of Ca ions while the pane ion Ion indicated that the gt current indicates that the Ca ions have an affect on the calcium dependent potassium current Click File gt Save in the Edit Neuron window to save the changes to the neuron model in SNNAP We have now programmed SNNAP to describe an ion process where calcium ions accumulate due to the Ca current and play a role in the mechanism of the calcium dependent potassium conductance What remains now is to establish the exact equations for these two processes 51 Tutorial Manual for Version 8 of SNNAP ee oes Setting lon Equation SNNAP uses numerous equations when running a simulation The Formula Editor window allows you to access all the equations used in a model In the main SNNAP window click the Edit Formula button to open the Formula Editor window see Fig 3 8 In the previous section we named two files mlCa ion and mlCa fBR which we now will establish Using the template library we will choose the equation for each process Click the button ion in the Formula Editor window In the file chooser window go to the template libraries in SNNAPhome SNNAP8 tutorial formulaTemplates The file default ion will appear Click the file name and click Open The window Editor for ion File will appear with an empty window Click the button Edit and the list of possible ion equations will appe
52. nerate this simulation are included in the Examples H_H type neurons Biophysics 01 subdirectory B Simulation of the Butera et al model of the bursting neuron R15 in Aplysia This model incorporates six voltage and time dependent conductances In addition it incorporates two intracellular pools i e an ion pool of calcium and a pool of the second message cAMP These pools in turn modulate several membrane conductances B2 SNNAP allows the User to plot any variable in a model such as the membrane voltage intracellular concentration of calcium and specific membrane currents The SNNAP input files used to generate this simulation are included in the Examples H H type neurons R15 subdirectory 400 nM 1nA In addition to simulating the complex biophysical properties of neurons Figure 1 SNNAP can simulate the complexities of synaptic connections and synaptic plasticity both homo and heterosynaptic plasticity Several examples are illustrated in Figure 1 2 SNNAP can simulate synaptic connections with homosynaptic facilitation and or depression synaptic connections with multiple components e g fast and slow PSPs 10 Tutorial Manual for Version 8 of SNNAP synaptic connections that produce decreased postsynaptic conductances as well as synaptic connections that are modulated via heterosynaptic connections In addition SNNAP can simulate modulatory synaptic connections i e synaptic connections that dr
53. ng your file explorer go to the folder where SNNAP is located double click on the SNNAP 8 jar file and the main control panel for SNNAP will appear Alternatively open a command line window see Fig 2 1 change directory to where SNNAP is located and type C SNNAP8 gt Jjava jar snnap8 jar and the main control panel for SNNAP will appear By using this second method of running SNNAP if there is a problem with a simulation error messages will be displayed in the command line window v Make a shortcut for SNNAP a Go to SNNAP folder b Highlight the SNNAP8 Jar file icon c While pressing the right mouse button drag the SNNAP 8 jar file to the desktop screen and drop it Select the Create Shortcut Here option and the shortcut for SNNAP is now created d You may wish to alter the appearance of the shortcut on the desktop To change the icon associated with SNNAP highlight the SNNAP desktop icon and press the right mouse button 17 Tutorial Manual for Version 8 of SNNAP Select the Properties option which will invoke a pop up window Select Change Icon and select the Browse option This will allow you to search for and select a new image for the SNNAP desktop icon An ico file is provide with SNNAP and you may use this icon 2 Downloading and installing Java and SNNAP on computers with the MacOS X operating system i Download SNNAP a Click Version 8 or 7 from URL http snnap uth tmc edu
54. nging the time of the second current injection what is the shortest delay between two action potentials you can find CHANGING MODEL PARAMETERS Up till now we have seen how to add current injection to the model Now we will look at how to change model parameters which alter the behavior of the model The standard Hodgkin Huxley model has a resting potential of approximately 60mV This is due to the dynamics between the two major ionic currents sodium and potassium If we 30 Tutorial Manual for Version 8 of SNNAP reduce the maximal ionic conductance of the potassium current g the model will show a higher resting potential For some value of g the model will begin to exhibit oscillatory behavior This value turns out to be 16mS cn How do we do this First lets turn off the current injection by setting the magnitude to zero and see that the HH model has a stable resting potential Open the Treatment window as described in Section Add Current Injection click the first injection data on the right and click the menu Edit gt Modify The Modify Connection dialog box will open Set the current magnitude to zero Repeat this for the second injection current as well Then click the menu File gt Save to save the changes you have made Go to the Simulation View window and click File gt Reload Simulation And then click the button Start The display should be a stable membrane potential at approximately 60mV Changing
55. ns communicate across synapses which may be either excitatory inhibitory or electric SNNAP offers you several ways to connect neurons in a network We will incorporate both excitatory and inhibitory synapses into our network This chapter is composed of the following sections e Introduction to Small Neuronal Networks A small neural network composed of three Hodgkin Huxley neuron models with excitatory connections is organized in a ring architecture The network may exhibits oscillatory behavior e Synaptic Connections Synaptic release is described by an alpha function By varying the synaptic strength the network exhibits oscillations of various frequencies By changing the synaptic connection to be inhibitory the network can still exhibit oscillatory behavior using a mechanism called post inhibitory rebound for action potential generation e Building Networks The SNNAP graphical user interface may be used to incorporate neurons and synaptic models in order to construct and enlarge a neural network We will incorporate an additional Hodgkin Huxley neuron model to our previous network model and exhibit oscillatory behavior with this four cell network 67 Tutorial Manual for Version 8 of SNNAP PS TN CO INTRODUCTION TO SMALL NEURONAL NETWORKS In order to understand the basic principles of how neural systems function we must investigate how neurons interact in a network In this chapter we will examine a simple ne
56. nsive set of Example simulations which can be used as an informal tutorial on how to use SNNAP and as a starting point for developing new simulations an additional program termed CellMatrix which can be used to manage empirical data and several Excel spread sheets README files and images of simulations In addition SNNAP requires a functioning version of Java on the user s computer This may be either the Java Runtime Environment JRE or the Java Development Kit JDK It is recommended that the user install the most current version of Java and that it be installed such that Java programs can be run from any directory Structure of SNNAP The snnaps directory contains two Java jar files SNNAP8 jar and CellMatrix jar a icon file SNNAP8 ico which can be used as a desktop icon and two subdirectories examples and tutorial which both contain many additional subdirectories The SNNAP8 jar contains all files necessary to run SNNAP Installing SNNAP SNNAP is distributed as a jar file The SNNAP8 jar file contains all of the class files necessary to run SNNAP The class files contain the byte code used by the JRE on any given computer Briefly Java applications are created and run by first writing source code just as in any programming language Second the JDK Java Development Kit is used to compile this source code into byte code Byte code is not a machine specific binary file however as with standard compiler
57. on Edit to open the fBR Equation window Click the first equation as seen in Fig 4 14 fBR Equations x JBR BR N 12w 10 BR 1 0 10 a BR JBR Figure 4 14 fBR equation window Clicking the first equation will place the equation in the Editor window see left side of Fig 4 17 Before continuing it would be advantageous to save the file in the appropriate folder Click the button Save As which will open a file chooser dialog box see Fig 4 15 Traverse your folder structure to the folder bursterCcPG the complete path is SNNAPhome SNNAP8 tutorial tutorialExamples bursterCPG 53 Tutorial Manual for Version 8 of SNNAP Save the file in the correct directory under the name m1Ca fBR which was provided to SNNAP see Fig 4 11 Click the button Save to save the file and close the dialog box Save in burstercPG v FEN k 3 Local Disk C a CI SNNAP C SNNAP8 C tutorial C tutorialExamples CD RW Drive D C My Network Places FileName miCaBR Files of Type File type fBR v Figure 4 15 File chooser dialog box In the editor window click the parameter BR with the grey background to open the BR Equation window Click the button Edit to display the possible equations shown in Fig 4 16 BR Equations dBR amp reg BR dt u _ _ reg Se reg N reg a reg BR 1 0 1 0 Za 1 0 a reg Figure 4 16 BR e
58. ons of the ion pools and second messenger pools can include serial interactions as well as converging and diverging interactions For example the synthesis of a second messenger e g cAMP can be regulated by both a modulatory transmitter and the levels of intracellular Ca v The number of ionic conductances as well as the number of intracellular pools of ions and second messengers that can be incorporated into a neuron is dynamically allocated The User can add as many elements to a model as may be necessary to describe a given neuron Thus models of neurons can achieve a high level of sophistication v Chemical synaptic connections have User defined kinetics i e fast or slow can produce either increases or decreases in postsynaptic conductance can be excitatory or inhibitory can have multiple components e g fast and slow components increase and decrease conductance components excitatory and inhibitory components etc and can manifest homosynaptic plasticity i e depression facilitation or both v Chemical synaptic connections can include a description of a pool of transmitter that is regulated by depletion and or mobilization and that can be modulated by intracellular concentrations of ions and second messengers Thus the User can simulation heterosynaptic plasticity v Descriptions of synapses both chemical and modulatory can include a voltage dependent component For example a synapse can include a NMDA like conduct
59. properties SNNAP can also be used to simulate the flow of current in multi compartment models of cell Many common experimental manipulations can be simulated such as injecting current voltage clamping and applying modulatory transmitters SNNAP since version 5 was implemented in the Java programming language Thus SNNAP can run on virtually any computer and with most operating systems Tutorial Manual for Version 8 of SNNAP INTRODUCTION TO TUTORIAL This tutorial was designed for non programmers who are interested in carrying out computer simulations of neuroscience experiments This hand on tutorial will guide you through several examples using SNNAP Each chapter was designed to introduce you to several basic SNNAP capabilities in a particular domain of neuroscience You will learn how to use SNNAP to write mathematical models and run computer simulations in the various domains described in each chapter This tutorial may complement an introduction to computational neuroscience or be a basis for further investigation Outline This manual is a hands on tutorial designed to provide you with experience in using SNNAP as well as introduce you to several topics in computational neuroscience e Chapter 1 ntroduction presents an overview of SNNAP and this tutorial as well as a short description of SNNAP highlights e Chapter 2 Getting Started describes how to download and install the SNNAP software e Chapter 3 Hodgkin Huxl
60. quation window 54 Tutorial Manual for Version 8 of SNNAP yee gt PF SLL Click the second equation Mechaelis Menten form and it will appear in the Editor window as shown in Fig 4 17 There is one parameter which must be set click on parameter a the disassociation constant and set the value to 1 0 Click the button ok to close the window and click Yes in the Save Modified File dialog box Open Save Saveas Edit BR ffx Figure 4 17 Biochemical regulation equation of Mechaelis Menton form There are two parameters that must be changed for the model to exhibit bursting behavior the maximal time constant and the input current To change the time constant parameter you need to open the time constant equation in the appropriate editor Open the Formula Editor window and click on button A to open the file chooser window Locate the file m1k A in folder SNNAPhome SNNAP8 tutorial tutorialExamples bursterCPG Open the file m1K A and click on parameter ta A window will appear where you can click on t a see Fig 4 18 to open a dialog box 55 Tutorial Manual for Version 8 of SNNAP ee aM SY SS xl CHSNNAPISNNAP8 tutorialitutorialExamples bur CASNNAPISNNAPStutorialtutoriaiExamplesibursterCPGmIK A Open Save saveas Edit Save Edit tmax Mia hy yisi Pais ha fsa tmax 0 008695 hl 12 0 h2 12 0 si
61. rent see red curve in lower trace Finally the interspike interval became shorter than the refractory period of the neuron and therefore the neurons stopped firing Exercise Using a time to peak time of 1msec what is the highest oscillating frequency possible for this network BUILDING NETWORKS SNNAP offers a graphical user interface to help you construct a network Constructing a network is essentially a two step process The first step is to declare neuron models 78 Tutorial Manual for Version 8 of SNNAP which will represent the neurons of the network The same model may be used for several neurons or various models may be used The second step is to connect the neurons into a network The links may be one of three types of synaptic connections The equations governing the type of connections are provided in the equation library formulaTemplates folder Adding a Neuron to a Network We will now add a fourth neuron to our three neuron network Adding a neuron to a network from a library of neurons that already exist only requires us to modify the network through the network editor Open the network editor by clicking the button Edit Network in the main SNNAP window Click File gt Open to open the file chooser dialog box Click on the file name hhNetwork ntw in folder SNNAP8ltutorialltutorialExamples hhNetwork and click the button Open Click the menu Edit gt Add Neuron gt Add H amp H Neuron as shown in Fig 5 11
62. ri Dec 13 15 48 05 GMT 06 00 21 The University of Texas Health Science Center at Houston Figure 4 19 Bursting behavior of Morris Lecar model with Rinzel Ermentrout extensions Lets change the duration of simulation to three seconds in order to display several burst periods This we will do in the next section dealing with the output screen CHANGING OUTPUT SCREEN There are numerous model variables and functions that can be displayed on the output screen But first lets extend the duration of simulation We have done this previously in Section Changing Simulation Duration Edit the simulation file m1 smu and extend the simulation to three seconds Edit the Output file and modify the time variable to a maximum time of three seconds Edit the treatment file and modify the duration of stimulation to 3 seconds You can rerun the simulation and observe several bursting periods 57 Tutorial Manual for Version 8 of SNNAP EE lt a 23 Lets add the variable intracellular calcium concentration to the output screen and see the evolution of this variable with time Adding New Output Variable To add a new output variable open the Output Screen window click button Edit Out Screen in main SNNAP window Click File gt Open and select the file m1 ous In the Edit Output Setup File window click Edit gt Add gt Add Variable A variable dialog box will appear Edit the dialog box to resemble Fig 4 20
63. rties window Edit the field Channel number and change the value to 2 Click Ok and the calcium scale should appear on the second graph on the lower half of the Output screen as in Fig 4 23 Click Fi le gt Save and rerun the simulation 60 Tutorial Manual for Version 8 of SNNAP 5 amp I SNAP Edit Output Setup File 0 01 200 _Cion CaML J lt tive 1 60 120 080 0 40 Figure 4 23 Output screen with two graph panes 61 Tutorial Manual for Version 8 of SNNAP g SNNAP On Line View of Simulation File Graphic Output Data Output Report Help 300 _ VOML J lt five 20 0 Start the simulation 10 0 0 0 10 0 20 0 30 0 40 0 50 0 am Sm lt lt T wo _ 2 20 Cion Ca ML e mr 0 0 0 6 y ale 18 2 4 3 0 time lt ivt SNNAP On Line Simulation CASNNAPSNNAPS tutorialitutorialExamples bursterCPGiml smu Mon Dec 16 10 48 28 GMT 06 01 The University of Texas Health Science Center at Houston Figure 4 24 Membrane potential and calcium concentration on separate graphs Now that we know how to display two separate graphs with SNNAP lets display the ion currents of our model to better understand the dynamics of the model Displaying lon Currents Figure 4 24 displays the time course of two model variables membrane potential and intracellular calcium concentration Lets add to the display window the major currents of this model the inward calcium an
64. s Rather byte code is an intermediate or generic form of the program that is used by the JRE Java Runtime Environment The JRE are specific to each type of computer and operating environment The user must locate and install the appropriate JRE for their computer system The JRE provides a virtual machine within which the byte is run Finally the user invokes the JRE and runs the Java application such as SNNAP Thus the user installs the JRE that is specific to their computer and runs a generic version of SNNAP byte code Java programs are machine and operating 15 Tutorial Manual for Version 8 of SNNAP system independent to the extent that Java is supported for each type of computer and operating environment Because SNNAP is distributed as a jar file no installation pre se is necessary You can simply copy the SNNAP8 jar file and its associated subdirectories onto your computer It is necessary however to have Java installed see below Java and the necessary installation instructions can be obtained free of charge from the Sun web site http java sun com To test whether the Java installation was successful use the command line interface of your operating system and simply types java version at the command line prompt If Java is installed properly the window will display the version of the installed Java environment similar to Fig 2 1 Once Java is installed and SNNAP has been copied onto yo
65. s and functions we can display to aid in understanding how model behavior comes about For example it is clear that in our model membrane voltage fluctuates between 50 and 15mV while intracellular calcium concentration varies between 0 5 and 1 9uM There are ionic currents which may vary by several orders of magnitude It would therefore be convenient to view several graphs on one screen using different scales As a first step lets see how to separate the two displays in Fig 4 21 Open the screen editor by clicking the Edit OutScreen button in the main SNNAP window In the Edit Output Setup File window click Edit gt Add gt Add Channel which will open the 59 Tutorial Manual for Version 8 of SNNAP eee SS CO Modify Channel Properties dialog box as shown in Fig 4 22 set the parameters accordingly 2 nodiy Channel Properties channel variable times 7 channel gain 1 number of ticks 5 minimum 0 0000 0 00 maximum 3 0000 3 00 color name Figure 4 22 Dialog box to create additional graph panes This is identical to the time axis for our current display Click Ok and the new axis will appear on the Output Screen see Fig 4 23 Now we need to move the calcium scale to this graph Click on Var2 lt ch1 Cion in the Variables pane on the right Once selected the text is highlighted in blue Click Edit gt Modify to open the Modify Variable Prope
66. seseseeeeesenaeerenacarecasasasaeaeareres 67 Introduction to Small Neuronal Networks 5 suciscssssssnseavrsarnaniaannsmmanianshinnationiunianiniaaann 68 Running Small Neuronal Network Simulation ccescsecesccsecssecsecsseserscneneedtenseseeecenenneecesatenens 68 ST 9 04 1101 RE EE ee ee 70 Changing Synaptic Strength 45454 49445455444ev494p5p Se 75 B ilding NEO used 78 Adding a Neuron to a Network nen 79 Adding a Synaptic Connection in a Network cecesesseccceesssseesesenesesesesseseeeeeeeseeenenaneceanessseneeess 81 CONCIUSIONS ERE EN SER tence ey ene AN a 85 Appendix References Luer duces tihacadelancutmmanutccd ele cutustebnoed oloncecavansues aden satis tatannladenentsasmarelcios 86 Published Studies That Used SING Peaster tesla etter ancestries ES 86 Literature Cited in Manual EEE EEE EEEE nEn EE nenene 88 3 Tutorial Manual for Version 8 of SNNAP yr Disclaimer SNNAP is distributed as is This program is distributed in the hope that it will be useful but without any warranty without even the implied warranty of merchantability or fitness for a particular purpose The authors make no claims as to the performance of the program Individuals who wish access to the source code should contact Douglas Baxter uth tmc eud Tutorial Manual for Version 8 of SNNAP CHAPTER 1 INTRODUCTION TO SNNAP Recently there has been a dramatic increase in the number of neurobiologists using computational methods as an
67. sion 8 of SNNAP ee ES 55005005 accumulation By reducing the rate of intracellular calcium accumulation the duration of activity is increased Figure 4 28 shows a slightly reduced value for parameter k2 of the calcium ion equation ion formula in Formula Edior window reduced from 0 4 to 0 35 The burst duration is increased compare with Fig 4 27 EF SNNAP On Line View of Simulation iol x File Graphic Output Data Output Report Help 30 0 20 IML J ivt Cion Ca ML ivr t2 10 0 og 04 20 0 04 02 30 0 12 50 0 2 0 0 0 10 2 0 30 40 5 0 times vr 20 500 og _IVDxREG K Ca ML J lt ivz Ivd K ML s vr Ivd Ca ML lt ir 14 40 0 10 0 0 8 30 0 20 0 02 200 300 0 4 10 0 40 0 10 00 50 0 5 0 0 10 FP 30 40 50 times vr SNNAP On Line Simulation CASNNAPISNN A P8MtutorialltutorialExamplesVbursterCPGiml smu Mon Dec 16 15 21 59 GMT 06 00 The University of Texas Health Science Center at Houston Figure 4 28 Burst modulation by decreasing calcium acc 66 Tutorial Manual for Version 8 of SNNAP CHAPTER 5 MODELING SMALL NEURONAL NETWORKS In this chapter we will learn how to simulate small networks of neurons using SNNAP A graphical user interface is provided with SNNAP which allows you to connect neuron models into networks The Hodgkin Huxley model presented in chapter 3 will be used as our neuron model Our network will be composed of several such neuron models Neuro
68. ted by the ion click Edit gt Add Connection gt Cond by Ion in the Edit Neuron window The Add Connect dialog box will appear as seen in Fig 4 11 The equation which governs the modulation of the current using the designated ion will reside in the file that is declared using this dialog box Set the filename to m1Ca fBR E Add Connect Add Connect 4 x lon name Ca ba Conductance name K Ca v File name mICa BR E Color name E blue v Figure 4 11 Select the destination current for ion activation and provide the filename which will contain the governing equation Click the button Ok in the dialog box The final display of the Edit Neuron window is shown in Fig 4 12 50 Tutorial Manual for Version 8 of SNNAP Tox File Edit ColorSetting Print Help GENERAL C SNNAPISNNAPBitutorialitutorialExamples bursterCPGimi neu CURRENT gt ION Threshold 0 0 een I Ca gt Ca Spikdur 0 0030 NMINIT 30 0 N S N Cm 0 0010 enk m 1 0 NK 1ON gt CURRENT 2 Ca gt 1 K Ca nano ca TRNSMTR POOL hi Maco 7 SM gt CURRENT CONDUCTANCES K Ca 1ON gt TRI leak Ca K IONS POOL SM gt TRF Ca PEN EEG K ION gt SM SM POOL LS J Se Ca SM gt SM G The University of Texas Health Science Center at Houston Figure 4 12 Final view of Edit Neuron window with incorporated calcium ion On the right hand side of the wi
69. the formula editor A file chooser dialog box will open Locate the cs file in our case it is SNNAP amp ltutorial tutorialExamples hhNetwork Synapse hhSyn cs The function that is used the alpha function is defined in the fat file To edit this file use the formula editor and click on the button fAt A window as seen in Fig 5 7 will appear Clicking on the highlighted parameter At will cause the equation window to open as seen on the right panel of Fig 5 7 The only value to edit with this alpha function is the time to peak currently set at 0 002 sec 2 msec 74 Tutorial Manual for Version 8 of SNNAP ee SNNAP Editor for fAt File Open Save Saveas Eat fA 4 At ffx u 0 0020 Browse Ok Figure 5 7 Alpha function equation There are two parameters that influence the effectiveness of the synaptic current The parameter u alters the duration of the current while the maximal synaptic conductance Zn Changes the strength of the current Lets increase the synaptic strength of the current to 2mS cm2 Changing Synaptic Strength In order to change the chemical synaptic cs strength you must open the cs file through the formula editor In the main SNNAP window click Edit Formula In the Formula Editor window click cs which will open a file selector window Select the file SNNAP amp Tutorial_Examples HH_Network Synapse hhSyn cs Click the parameter g w
70. the membrane is taken to be positive Also the resting potential has been shifted to 60mV from the original OmV RUNNING THE HODGKIN HUXLEY MODEL WITH SNNAP SNNAP is provided with the Hodgkin Huxley model in the file SNNAPhome SNNAP amp 8 tutorial tutorialExamples hhModel SNNAPhome is the location where you installed the SNNAP8 directory In order to run the model you will need to launch SNNAP and run the simulation example Launching SNNAP To launch SNNAP go to the folder SNNAP8 The folder has the following structure Name examples tutorial E CellMatrix jar E snnaPs jar Figure 3 1 The structure of the folder SNNAP8 Double click the executable file SNNAP8 jar to open the main SNNAP window see Figure 3 1 23 Tutorial Manual for Version 8 of SNNAP Simulator for Neural Networks and Action Potentials The University of Texas Health Science Center at Houston Copyright c 2002 All Rights Resererd Figure 3 2 Main SNNAP window The main SNNAP window provides buttons to open the various functional windows to edit SNNAP parameters edit model and network parameters and execute simulations Running a Simulation In the main SNNAP window click the button Run Simulation to open the simulation viewing window 24 Tutorial Manual for Version 8 of SNNAP EF SNNAP On Line View of Simulation E ioj x File Graphic Output Data Output Report Help Load Simulation N
71. the equation file is SNNAPhome SNNAP amp 8 tutorial tutorialExample bursterCPG mIKCA vdg xi Save in C burstercPG ca e cs Be C micavdg C mik vdg C miLeak vdg File Name miKCavdg Files of Type File type vdo Figure 4 6 Dialog box to save vdg file in model folder We have now completed the description of the new ion conductance and incorporated it into SNNAP The use of calcium to activate a potassium current requires us to introduce the concept of calcium as an intracellular ion pool We now turn to introducing how to use ion pools with SNNAP lon Pools lons can play a role in a cell as molecules that activate or inactivate certain processes Such ions are called jon pools in SNNAP Calcium dependent potassium channels are activated by intracellular calcium the higher the calcium concentration the higher the channel activation In order to use an ion as a second messenger SNNAP must be programmed to calculate the ion concentration Use the window Edit Neuron to declare ion pools in SNNAP To open the Edit Neuron window click the button Edit Neuron in the main SNNAP window Locate and select the file m1 neu in the folder 46 Tutorial Manual for Version 8 of SNNAP SNNAP8ltutorialltutorialExamples bursterCPG and click the button Open Figure 4 7 shows that the K Ca conductance was included into the model description EF SNNAP Edit Neuron
72. twork composed of three Hodgkin Huxley neuron models see chapter 3 Such neuron models exhibit an action potential as a response to suprathreshold stimulation We will simulate a network of three Hodgkin Huxley HH model neurons connected by excitatory chemical synapse The network architecture is in the form of a ring After providing an initial transient stimulus to one neuron the three neurons fire sequentially and the network exhibits stable oscillatory behavior Running a Small Neuronal Network Simulation This SNNAP tutorial provides you with a small network you can run and experiment with Open the main SNNAP window double click the file SNNAP8 jar in folder SNNAP 8 and launch the simulation window click button Run Simulation Locate the file hhNetwork smu in folder SNNAPhome SNNAP8 tutorial tutorialExamples hhNetwork and run the simulation see section Running a Simulation Note For Linux users you should copy the model in SNNAPhome SNNAP8 tutorial tutorialExamples archive Linux initialModel hhNetwork to your working directory SNNAPhome SNNAP8 tutorial tutorialExamples hhNetwork Figure 5 1 displays the results of the simulation 68 Tutorial Manual for Version 8 of SNNAP Fa SNNAP On Line View of Simulation ioj x File Graphic Output Data Output Report Help 60 0 600 _ 60 0 V Neuron 2 n7 V Newon 3 lt vr V Neuron 1 svr Start the simulation 40 0 400 1 40 0
73. ual for Version 8 of SNNAP 0 00 mete ooo IVDXREGIK Ca ML ivr Ivd K ML ivr Figure 4 26 Output screen in after addition of current variables for ploting Now lets rerun the simulation 64 Tutorial Manual for Version 8 of SNNAP Fa SNNAP On Line View of Simulation 0 x File Graphic Output Data Output Report Help 30 0 20 VIML ivz Cion Ca ML ivr 12 10 0 0 0 04 200 04 02 30 0 12 50 0 2 0 Cle 0 0 06 4 Aa 18 24 30 Ss time lt ivt 20 50 0 og _IVDxREG K Ca ML lt ivz Ivd K ML s r Iva Ca ML J lt ivr 14 40 0 100 0 8 30 0 20 0 02 200 300 0 4 10 0 40 0 10 00 50 0 0 0 06 18 24 30 time ivr SNNAP On Line Simulation CASNNAPVWS PS tutorial tutorialExamples bursterCPGiml smu Mon Dec 16 14 20 46 GMT 06 00 The University of Texas Health Science Center at Houston Figure 4 27 Final display of model two variables and three currents CENTRAL PATTERN GENERATOR MODULATION There are three basic characteristics of a bursting neuron e the duration of spiking activity e the frequency of action potentials during the burst e the duration of the quiescence period The period of an entire bursting event is the sum of both active and quiescence durations There are various parameters that can alter the behavior of bursting neurons For example the duration of the burst is dependent on the rate of intracellular calcium 65 Tutorial Manual for Ver
74. uency of the neurons are less than 125Hz see Fig 5 5 because of the added neuron in the ring 84 Tutorial Manual for Version 8 of SNNAP EE eee SS EF SNNAP On Line View of Simulation Jo x File Graphic Output Data Output Report Help pr 600 ano eoo _ Neun 4 Je or Neuron _2 lt ivr Newon 3 lt imiV Neuon 1 etsvr Fy a DA SNNAP On Line Simulation CASNNAPVSNNAP8MtutorialttutorialExamplesthhN etwork hhN etwork smiyi Dec 20 16 01 02 GMT 06 00 2 The University of Texas Health Science Center at Houston 0 004 0008 0 012 0 016 time 17 Ics Neuron 2 gt Neiti einem Potivaj Neuron 0008 time 17 Figure 5 17 Four neuron network with synaptic conductance of 2mS cm2 CONCLUSIONS We have seen how to construct a network with SNNAP The graphical user interface is intuitive and easy to use The connections between neurons may be chosen from a library of equations and specific parameters provided by the user 85 Tutorial Manual for Version 8 of SNNAP APPENDIX REFERENCES PUBLISHED STUDIES THAT USED SNNAP 1 Baxter D A Canavier C C and Byrne J H 2002 Dynamical properties of excitable membranes In Byrne J H and Roberts J Eds Cellular and Molecular Neuroscience Chapter 7 New York Academic Press Baxter D A Canavier C C Clark J W and Byrne J H 1999 Computational model of the serotonergic modulation of sensory neurons in Aplysia J Ne
75. ur computer running SNNAP can be as simple as double clicking on the SNNAP 8 jar file cv Command Prompt E Microsoft Windows XP Version 5 1 26001 lt C Copyright 1985 2661 Microsoft Corp P gt java version Standard Edition Cbuild 1 4 1 bh 3 gt Java HotSpot lt TM gt Client UM lt build 1 4 1i bh 3 mixed mode gt PN Figure 2 1 Testing the Installation of Java To test whether Java is properly installed the User must first access the command line interface of their operating system All Java applications are initiated from the command line interface In Microsoft Windows the command line interface is often referred to as a DOS window In UNIX environments the command line interface can be accessed via a console or terminal window To test whether Java is properly installed the User simply types java version at the command prompt If Java is installed properly there should be a response similar to that illustrate above Otherwise the operating system will respond with an error message If the response is an error message the User should refer to the installation instructions provide with Java lt may be necessary to modify some environment variables of the operating system e g the PATH or User PROFILE Installing SNNAP for specific operating environments The specific procedures for installing Java and SNNAP are listed for three operating systems Microsoft Tutorial Manual
76. urophysiol 82 2914 2935 Baxter D A Susswein A J and Byrne J H 2002 Mechanisms of pattern generations underlying fictive feeding in Aplysia J Neurophysiol under review Byrne J H Cleary L J and Baxter D A 1990 Aspects of the neural and molecular mechanisms of short term sensitization in Aplysia modulatory effects of serotonin and cAMP on duration of action potentials excitability and membrane currents in tail sensory neurons In L Squire and E Lindenlaub Eds The Biology of Memory pp 7 28 New York F K Schattauer Verlag Cai Y Baxter D A and Crow T 2002 Computational study of enhanced excitability in Hermissenda type B photoreceptors underlying one trial conditioning Role of conductances modulated by serotonin Biophy J under review Cataldo E Byrne J H Baxter D A and Brunelli M 2002 Conduction block at axonal branch points in touch mechanoafferents T cells of the leech J Neurophysiol In preparation Flynn M C Cai Y Baxter D A and Crow T 2002 Computational study of Hermissendia type B photoreceptor Role of action potential duration in synaptic facilitation Biophy J under review Kabotyanski E A Ziv I Baxter D A and Byrne J H 1994 Experimental and computational analyses of a central pattern generator underlying aspects of feeding behavior in Aplysia Netherlands J Zool 44 357 373 Komendantov A O and Kononenko N I 2000 Caff
77. w must connect this neuron to the other neurons of the network In the Edit Network window click the menu Edit gt Add Connection gt Add Chemical as seen in Fig 5 14 to open the dialog box Add Chemical 81 Tutorial Manual for Version 8 of SNNAP ES SNNAP Edit Network o x ColorSetting Print Help CASNNAPISNNAP8 tutorialitutorialExamplesihhNetworkihhNetwork ntw Connections Neuron 1 lt Neuror Neuron 2 lt Neuror Neuron 2 Neuron 3 lt Neurorl Add Neuron Add Modulatory Add Electrical Add Weight I amp F a Neuron 3 lt Neuron_1 j Neuron 4 The University of Texas Health Science Center at Houston Figure 5 14 Add a synaptic connection from Neuron_4 to the network In the Add Chemical window we define the synaptic connection There are two connections that need to be defined from Neuron_3 to Neuron_4 see Fig 5 15 and from Neuron_4 to Neuron_1 For both connections we will use the same synaptic model file which is Synapses hhSyn cs The parameter Receptor type is a label you may use to distinguish various synaptic connections which appears in the pane Connections of the Network window Also the connection from Neuron_3 to Neuron_1 should be deleted Select this connection in the Connections pane of the Edit Network window and click Edit gt Cut Note For Linux users you must provide the pathname from the folder where snnap8 jar IS located

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