Documentation for the ACnet2 GENESIS model

------------------------------------------

I encourage you to extend and share this tutorial/documentation and simulation script package with others. The files here may be freely distributed under the terms of the \`GNU Lesser General Public License version 2.1 <figures/copying.txt>\`*.

David Beeman

University of Colorado, Boulder

dbeeman@colorado.edu

Sat Aug 31 12:30:34 MDT 2013

h3. Introduction

Cortical waves have been observed in many cortical areas, including the primary auditory cortex . For example, see Kral et al. . The **ACnet2** model was developed both as a study of the propagation of cortical waves, and as a tutorial example for the construction of cortical networks. The simulation scripts are designed to be run with GENESIS 2.3 and are extensively commented, are easily customizable and are designed for extension to more detailed models, or conversion to other simulation

systems. The model is slowly being implemented in GENESIS 3 and a conversion to neuroConstruct is planned. The scripts will be of particular interest to those GENESIS network modelers who have not taken advantage of the ten-fold speed increase available with the use of ‘hsolve’

for network simulations.

This package also contains both GENESIS and simulator-independent Python tools for the visualization and analysis of network activity.

More information about GENESIS can be obtained from http:genesis-sim.org and Beeman and Bower .

The scientific purpose and simulation results are described in the published abstract and in the CNS 2013 poster presentation, which may be viewed here in two panels:

\`Beeman-CNS2013-poster-top.pdf <figures/Beeman-CNS2013-poster-top.pdf>\`*

\`Beeman-CNS2013-poster-bottom.pdf <figures/Beeman-CNS2013-poster-bottom.pdf>\`\_

These are to be read left column first, top to bottom, then right column from top to bottom.

This document describes the model and how to use the provided simulation scripts to obtain the results shown in Beeman (2013) Fig. 1 (also at the top right of the poster). It also suggests experiments that may be tried with the many configurable options in the scripts, and ways to extend the model. It is my hope that this short tutorial and the example simulation scripts can provide a head start for a graduate student or postdoc who is beginning a cortical modeling project.

### The ACnet2 model

Biologically detailed and realistic models can hope to reproduce the relevant mechanisms that cause the system’s behavior. But, they have a very large space of poorly-defined or poorly-quantified parameters to be explored. This makes the analysis and interpretation of results very difficult. However, simplified models, such as networks of point integrate-and-fire neurons without dendritic structure, may leave out important details that affect network behavior.

The ACnet2 model attempts a compromise by restricting the model to a piece of the thalamorecipient layer (IV) of primary auditory cortex (AI). It has has only two populations of neurons, excitatory ‘Ex\_cells’ and inhibitory ‘Inh\_cells’. The default configuration of the model is for the Ex\_cell population to be a 48 x 48 grid of 9-compartment pyramidal cells, and for the Inh\_cells to be a 24 x 24 grid of two-compartment basket cells. The size of the two grids, cell spacing, and types of cells, as well as the location of synaptic connections may configured in the main script. Inputs from other cortical layers were crudely represented by Poisson-distributed random inputs to provide background levels of firing for the two populations in agreement with typical measured values.

The model omits the following important features:

-   A detailed model of interlaminar connections

-   Other types of inhibitory channels (e.g.GABA\_B, NMDA) and cells (e.g. chandelier) with their particular patterns of connections

-   gap junctions betweeen inhibitory cells

-   Synaptic plasticity (e.g. facilitation, depression, STDP)

This simplification nevertheless leaves the weighted synaptic conductances for the four types of connections as poorly known adjustable parameters.

The scaling and balancing of these conductances depends strongly on the assumptions made on cell density and the connection probablity between cells as a function of separation. The goal of this modeling effort was to find a suitable set of the four maximal synaptic conductances (gmax). This should allow the propagation of waves of excitation with neither widespread undamped oscillations nor excessive inhibitory supperession of the response to stimuli.

The top right portion of the CNS poster describes how the default parameters (shown in the \`Runtime GUI <figures/0001C_run.png>\`*) produce propagation and constructive interference from two tone inputs to the model. Although the input model provides for a realistic spread of thalamic inputs that have typically observed spike time distributions at a given characteristic frequency, this example used single row excitations from spike trains of 220 and 440 Hz, in the absence of background input.

h3. The cell models

The main features of the two cell models are shown in the figure below.

![](https://github.com/OpenSourceBrain/ACnet2/raw/master/GENESIS/figures/AC_cells.png)

The morphology and passive parameters of the pyramidal cell are based on the reduced neocortical pyramidal cell models of Bush and Sejnowski . The passive symmetric compartmental model was transformed to an equivalent asymmetric model by a method that scales compartment dimensions to preserve the passive properties of each compartment. The resulting cell has the same input resistance of 47 Mohm and membrane time constant of 10 msec. The soma contains voltage and calcium activated channels, and there are dual-exponential synaptically activated channels at appropriate locations on the dendrites. The basket cell is a simple ‘ball and stick’ model with a 40 micrometer diameter soma and a 200 x 2 micrometer cylindrical dendrite. It has an input resistance of 113 Mohm and a time constant of 10 msec.

The active channels used in the soma are a small set of modified Traub

et al. hippocampal CA3 region channels with activation and

inactivation time constants scaled to give dynamics typical of neocortical

cells. Parameter searches were performed manually and with the GENESIS 2

parameter search library simulated annealing method to approximately fit current clamp results by Nowak et al.

for regular spiking cells in cat visual cortex, and by Hefti and Smith

 for rat layer V primary auditory cortex.

The connection model

———————

Some neocortical models assume all-to-all connections with a constant small

probability, such as 0.02. Others use a larger fixed connection

probability over a specified region, but have a synaptic weight factor that

decays with separation distance. Recent studies of excitatory and

inhibitory synaptic connectivity show that the connection strength is

relatively independent of distance, but that the probability falls off with

a Gaussian probability with distance. Thus, in accordance with

experimental measurements on cat AI by Yuan et al. and rescaled

results for mouse AI by Levy and Reyes , a fixed connection

weight was used, and the probability for both excitatory and

inhibitory connections was taken to vary with radial distance r in the

layer as::

 P (r) = P0 \* exp\^2), with the s = 10\*SEP\_X = 0.4 mm

 P0 = 0.15, P0 = 0.45, P0 = P0 = 0.6

For this model, the separation between excitatory cells SEP\_X = 40 micrometers.

The maximum range for connections was 1 mm. This connection scheme

may be easily modified in the main simulation script to corresond

to measurements from other cortical areas.

Many simple cortical models ignore axonal conduction delays. Experiments

with this model showed that in order to produce propagation of waves of

excitation, the conduction delays should be large enough to produce a

sufficient delay in the onset of inhibition after excitation. This could

not be achieved by merely increasing the time constant for the inhibitory

conductance. Estimates for the conduction velocity of short

intralaminar unmeyelinated axons are in the range of 60-90 mm/s. . Values for longer distance axons between other cortical

layers or regions are generally higher, ranging from 0.2 to 0.3 m/sec.

. This simulation uses the default axonal

conduction velocity of 0.08 m/sec, or a delay of 12.5 sec/m. This delay can

have a significant effect in the delay of the onset of inhibition, as

connected cells 400 um apart can have a delay of 5 msec.

In addition to the interconnections between the cells, the excitatory cells

receive a Poisson-distributed random activation at their basal dendrites in

order to represent excitatory inputs from other layers. The default

parameters and average frequency were chosen in order to give

background levels of firing for the two populations in agreement with those

measured by Steriade et al. .

The runtime GUI

—————

The screen capture of a typical simulation run

shows the Control Panel with the simulation

parameters.

.. image:: figures/0001C\_run.png

These parameters were used to produce the network activity shown in

the ‘netview’ replay of the results.

.. image:: figures/C0003Aasc\_prop-sm.png

The sequence of frames at times from 0.8060 to 0.8138

represent the soma membrane potential of each excitatory cell on the 48 x

48 grid at the left. These are an indication of firing or changes in the

subthreshold potential. Those on the right give their post-synaptic

currents due to excitatory connections from other pyramidal cells.

These show initial responses to the two tone input produce a growing wave

of excitation past the initial responses of the input rows, culminating in

constructive interference in the middle shown in frame E.

The plots shown in the runtime GUI are for the middle cell in the labeled

row. Thus, the ‘Ex\_cell Membrane Potential’ plot shows that there are

pulses of firing in the middle cells of rows 12 and 36, corresponding

to the left netview displays. The ‘Ex\_cell synaptic currents’ plots

can be related to the right column EPSC display. These plots are useful

in tuning the synaptic conductances to achieve a good balance of

excitation and inhibition.

The input model

—————

The input model provided by the default simulation is specialized to

represent the tonotopic organization of inputs from the ventral division of

the medial geniculate body . This is the primary source of thalamic

inputs to the primary auditory cortex. The description of the scripts

further below tells how the input model may be modified to represent other

types of inputs, for example when using the network to model other cortical

areas.

The default parameters give a 48 x 48 network of Ex\_cells with rows

numbered from 0 to 47, and a 24 x 24 network of Inh\_cells with rows

numbered from 0 to 23. However, inputs are not provided to cells in the bottom 8 and top

8 rows, in order to avoid edge effects. As a result, there is an array of

Ex\_NY - 16 = 32 input “thalamic cells”, which are numbered from 1 to 32.

Each one of these sources of spike trains targets a row of Ex\_cells with a

row number that is offset by 7 from the input number. This accounts for

the entries in the ‘input\_control’ form below the Control Panel, where

Input 29 is to Target row 36. Initially, the inputs are assigned a

frequency on a logarithic scale from 220 Hz to 538.584 Hz with a default

set of pulse parameters, and are disconnected. By specifying an input

number corresponding to the row to be activated, these parameters may be

changed, and the Spike Train toggle set to ‘ON’, as shown above.

Providing the identical input to an entire row of cells produces more

correlation in the inputs than one would expect from thalamic connections.

One alternative would be to have a two-dimensional array of thalamic

inputs. The approach taken in this simulation is to have an ‘input\_delay’

for each connection from an input to the row of cells, and an

‘input\_jitter’ factor that provides a random ‘jitter’ in the arrival time

of the input to each cell in the row. This is implemented with a random

delay uniformly distributed between input\_delay**and

input\_delay**. The default values of these are 0,

but a reasonable amount of decorrelation can be provided with

input\_delay = 0.002 seconds, and input\_jitter = 0.4. These can be set

separately for each input via the ‘input\_control’ GUI.

Once set, the delays with jitter are the same at each time step. They

represent a decorrelation between the inputs to a row, not a true jitter in

spike arrival time. Yet, thalamic inputs are known to have a jitter in the

time at which they arrive at their targets. A typical value would be

0.0005, or 0.5 msec. As described below in the ‘Overview of the simulation

scripts’, this may be changed from the default of zero with the variable

‘spike\_jitter’.

There are yet more options for providing more realistic thalamic inputs.

In addition to providing other options for the type and distribution of the

inputs, there is a parameter ‘input\_spread’. This the number of rows below

and above the “target row” getting thalamic input. Typical values span

about one-third of an octave. The implementation in simple\_inputs.g

function **connect\_inputs** creates connections with an exponentially

decaying probablility to adjacent rows +/- input\_spread.

The default value used here is 0 , but a line in the main script can be changed to set it

to 1/6 of an octave, or any other value.

Running the simulation

———————-

Of course, the first step in running the simulation is to have GENESIS 2.3

installed. If you have not used GENESIS before, it is simplest to download

the entire ‘Ultimate GENESIS Tutorial Distribution’ package with all source

code, installation instructions, and the complete set of tutorials

 from \`http://genesis-sim.org/GENESIS/UGTD.html

<http://genesis-sim.org/GENESIS/UGTD.html>\`*. GENESIS installs without

problems under modern versions of Linux.

**ACnet2-main.g** is the main simulation script, with default values of

the parameters that were used to generate the results in Beeman (2013).

It includes the files to create the GUI and contains many configurable

options.

**ACnet2-batch.g** is a stripped down version with fewer options and no

graphics. Once GENESIS 2.3 has been installed, the command::

genesis ACnet2-batch.g

runs the simulation with the parameters used to generate the figure in

Beeman (2013). It creates the output files that can be displayed with

the Python G-3 Network View tool in this package, printing messages

such as::

Starting connection set up: Sun Aug 11 14:47:17 2013

 (many dots and debugging messages)

 Finished connection set up: Sun Aug 11 14:49:39 2013

All maxium synaptic weights set to 1

Using simple pulsed spiketrain input

 ….

 time = 0.000000 ; step = 0

 time = 0.000000 ; step = 0

 time = 0.000000 ; step = 0

 Network of 48 by 48 excitatory cells with separations 4e-05 by 4e-05

 and 24 by 24 inhibitory cells with separations 8e-05 by 8e-05

 Random number generator seed initialized to: 1369497795

Average number of Ex\_ex synapses per cell: 36

 Average number of Ex\_inh synapses per cell: 36

 Average number of Inh\_ex synapses per cell: 109

 Average number of Inh\_inh synapses per cell: 35

 time = 0.000000 ; step = 0

 time = 0.000000 ; step = 0

 dt = 1.999999949e-05 tmax = 1

 RUNID: B0003

 START: Sun Aug 11 14:49:41 2013

 END : Sun Aug 11 14:53:01 2013

genesis \#0 \>

At this point, you can type ‘quit’ to the prompt.

Note the significant amount of time spent setting up the connections. It

is nearly as long as the time to run the model for 1 second. This is

because a GENESIS script language (SLI) function **planarconnect\_probdecay**

was used to loop over source and destination cells. Some suggestions for

implementing this as a compiled GENESIS command are given in the section

‘Experiments to try’.

The ‘RUNID’ is a string that is given a default value in the simulation

script, and can be changed in the GUI for **ACnet2-main.g**. When

**ACnet2-batch.g** is run, it is used to produce output files with names:

-   run\_summary\_B0003.txt - a summary of simulation parameters, read by replay\_netview10.g

-   Ex\_netview\_B0003.txt - a file containing the membrane potential Vm for

     each Ex\_cell at time intervals ‘netview\_dt’, which is 0.2 msec, the

     slowest ‘clock’ used in this simulation.

-   Inh\_netview\_B0003.txt - Vm for the Inh\_cells

-   EPSC\_netview\_B0003.txt - for each Ex\_cell, contains the net Excitatory

     Post Synaptic Current (EPSC) due to connections from other Ex\_cells.

     The format is similar to the Ex\_netview file.

-   EPSC\_sum\_B0003.txt - the EPSC summed over all Ex\_cells in the network at

     each time step ‘out\_dt’ ( = 0.0001 sec). This has been related to

     signals detected by MEG and the Power Spectral Density (PSD)

     (Vierling-Claassen et al, 2008), which can be calculated with the

     analysis tools provided here.

The netview files are plain text with a header line in a format that is

described in comments in the main script.

The section below ‘Python data analysis and visualization tools’ tells how

to generate the \`wave propagation results <figures/C0003Aasc_prop-sm.png>\`*.

Running ACnet2-main.g

———————

Running the simulation from the GUI is similar, with the command::

 genesis ACnet2-main.g

After the connections have been set up, with similar messages to the

console, a GUI similar to the \`Runtime GUI <figures/0001C_run.png>\`*

will appear. The default RUNID of “0000” can be changed to another string

of your choice (no spaces or other unix-unfriendly characters). The output

files will contain this string in place of ‘B0003’. At this point, the

network will get a random Poisson activation at 8 Hz, and no thalamic

input. To get wave propagation results, set the Random Background

Activation Freq to 0, and use the ‘input\_control’ GUI to provide

the inputs::

Input 5 (Target Row 12) Input freq 220

 ‘Toggle to Spike Train ON’

Input 29 (Target Row 36) Input freq 440

 ‘Toggle to Spike Train ON’

leaving the pulse parameters as given. This will produce 50 msec tone

‘pips’ 150 msec apart.

**Don’t forget the GENESIS/XODUS eccentricity that you have to hit ‘Enter’

or click the mouse in a text field after you edit it, in order

for the changes to register.**

Then click RESET and RUN. After the run has completed, you may use

interactive GENESIS commands to inspect the model, or click on QUIT.

On examining the output files, you will notice that the ‘netview’ files

have the extension ‘.dat’ instead of ‘.txt’, and that the elapsed time

to run is about a minute shorter than with ACnet2-batch.g. That is

because ACnet2-main.g specifies the option ‘binary\_file = 1’, producing

output in the GENESIS ‘FMT1’ binary format. This is faster than

with text files, and the files are much smaller. It also allows

some spike analysis which is not yet possible with the Python tools.

However, when the **bzip2** compression utility is used, the text files

are much smaller than the binary files. The section ‘GENESIS data analysis

and visualization tools’ describes some of the analysis and visualization

that can be done using the binary files.

Python data analysis and visualization tools

———————————————

The directory **Gpython-tools** contains several Python scripts that were

developed for use with the analysis and visualization of results from

simulations carried out with both GENESIS 2.3 and G-3. They use plain text

data files with optional header information, and some can read text files

that have been compressed with the **bzip2** utility, making data storage

very efficient. New tools and documentation will be added to this

directory in future updates of the ACnet2 distribution, and will become

available through the \`GENESIS Documentation Homepage

<http://genesis-sim.org/userdocs/documentation-homepage/documentation-homepage.html>\`*.

The documentation for the present version of **Gpython-tools** is in the file

\`Gpython-tools/README.html <Gpython-tools/README.html>\`*.

These make use of the **maplotlib** and **wxpython** libraries, which must be

installed. When **netview-I.py** is installed in your search path, it can be

used to provide the animation from which the frames in the \`wave propagation

figure <figures/C0003Aasc_prop-sm.png>\`\_ were taken.

To reproduce the results from running the ACnet2-batch.g, bring up two

instances of **netview-I.py** for the Vm and EPSC data files::

\$ netview-I.py Ex\_netview\_B0003.txt &

 \$ netview-I.py EPSC\_netview\_B0003.txt &

You will have to click and drag the second netview window off of the first.

The Help menu ‘Usage and Help’ selection provides a pop-up window telling

you how to use the program, as with many of the tools in this collection.

In each window, click ‘New Data’. When the message ‘Data has been loaded’ appears

you can click ‘Play’, to replay the entire run. The frames shown in the

poster are at times::

A: 0.8060

 B: 0.8072

 C: 0.8082

 D: 0.8112

 E: 0.8138

To reproduce them, drag the ‘t\_min’ bar to 0.79 and the ‘t\_max’ bar to

0.80, then click ‘Play’. Then repeatedly click ‘Single Step’ until the

time display is 0.806. Continue for each of the next times, and compare to

the results in the \`wave propagation figure

<figures/C0003Aasc_prop-sm.png>\`*.

There are also tools to calculate the PSD of the summed EPSC file.

**plotPSD.py** averages the PSD from a wildcard list of times series data

files, such as the ‘EPSC\_sum\_B0003.txt’ file. **plotPSD0.py** is similar, but

it overplots the spectra on the same graph. A typical usage would be::

 \$ plotPSD.py EPSC\_sum\_M0005\*

 Plottting average of 4 runs from series M0005

 EPSC\_sum\_M0005B.txt EPSC\_sum\_M0005D.txt EPSC\_sum\_M0005E.txt EPSC\_sum\_M0005.txt

For a single file with a run time of only 1 second such as

EPSC\_sum\_B0003.txt , they both yield the

same low resolution plot. The PSD plots shown in the lower right column of

the poster were generated from averages of four ten second runs that used

different random number seeds.

GENESIS data analysis and visualization tools

———————————————

The GENESIS script **replay\_netview10.g** and its included files

**analysis\_funcs5.g** and **spectra\_funcs3.g** are scripts that perform

visualization and spike train analyis on the binary ‘netview’ files that

are generated with the option ‘binary\_file = 1’. These use a fairly

confusing mixture of XODUS graphical objects, spike generators, and obscure

uses of the GENESIS **table** object in order to generate a number of useful

analyses and plots that are not yet available with the Python tools.

In order to set up the graphics and analysis tools, **replay\_netview10.g**

needs to read the default ‘run\_summary\_0000.txt’ and other files that are

generated from a run of **ACnet2-main.g** with the default RUNID = “0000”.

Once these has been done you can type::

 genesis replay\_netview10.g

and use “0000” or another RUNID that has existing data.

Windows will appear for for Vm plots, Ex\_netview, Inh\_netview and

EPSC\_netview, as well as

**summary\_form** - Displays the run summary file in a scrolling text window

**histform** - A Firing rate distribution histogram for the Ex\_cells in the

network. This will typically by Gaussian, although a model with STDP

should show a lognormal distribution.

**freq\_form** - Displays the Average Spike Frequency for Ex\_cells and

Inh\_celsl with time, with a settable time bin size. This is particularly

useful when balancing conductances to give proper background firing rates

and fluctations in the rate.

**spectra\_form** - Hidden under the freq\_form, it calculates the PSD using

a slow Discrete Fourier Transform implemented in the GENESIS scripting

language. On a modern PC, the performance is not too bad, taking a little

over 20 seconds. However, the Python ‘plotPSD’ tools are better suited for

spectral analysis.

Then, click ‘RUN’ .

You will find that the spike frequency calculations will have produced a

new output file ‘spike\_freq\_0000.txt’ that did not exist before. The

**rowrateplot.py** utility in the \`Gpython-tools

<Gpython-tools/README.html>\`* directory can be used with the

‘spike\_freq*<RUNID>.txt’ files to create displays, similar to the \`average

spike frequency display <figures/freqplot_C0003A-new.png>\`* shown at the

left of the top poster panel. This display of the time course of the

average firing rate on a row can be useful in detecting propagation of

neuron firing from row to row. Note that replay\_netview10.g outputs

data for only the 32 ’target rows’ that receive inputs, so that the

display will normally not show all 48 rows. (This is a feature of

replay\_netview10.g, not of rowrateplot.py.)

Experiments to try

——————

Here are some suggestions of both simple and challenging things that can

be done to explore or extend the model.

**Changes in parameters through the GUI**

Most important model parameters can be changed through the GUI of

ACnet2-main.g. The ‘gmax’ values for the four types of synaptic

connections (which can also be scaled by the ‘Weight’ value) are a good

place to start.

The default values of the four synaptic conductance values shown in the

\`Run Time GUI <figures/0001C_run.png>\`\_ are more arbitrary than other

parameters. The assumption that the variable Inh\_inh\_gmax = 0.0, made also

in the model of Levy and Reyes (2011), produced a balance of excitation and

inhibition that resulted in an appropriate average background firing rate

for the inhibitory cells (about 22 Hz.) The GABA conductance of basket

cells, and their mutual connection probability are poorly known. However it

seems reasonable that there be some degree of mutual inhibition between

basket cells.

The poster mentions an alternate set of gmax values that increased the

basket cell AMPA conductances to 0.3 nS and provided mutual inhibition with

a 0.1 nS basket cell GABA conductance, These resulted in similar background

firing rates, but less robust wave propagation. However, this resulted in

a better match of the EPSC spectra to experiment. Clearly, there is a lot

of parameter space to explore with this small set of parameters.

Wave propagation is strongly affected by the axonal conductance delay

(the inverse of conduction velocity). How much less than 12.5 sec/m

can the network have and still support wave propagation?

The frequency and strength (gmax) of the random background activation can

be varied. The results shown in the poster used zero background

activation, because it was difficult to observe the response to

the input in the presence of strong backgrond activity. Studies

of propagation in the presence of background should be done.

Parameters for the thalamic input can be varied either by setting

the Ex\_cell and Inh\_cell gmax for thalamic input, or by setting

parameters for individual inputs. What is the effect of increasing

or decreasing the strength of the thalamic input?

There is much yet to be explored regarding the interaction of nearby

cells that receive auditory inputs. Do these parameters allow lateral

inhibition of closely spaced tones?

As described on the section ‘The input model’, the ‘Input delay’ and ‘Input

jitter’ values can produce a more realistic variation in the input received

by the cells within a row.

**Changes by editing the option strings in the main script**

There are many global parameters that are used for the initial setup

of the network, and that cannot be changed from the GUI. The section

‘Overview of the simulation scripts’ below describes the option strings

and global variables that can be changed.

Of course, any serious use of the network for auditory modeling should

extend the length of the network along the y-axis (the tonotopic axis)

to cover a wider range of octaves.

The time constants tau1 and tau2 for the synaptic conductances have a major

effect on the balance of excitation and inhibition, the default values are

based on reasonable estimates from available data, but there is some

uncertainty in the time constants for the rise and decay of inhibition.

(Because of a bug in hsolve, these cannot be changed from the GUI after

the network has been set up.)

How much of the results are due to the particular set of probabalistically

assigned connections? The random number seed is set to a fixed value in the

main script. This allows you to reproduce these results, but you will

certainly want to change it in order to compare multiple runs with

different connections.

**Providing more realistic auditory inputs**

The section on ‘The input model’ describes the ‘spike\_jitter’ parameter, in

addition to the GUI-settable ‘Input delay’ and ‘Input jitter’ values. This

parameter can be set in order to see if fluctuations in spike arrival time

affect the network behavior in a significant way.

The restriction that the thalamic inputs do not extend beyond cells on the

‘target row’ was made in order to simplify the analysis of wave

propagation. It would be more reasonable to extend the range with

the parameter ‘input\_spread’. This will probably require modifications

in the thalamic drive weight and some rebalancing of the gmax for

the four types of connections between cells.

The assumption that the output of thalamic cells is a uniform spike train

at the frequency of the sound source is probably a good one for low

frequency ‘click trains’, but not for tones of 220 or 440 Hz. In order to

provide a more realistic distribution of input spike train intervals, one

might develop a more detailed thalamic (MGBv) cell model for inputs to the

network. Some early work by Roullier et al. (1979) on unanethesized cats

provides histograms of spike time distributions. A simpler alternative is

to experiment with the alternate option string input\_type =

“pulsed\_randomspike”. Can you see any significant differences?

The options for ‘input\_pattern’ give an opportunity to explore other

non-auditory inputs. Customizing these will involve changes to some

parameters in the script ‘simple\_inputs.g’. For example, the “box” input

pattern can be used to study a spiking neurion model analgous to the model

of Rule et al. (2011) to simulate flicker phospenes in visual cortex.

The poster also describes experiments that can be performed on the model

to study the power spectra of responses to 20 - 40 Hz click trains.

**Speeding up the connection setup**

The definition of **function planarconnect\_probdecay** shows how the

algorithm is impelmented. It is similar to what is used in the C code for

planarconnect2, defined in the GENESIS source file

**genesis/src/newconn/connect.c**. The GENESIS Reference Manual in the

‘UGTD’ or the help files in **genesis/Doc** give detailed instructions on

implementing new commands and objectsi in GENESIS. This could be a fairly

simple project for someone with some experience in hacking other people’s C

code.

**Adding synaptic plasticity** (suitable for an advanced project)

The most serious feature lacking in the model is any form of synaptic

plasticity. Spike Timing Dependent Plasticity (STDP) and other forms of

plasticity are important effects in auditory and other cortical areas. It

would be a productive next step in the evolution of the ACnet2 model to

incorporate a phenomenological STDP model such as the one by Song, Miller

and Abbott (2000). It is perhaps better to understand the effect of these

mechanisms on this simple two population network before extending the

network model to include connections to other layers or other types of

neurons.

Implementing STDP will require some hacking of the GENESIS 2.3 source code

and/or modifications (and bug fixes) to the contributed stdpSynchan library

from the ‘Libraries’ collection at \`http://genesis-sim.org

<http://genesis-sim.org>\`*. Also see the GENESIS documentation for

‘Creating New Synaptic Objects’. Simpler methods of adding plasticity

would be to use the GENESIS ‘hebbsynchan’ or ‘facsynchan’ objects. These

have the restriction that they cannot be used with the ‘hsolve’ object, and

cannot take advantage of its efficient delivery of spike events.

The simulation scripts

———————-

**ACnet2-main.g**, the main simulation script, directly or indirectly includes:

**pyr\_4\_asym.p** - The cell parameter file that describes the excitatory

pyramidal cells in the network.

**pyr\_4\_sym.p** - This file is not used in the scripts, but is offered for

those who may want to implement the pyramidal cell model with symmetric

compartments instead of asymmetric compartments.

**bask.p** - The cell parameter file that describes the inhibitory basket

cells in the network.

**protodefs.g** - Defines a library of components that are used to create the cells

**pyrchans.g** - Defines the functions that create the pyramidal cell channels.

**FSchans.g** - Defines the functions that create the basket cell channels.

**synapseinfo.g** - Inclusion of this file is optional. Comments in the file

tell how to use the function **synapse\_info\* to get information

about the connections in a network.

**graphics2-5.g\* - Defines functions to create the main GUI of

control panel and graphs.

**input\_graphics2-5.g** - Provides an additional GUI for setting the

array of inputs to the model.

**simple\_inputs.g** - This defines the two functions **make\_inputs** and

**connect\_inputs** that are used by the main script. **make\_inputs** creates an

array of “cells” that mimic the output of auditory thalamic cells .

**connect\_inputs** connects the array of of inputs to the network cells.

The simplified **ACnet2-batch.g** script does not include the graphics files,

and includes a simplified **basic\_inputs.g** script instead of

**simple\_inputs.g**.

Overview of the simulation scripts

———————————-

Here are some notes to help you make changes to the default simulation

scripts, either by simple edits to the option strings and global variables,

or by modifications to the commands and functions that are described

below. If you would like to make significant changes, and are new to

GENESIS SLI script hacking, it would be useful to look through the \`GENESIS

Modeling Tutorial

<http://genesis-sim.org/GENESIS/UGTD/Tutorials/genprog/genprog.html>\`*

from the GENESIS web site. The section ‘Creating large networks with

GENESIS’ provides a tutorial on the construction of a simpler model

‘RSnet’, and explains the use of the GENESIS commands for creating networks

and setting synaptic weights and delays. For the complete set of tutorials

with source code for GENESIS 2.3 and PGENESIS, download the entire package

(about 50 MB) from \`http://genesis-sim.org/GENESIS/UGTD.html

<http://genesis-sim.org/GENESIS/UGTD.html>\`\_

The main script ACnet2-main.g is profusely commented, and contains

citations for the sources of most parameter values. The script

(and ACnet2-batch.g, with fewer options) begins with the definitions

of the simulation and output time steps and default run time (1 sec),

and a ‘RUNID" string that will be used in the names of the

several simulation output files that are generated.

then it defines some flags to control which options are used. The

more useful ones define the output of the simulation::

 int batch = 0 // if (batch) run the default simulation without graphics

 int graphics = 1 // display control panel, graphs, optionally net view

 int netview = 0 // show network activity view (slow, but pretty)

 int netview\_output = 1 // Record network output (soma Vm) to a file

 int binary\_file = 1 // if 0, use asc\_file to produce ascii output

 // else use disk\_out to produce binary FMT1 file

 int write\_asc\_header = 1 // write header information to ascii file

 int EPSC\_output = 1 // output Ex\_ex EPS currents to file

 int calc\_EPSCsum = 1 // calculate summed excitatory post-synaptic currents

With these defaults, the GUI implemented in graphics2-5.g and

input\_graphics2-5.g will be used to control the simulation and

display results. The network activity view will not be displayed,

but the data to generate the display will be output in binary format.

These will be the soma Vm of all the cells and the net Excitatory Post

Synaptic Current (EPSC) that each pyramidal cell “apical3/AMPA\_pyr”

channel receives from other pyramidal cells.

These determine the type of connection to be used::

 int use\_weight\_decay = 0 // Use exponential decay of weights with distance

 int use\_prob\_decay = 1 // Use connection probablility exp(~~r\*r/)

 int connect\_network = 1 // Set to 0 for testing with unconnected cells

Some network models use a constant connection probability over some

region , and then compensate by weighting more

distant connections with a lesser weight factor. This model

use the more realistic assumptions that the effectiveness

of synaptic connections does not appreciably decrease with

distance, but connection probability follows a roughly Gaussian

decay with distance.

You will likely not want to change these::

 int hflag = 1 // use hsolve if hflag = 1

 int hsolve\_chanmode = 4 // chanmode to use if hflag != 0

 int use\_sprng = 1 // Use SPRNG random number generator, rather than default

Without hsolve, the simulation will run approximately ten times slower.

However, you will not be able to use hsolve if your cell model contains any

custom objects or others that are not hsolveable. The default GENESIS 2

random number generator does not give an unbiased distribution, and it is

better to use the SPRNG random number generator.

The next definitions describe the inputs to the model::

 str input\_type = “pulsed\_spiketrain” // pulsed spike train input

 // str input\_type = “pulsed\_randomspike” // pulsed input from a randomspike

 // str input\_type = “MGBv” // input from simulated thalamic cells

 str input\_pattern = “row” // input goes to an entire row of cells

 // str input\_pattern = “line” // input to specified line of cells within row

 // str input\_pattern = “box” // input to a square block of cells

The sequence of spikes generated depends on the string ’input\_type’, which

can be either “pulsed\_spiketrain” or “pulsed\_randomspike”. The pattern of

connections from a member of the “input cell” array to the network cells is

determined by string ’input\_pattern’. These options and the ones further

below for input\_delay, input\_jitter, spike\_jitter, and input\_spread have

been described above in the section ’The input model’.

The line::

 int seed = 1369497795

guarantees that the same set of connections and inputs will be used each

time the simulation is run, and to produce the results shown in Beeman

. Change it to 0 for new random numbers each time, or to another

seed value to explore other configurations.

This is followed by some strings that specify the cell parameter files and

names for the Ex\_cells and Inh\_cells, and paths to the synaptic

conductances for the various locations for synaptic input. It is a simple

matter to plug a different set of cell models into this network by changing

these. Next come the definitions for the size and spacing of the two grids

of cells::

 int Ex\_NX = 48; int Ex\_NY = 48

 int Inh\_NX = 24; int Inh\_NY = 24

 float Ex\_SEP\_X = 40e-6

 float Ex\_SEP\_Y = 40e-6

 float Inh\_SEP\_X = 2\*Ex\_SEP\_X // There are 1/4 as many inihibitory neurons

 float Inh\_SEP\_Y = 2\*Ex\_SEP\_Y

and default values of the synaptic conductances and their time constants.

After definitions of many functions for setting paramwter values come the important functions

for setting up the network. The definition of::

 function planarconnect\_probdecay

is worth studying, as it can be modified to change the distribution and

probabilities of connections beteen cells. It is indirectly called by function

**connect\_cells**, which sets these arguments to **planarconnect\_probdecay**.

This is followed by other functions to set weights and delays.

The functions for file output of results are worth studying to understand

the format of the files, and to see how ’findsolvefield’ is used with

’hsolve’ to access data values.

The ’Main simulation section’ creates, the cell prototypes, creates the

unconnected network of Ex\_cells and Inh\_cells, and sets a number of

paramters. The statements following ’if’ are a guide to using

hsolve with a network simulation. Note the network is created first, a

solver is created for only on cell of each type, and then duplicated for

the other cells. There is more information on tricks with hsolve in the

hsolve documentation and in the advanced tutorial \`Simulations with GENESIS

using hsolve <http//:genesis-sim.org/GENESIS/UGTD/Tutorials/advanced-tutorials>\`\_.

After ’simple\_inputs.g’ is included, the functions **make\_inputs** and

**connect\_inputs** are called. Study this file if you would like to explore

alternate input models. Note that hsolve allows you to create connections

and change weights and delays **after** hsolve has been set up.

After some more parameter setting and optional inclusion of the graphics

GUIs and outputs, the program ends with::

 if

 // set up the inputs for the default simulation with two inputs:

 // Row 12~~ 220Hz, Row 36 - 440 Hz

 set\_frequency 0.0 // No background excitation

 setfield /MGBv[5]/spikepulse level1 1.0

 setfield /MGBv[5]/spikepulse/spike abs\_refract {1.0/220.0}

 setfield /MGBv[29]/spikepulse level1 1.0

 setfield /MGBv[29]/spikepulse/spike abs\_refract {1.0/440.0}

 reset

 reset

 step\_tmax

 end

To run the simulation in batch mode, set the options strings::

 int batch = 1

 int graphics = 0

You may set the ’binary\_file’ flag as you like, and edit the ‘setfield’

commands above to produce the desired input.

References

———-

Beeman D (2013) A modeling study of cortical waves in primary auditory

cortex. BMC Neuroscience, 14(Suppl 1):P23 doi:10.1186/1471-2202-14-S1-P23

(\`http://www.biomedcentral.com/1471-2202/14/S1/P23

<http://www.biomedcentral.com/1471-2202/14/S1/P23>\`\_.)

Bower JM, Beeman D (1998, 2003) The Book of GENESIS: Exploring Realistic

Neural Models with the General NEural SImulation System, second edn.

Springer-Verlag, New York. Free internet edition available at:

http://genesis-sim.org/GENESIS/iBoG/iBoGpdf

Bush PC, Sejnowski TJ (1993) Reduced compartmental models of neocortical

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Steriade M, Timofeev I, Grenier F (2001) Natural Waking and Sleep States: A

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