This directory contains the GENESIS implementations of the Vogels and Abbott (2005) model using single compartment neurons with Hodgkin-Huxley dynamics. This was used as a benchmark for neural simulators in the review by Brette et al. (2007), which gives further details of the benchmarks.
Two versions of the benchmark are provided here. The script dualexpVA-HHnet.g implements a version that uses a dual exponential form of the synaptic conductances, having a 2 msec rise time, with a 5 msec decay for excitatory connections and 10 msec for inhibitory connections. The script instcondVA-HHnet.g, is nearly the same, but it uses synaptic conductances with an instantaneous rise time, as specified by the benchmark.
Both versions are based on simple modifications of the RSnet.g example from the GENESIS Neural Modeling Tutorials (Beeman, 2005). The latest versions are available from http://www.genesis-sim.org. The tutorial "Creating large networks with GENESIS" analyzes this script in detail, and explains how to modify it for other network models.
These simulations implement a network of 3200 simplified neocortical neurons providing excitatory connections and 800 interneurons providing inhibitory connections, randomly interconnected with 2% probability. As specified by the benchmark, these are simple one compartment neurons with Hodgkin-Huxley sodium and potassium channels. The excitatory and inhibitory neurons are identical, and fire tonically without spike frequency adaptation. Comments in the scripts and the tutorial on RSnet explain how to modify the scripts to use more biologically realistic neuron models and how to modify the network connections. The benchmark specifies that there be no axonal propagation delays (nor even a location of neurons in space), but this implementation allows optional delays through the GUI provided by the graphics.g file.
To run the simulations, you must have the GENESIS simulator installed, and in your search path. You can download the latest GENESIS distribution from the GENESIS Web site (http://www.genesis-sim.org/GENESIS), with documentation and installation instructions. If you are using Linux, Mac OS/X, or Microsoft Windows with Cygwin (a UNIX-like environment for Windows), you may download the appropriate GENESIS binary distribution that includes a precompiled "genesis" executable file. This will save you the trouble of compiling GENESIS from the source code that is included with the GENESIS source distribution.
Open a console window in this directory, and type
The CONTROL PANEL that appears has a toggle button to turn current injection pulses on and off, and text fields to set parameters such as maximal synaptic conductances, synaptic weight scaling, and propagation delays, injection pulse characteristics, and the frequency of random Poisson-distributed synaptic activation.
The plots in the middle will show the membrane potentials of three excitatory neurons (0, 1536, and 1567), and inhibitory neuron 0. The netview displays at the right show the membrane potentials of the excitatory neurons (top) and inhibitory neurons (bottom). With no propagation delays, the positions of the neurons on the grid are irrelevant. Nevertheless, this two-dimensional representation of the network layers makes it easy to visualize the number of cells firing at any time during the simulation.
The first figure in the GENESIS section of the paper shows the results of running this simulation for 0.05 msec with a random background synaptic activation applied to all excitatory cells at a mean frequency of 70 Hz., removing the activation, and then running for another 4.95 seconds. This figure is included here as FigG1-dualexpVA-HH.gif. In order to produce the results shown in the figure:
The instantaneous rise in the synaptic conductance that is used in the Vogels-Abbott model and benchmark is a very efficient model to implement with a simulator specialized for IF networks, but such a non-biological conductance is not normally provided by GENESIS.
Fortunately, GENESIS has a simple procedure for adding user-contributed objects and commands to extend it. The user-contributed code for the "isynchan" object in the isynchan directory adds a new GENESIS object type called the "isynchan", which is used by the instcondVA-HHnet.g script and its associated scripts.
If you are using Linux, you may be able to run the executable binary "isynchan/igenesis", without compiling the isynchan code. Because of its size, it is not included here, but is contained with the original version on the GENESIS web site http://www.genesis-sim.org, under "Models" and at ModelDB as "GENESIS_HH_benchmark". If you have problems with libraries not being found, or are using another operating system, you will have to follow the directions in isynchan/README to create the igenesis executable.
Once you have a usable "igenesis" you can run the instcondVA-HHnet.g with the command,
and proceed as before to get the second figure (FigG2-instVA-HH.gif), which displays the Instantaneous VA HH Model simulation results, generated under the same conditions as the first figure.
Introducing propagation delays:
Turn on current injection by clicking on the the "Current Injection OFF" toggle so that it reads "Current Injection ON". With the default settings of the injection fields, this will apply a 50 msec pulse of 1 nA injection to the soma of the center cell of the excitatory cell grid.
The field labeled "Prop delay (sec/m)" is used to hold the reciprocal of the axonal conduction velocity. In the benchmark simulation, there is an infinite conduction velocity with no delays, so this delay field is set to zero. In this case, the propagation delays of all synapic connections are set to zero. For a non-zero delay, the the conduction velocity will be used to calculate delays based on radial distances to the target synapses.
Experiment with the effect of using different propagation delays, with short injection pulses to the center cell.
Using synapse_info to inspect connections:
You can explore the connections that are made by invoking the 'synapse_info' function at the genesis prompt. This function is defined, with further explanation, in the file synapseinfo.g, which is included by the main simulation script. For example, to see a list of all the synaptic connections to both the excitatory and inhibitory channels of the three excitatory neurons for which the membrane potential was plotted, give the commands to the genesis prompt:
synapse_info /Ex_layer/Ex_cell/soma/Ex_channel synapse_info /Ex_layer/Ex_cell/soma/Inh_channel synapse_info /Ex_layer/Ex_cell/soma/Ex_channel synapse_info /Ex_layer/Ex_cell/soma/Inh_channel synapse_info /Ex_layer/Ex_cell/soma/Ex_channel synapse_info /Ex_layer/Ex_cell/soma/Inh_channel
How do these results relate to the relative amount of spiking produced by these three cells?
You may alter the set of random numbers used (and therefore the connections produced) by starting GENESIS first, giving the "randseed" command to change the random number seed (to the system clock time, if no argument is given), and then load the simulation script, e.g.
% genesis (some startup messages)
genesis #0 > randseed
genesis #1 > dualexpVA-HHnet.g
The tutorial "Creating large networks with GENESIS" from the GENESIS Neural Modeling Tutorials explains how to modify scripts based on RSnet.g for other network models. Simple changes to the scripts can substitute different neuron models for the ones specified in the Ex_cellfile and Inh_cellfile strings. The arguments of the planarconnect and planardelay commands can be changed to provide probabilities of connections or propagation delays, that either depend on distance, or are assigned according to various probability distributions.
Here is a suggested project:
Bush and Sejnowski (1996) constructed a realistic model of a single column in layer V of the visual cortex. The model used 80 9-compartment pyramidal cells and 20 7-compartment fast spiking inhibitory basket cells, with random 10% probablility all-to-all connections. In spite of introduced random variations in cells, it fires synchronously when the pyramidal cells are given uncorrelated spike train inputs, with results similar to those observed by Gray et al. (1992).
Question: To what extent do these results depend on the details of the cell models and the network connections?
Modify the dualexpVA-HHnet.g script to produce a network similar to the one used by Bush and Sejnowski and try using the "VA_HHcell" used in this script, or more realistic cell models that are available with the GENESIS Modeling Tutorial. Experiment with different ways to specify the propagation delays, and with the GENESIS commands for introducing random variations in neuron passive parameters and positions.
The GENESIS Reference Manual section on Objects describes several "Device Objects" that can be used for spike train analysis:
autocorr Calculates histograms of auto-correlations crosscorr Calculates histograms of cross-correlations interspike Generates an interspike interval histogram peristim Generates a peristimulus spike histogram event_tofile Records event times; e.g spikes or threshold crossings
These objects can be used to generate histograms for analysis of spike trains produced by the network, similar to those in Fig. 2 of the Vogels and Abbott paper.
Beeman D (2005) GENESIS Modeling Tutorial. Brains, Minds, and Media. 1: bmm220 (urn:nbn:de:0009-3-2206). (http://www.brains-minds-media.org)
Brette R, Rudolph M, Carnevale T, Hines M, Beeman D, Bower JM, Diesmann M, Morrison A, Goodman PH, Harris Jr FC, Zirpe M, Natschlager T, Pecevski D, Ermentrout B, Djurfeldt M, Lansner A, Rochel O, Vieville T, Muller E, Davison AP, El Boustani S, and Destexhe A (2007) Simulation of networks of spiking neurons: a review of tools and strategies. J. Comput. Neurosci. 23: 349-398.
Bush P, Sejnowski T (1996) Inhibition synchronizes sparsely connected cortical neurons within and between columns in realistic network models. J. Comput. Neurosci. 3: 91-110.
Gray CM, Engel AK, Konig P, Singer W (1992) Synchronization of oscillatory neuronal responses in cat striate cortex: Temporal properties. Vis. Neurosci. 8: 337-347.
Vogels TP, Abbott LF. (2005) Signal propagation and logic gating in networks of integrate-and-fire neurons. J. Neurosci. 25: 10786-10795.