ACnet23 primary auditory cortex model (Beeman et al 2019)

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These scripts were used to model a patch of layer 2/3 primary auditory cortex, making use of the the improvements to PGENESIS by Crone, et al. (2019). This single layer model contains a 48 x 48 grid of pyramidal cells (PCs) and a 24 x 24 grid of basket cells (BCs). The reduced PC models have 17 compartments with dimensions and passive properties that were fit to human cortical PC reconstructions. This parallel version of the simulation was used by Beeman, et al. (2019) to understand the effects of inhibition of PCs by BCs on auditory evoked potentials.
1 . Beeman D, Yu A, Crone J (2019) Studying evoked potentials in large cortical networks with PGENESIS 2.4 BMC Neuroscience 20 Suppl 1:P46
2 . Crone JC, Vindiola MM, Yu AB, Boothe DL, Beeman D, Oie KS, Franaszczuk PJ (2019) Enabling Large-Scale Simulations With the GENESIS Neuronal Simulator Frontiers in Neuroinformatics 13:69
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Model Information (Click on a link to find other models with that property)
Model Type: Realistic Network;
Brain Region(s)/Organism: Auditory cortex;
Cell Type(s): Neocortex L2/3 pyramidal GLU cell; Neocortex fast spiking (FS) interneuron;
Gap Junctions:
Receptor(s): GabaA; AMPA;
Transmitter(s): Gaba; Glutamate;
Simulation Environment: PGENESIS; GENESIS;
Model Concept(s):
Implementer(s): Beeman, Dave;
Search NeuronDB for information about:  Neocortex L2/3 pyramidal GLU cell; GabaA; AMPA; Gaba; Glutamate;
README file for the parallel version of the ACnet23 network model

Model Description

These scripts were used to model a patch of layer 2/3 primary auditory
cortex, similar to that used in Kudela, et al. (2018). This single
layer model contains a 48 x 48 grid of pyramidal cells (PCs) and a 24
x 24 grid of basket cells (BCs).  The reduced PC models have 17
compartments with dimensions and passive properties that were fit to
human cortical PC reconstructions (Eyal, et al. 2016). This parallel
simulation was used to understand the effects on evoked potentials of
inhibition of PCs by BCs. It was desctibed in the CNS 2019 poster and
published paper:

Beeman D, Yu A, Crone J (2019) Studying evoked potentials in large cortical
networks with PGENESIS 2.4. BMC Neuroscience 2019, 20(Suppl 1):P46

The default simulation reproduces the results shown in Figure 1 of the paper.

Details of the original (Beeman, 2013) model can be found at

These scripts are for PGENESIS 2.4 (May 2019 official release
version), available from The
improvements in this new Parallel GENESIS version have been described by
Crone, et al. (2019).

The instructions here are for running the simulations with 'pgenesis'
invoked directly on a system with PGENESIS/GENESIS 2.4 installed.
Directions for running them through the Neuroscience Gateway Portal
are given further below.

The default main script divides the network into 24 horizontal slices
that run in parallel on 24 MPI simulation nodes, plus a control_node
for console messages and input to the model, and an output_node for
file I/O (asc_file and par_asc_file objects).

The fast GENESIS "hsolve" solver is used on each cell to provide
highly efficient spike distribution. This requires the use of two new
hsolve-compatible GENESIS 2.4 objects. Short term synaptic depression
or facilitation is provided with "facdep_rules2", and "efield_cells"
properly calculates the field potential from a list of cells, using
all transmembrane and capacitive currents.

Running the Simulation

The main script is 'par-ACnet23-6-4.g'. It should be run with the command:

    pgenesis -nox -nodes 26 par-ACnet23-6-4.g

This will step the simulation for 11.1 sec to produce 11 trials, with
the 15 msec input pulse at 1 sec intervals, and then exit. It has the
default settings "RUNID = 6404" and BC to PC inhibition gmax = 4 nS.
It will produce (among others) an output file "Im_field_6404.txt".

A plot of this file will show 11 traces of the simulated field
potential, as measured from a point 2 mm above the middle of network
row 16. The image "RUN_6404_Im.png" is from a plot of results from a
run of this simulation. The green trace in "Fig1-abstract.png" was
produced from an average of these 11 trials over the period from the
start of the pulse until 80 msec afterwards.

In order to generate the data shown in the figure for other values of
inhibitory gmax, you can add optional parameters for the RUNID and Ex_inh_gmax.
For example,

    pgenesis -nox -nodes 26 par-ACnet23-6-4.g 6410 10e-9

will generate data for the blue trace in the figure.

Analyzing the output

Two Python scripts have been included to supplement the collection of
analysis tools that are provided with GENESIS in genesis/Scripts/gpython-tools. - A general two-dimensional plotting program that can
overplot multi-column data from single or multiple files.  It also has
several command line options, including '-h' for help. It was used to produce
the plots used in 'Fig1-abstract.png ' and 'RUN_6404_Im.png'. You can use
any other plotting application to plot the simulation results. - calculates a trial averaged Local Field Potential
from time series data that is organized into 'trials' with an
identical stimulus applied during each trial. It takse two arguments:

The 'trial_param_file' contains a header with information about the
stimulus parameters. The header should be in the format:

    #RUNID  stim_delay stim_length trial_interval sample_length

This is followed by lines, with each one containing the trial start time and
the stimulus type:

    0 = none, 1 = preferred stimulus, 2 = oddball (infrequent) stimulus

A typical file name is in the form 'oddball_freqs_<RUNID>.txt'.

The 'time_data_file' typically contains some measure of Excitatory
Post-Synaptic Currents in the network over a run that has multiple
trials.  This typically contains Local Field Potentials recorded at a
distance from a network of cells. The average LFP(t) is calculated by
averaging over trials up to, but not including, the final entry in the

A typical file name is in the form 'Im_field_<RUNID>.txt'. outputs the LFP to be plotted for stimulus type 1
(the frequent stimulus) to a file 'LFP-out1_<time_data_file>'.
Likewise, it performs the calculation and outputs a file
'LFP-out2_<time_data_file>' if samples exist for stimulus 2.
In this simulation, there is no infrequent stimulus.

In order to average the results from RUNID 6404, give the command: oddball_freqs_6404.txt Im_field_6404.txt

After doing this for the other runs with 10, 6, and 3 nS inhibitory gmax,
you can plot the results shown in Fig1-abstract.png with (on one line): LFP-out1_Im_field_6410.txt LFP-out1_Im_field_6406.txt
LFP-out1_Im_field_6404.txt LFP-out1_Im_field_6403.txt
-t "RUN 6410 (10 nS) 6406 (6 nS) 6404 (4 nS) 6403 {3 nS)"

Use the zoom feature of, or your favorite plotting application to
select the region from 0.1 to 0.18 sec.

Running the simulation on the Neuroscience Gateway Portal

The home page for the Neuroscience Gateway Portal is

Anyone with a need to run neuroscience simulations on one of their
high performance supercomputers can apply for a free account by clicking on
"Register Account".

Instructions for submitting jobs through the portal can be accessed from the
home page from "Support / NSG Tutorial" and clicking on "PDF Tutorial".
It may be convenient to download and save the file "HowtousetheNSGportal.pdf"
for reference while you go through the steps of creating a folder for a new
project and defining tasks.

After you have received an account, login by clicking "Access NSG Portal".

Once you log in, follow the tutorial directions to create a new
folder, give it a label and a description. The "input data" that you
will be asked to upload in a zip file, which can be this zipped package
or your own variation.

Then, from the tasks tab, create a new task and provide the
information that is required. Maximum Hours to Run can be a conservative
0.1 hours. This small network typically takes a little over a minute to run.

The Main Input Filename can be par-ACnet23-6-4.g or a variant, with the two
optional parameters.

When it is time to select a tool, choose "PGENESIS on Comet (2.4)".

This simulation uses 26 processes (PGENESIS "nodes") to be run on 26
processor cores.  Comet has a maximum of 24 cores per node, so the
simulation is run on 2 Comet nodes with 13 cores per node. Use these
values when setting "Number of Nodes" and " Number of Cores per Node".

After you have clicked "Save and Run Task" you can monitor the
progress of your run in the queue in "View Task Output", or wait for
the automated email telling you that the task has completed. Then download
the output files for analysis.

Configuring/Modifying the Simulation

There are many flags for options and definitions of global variables
in the file 'constants3.g' that is included near the beginning of the
main script. These are described further below.

Some of the most important simulation parameters can be modified from
the defaults in a section of the main script, following the include of
'constants3.g'. This is the place where edits could be made to run the
simulation to produce the other three plots in the figure.

/* ============ Customizations for this run =========================
   Change these global variables from their default values before
   setting up the simulation.

For example:

    // Change the RUNID (and output file names) from the default
    RUNID = "6406"
    Ex_inh_gmax = 6.0e-9  // PC inhibition by BCs
    tmax = 11.1 // set an alternate  run time

This will generate the data for the orange trace in 'Fig1-abstract.png'.

Principal Flags and Parameters

Some of the more important flags for options and global variables are:

  str RUNID = "6404"  // default ID string used in output file names
  int debug_level = 0 // display additional information during setup
                      // Use a higher number for more verbosity
  int pdebug = 0      // print parallel debugging messages

The simulation will be run using the function 'do_batch':

int batch = 1        // if (batch) run the default simulation without graphics
int graphics = 0     // display control panel, graphs, optionally net view

These determine the type of output files to be created. All are
possible, but only the field potential plots are created in this

int netview_output = 0  // Record network output (soma Vm) to a file
int EPSC_output = 0  // output Ex_ex EPS currents to file
int calc_EPSCsum = 0 // calculate summed excitatory post-synaptic currents
int calc_LFP = 1     // calculate summed local field potentials from efield

These determine how many simulation nodes will be used. In thie case, there
will be 24 slices of the network, plus two more for input and output.

  int n_slices = 24   // each slice will do a horizontal slice of network

  control_node = 0 // This should be the terminal window
  graphics_node = 0  // If there is a GUI, put it in this node also
  output_node = n_slices + 1 // Use a separate node for output to files
  n_nodes = n_slices + 2

Changing the Type of Input

This simulation was originally designed to measure the different
responses of the network to a frequent tone and a different infrequent
"oddball" tone. For this study on the effects of inhibition, there was
no infrequent tone. Although the PS and BC excitatory synapses undergo
short term depression, this effect was not studied in the default
simulation configuration. This can be modified with the settings

  str input_paradigm = "oddball" // two inputs
  // str input1_prob = 0.82 // input 1 has 0.82 probablitiy, input 2 has 0.18
  str input1_prob = 1.0 // For this version, use 100% probability for input 1
  int use_stim_order = 0
The flag 'use_stim_order' determines the type of sequence to use with
input_paradigm = "oddball". If use_stim_order = 0, a random sequence
of frequent and infrequent tones will be used, with the probability
for input tone 1 to be given by 'input1_prob', typically 0.82. For a
repeated single tone only, use input1_prob = 1.0.

use_stim_order = 1 is a variation that uses non-random sequences for
the two tones, taken from a file {stim_order_file}.

use_stim_order = 2 produces an initial frequent tone, and then trains
of four frequent tones, followed by one infrequent tone.

use_stim_order = 3 produces trains of four frequent tones, followed by
two infrequent tones. These are useful for short test runs.

When calculating external field potentials with input1_prob < 1.0, it
is necessary to keep track of which input was used for a particular
time interval.  The simulation outputs a file
"oddball_freqs_<RUNID>.txt" with a header line and two columns that
list the start time of the trial and the input number 1 or 2.  For the
default run of this script, oddball_freqs_6404.txt contains only "1"
in the second column.

Changing the Layer Names

The names of the grids for layer23 cells used here and in the included
scripts are Ex_layer23 and Inh_layer23. In order to adapt the scripts
to model other cortical layers, these can be changed to use "layer4"
or any other layer name by changing the global variable

  // This global parameter is used to define layer names, such as
  // "/Ex_"@{default_layer} == "Ex_layer4"
  str default_layer = "layer23" // could be replaced by "layer4"

Files included

par-ACnet23-6-4.g - the main simulation script
par-netfuncs-5.g - defines functions for creating and connecting the network
par-ACnet23_funcs-6.g  - defines output and most other functions
par-LFPfuncs.g - functions for measuring field potentials
par-simple_inputs3.g - delivers regular or random pulsed spike trains to the network.
pyr_23_asym.p - cell parameter file for PCs
bask.p - cell parameter file for BCs
protodefs3-0.g - creates the prototype compartments and channels
pyrchans3.g - channel definitions for PCs
FSchans.g - channel definitions for fast spiking BCs
Stimulus_order_binary.txt - stimulus order file used in Kudela, et al. (2018) - 2D plotting application - requires matplotlib - calculates the trial averaged Local Field Potential

Fig1-abstract.png - Figure 1 from Beeman, et al. (2019)
RUN_6404_Im.png - plot of the LFP from a run with default parameters


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
(Model available from ModelDB, Accession number: 150678)

Crone JC, Vindiola MM, Yu AB, Boothe DL, Beeman D, Oie KS, Franaszczuk PJ (2019)
Enabling Large-Scale Simulations With the GENESIS Neuronal Simulator.
Front. Neuroinform. 13:69. doi: 10.3389/fninf.2019.00069
(Model available from ModelDB, Accession number: 260267)

Eyal G, Verhoog MB, Testa-Silva G, Deitcher Y, Lodder JC, Benavides-Piccione R,
Morales J, DeFelipe J, de Kock CP, Mansvelder HD, Segev I (2016)
Unique membrane properties and enhanced signal processing in human neocortical
neurons. Elife 5:e16553. doi:10.7554/eLife.16553
(Model available from ModelDB, Accession number: 195667)

Kudela P, Boatman-Reich D, Beeman D and Anderson WS (2018) Modeling
Neural Adaptation in Auditory Cortex. Front. Neural Circuits, 05 September 2018.