Networks of spiking neurons: a review of tools and strategies (Brette et al. 2007)

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This package provides a series of codes that simulate networks of spiking neurons (excitatory and inhibitory, integrate-and-fire or Hodgkin-Huxley type, current-based or conductance-based synapses; some of them are event-based). The same networks are implemented in different simulators (NEURON, GENESIS, NEST, NCS, CSIM, XPP, SPLIT, MVAspike; there is also a couple of implementations in SciLab and C++). The codes included in this package are benchmark simulations; see the associated review paper (Brette et al. 2007). The main goal is to provide a series of benchmark simulations of networks of spiking neurons, and demonstrate how these are implemented in the different simulators overviewed in the paper. See also details in the enclosed file Appendix2.pdf, which describes these different benchmarks. Some of these benchmarks were based on the Vogels-Abbott model (Vogels TP and Abbott LF 2005).
1 . Vogels TP, Abbott LF (2005) Signal propagation and logic gating in networks of integrate-and-fire neurons. J Neurosci 25:10786-95 [PubMed]
2 . Brette R, Rudolph M, Carnevale T, Hines M, Beeman D, Bower JM, Diesmann M, Morrison A, Goodman PH, Harris FC, Zirpe M, Natschl├Ąger T, Pecevski D, Ermentrout B, Djurfeldt M, Lansner A, Rochel O, Vieville T, Muller E, Davison AP, El Boustani S, Destexhe A (2007) Simulation of networks of spiking neurons: a review of tools and strategies. J Comput Neurosci 23:349-98 [PubMed]
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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:
Cell Type(s): Abstract integrate-and-fire leaky neuron;
Gap Junctions:
Simulation Environment: NEURON; GENESIS; NEST; C or C++ program; XPP; CSIM; NCS; SPLIT; MVASpike; SciLab; Brian; PyNN; Python;
Model Concept(s): Activity Patterns; Methods;
Implementer(s): Carnevale, Ted [Ted.Carnevale at]; Hines, Michael [Michael.Hines at]; Davison, Andrew [Andrew.Davison at]; Destexhe, Alain [Destexhe at]; Ermentrout, Bard []; Brette R; Bower, James; Beeman, Dave; Diesmann M; Morrison A ; Goodman PH; Harris Jr, FC; Zirpe M ; Natschlager T ; Pecevski D ; Djurfeldt M; Lansner, Anders [ala at]; Rochel O ; Vieville T ; Muller E ; El Boustani, Sami [elboustani at]; Rudolph M ;
%  CSIM implementation of a benchmark simulation described in the paper
%  "Simulation of networks of spiking neurons: A review of tools and strategies"
%  using the "Circuit Tools" available from
%  Benchmark 3: Conductance-based HH network. This benchmark consists of a 
%               network of HH neurons connected with conductance-based synapses.
%  The "Circuit Tools" and CSIM are freely available from
%  Authors: Dejan Pecevski,
%           Thomas Natschlaeger,
%  Date: April 2006

close all; clear csim; 

% Global parameter values
ConnP           = 0.02;   % connectivity probability
Tsim            = 0.4;    % duration of the simulation [sec]
DTsim           = 0.1e-3; % simulation time step [sec]
Tinp            = 50e-3;  % length of the initial stimulus [sec]
nInputNeurons   = 10 ;    % number of neurons which provide initial input (for a time span of Tinp)
inpConnP        = 0.01 ;  % connectivity from input neurons to network neurons
inputFiringRate = 80;     % firing rate of the input neurons during the initial input

% initialize an empty neural microcircuit object
nmc = neural_microcircuit('dt_sim', DTsim);

% Add a pool of Traubs HH Neurons to the circuit 
[nmc, pool] = add(nmc, 'pool', 'type', 'TraubsHHNeuron', ...
                  'size', [20 20 10], 'origin', [20 1 1], 'frac_EXC', 0.8, ...
                  'Neuron.Cm', 2e-10, 'Neuron.Rm', 100e6, 'Neuron.Vresting', -60e-3, 'Neuron.Vinit', -60e-3, ...
                  'Neuron.Vthresh', -50e-3, 'Neuron.doReset', 0, 'Neuron.Trefract', 5e-3, ...
                  'Neuron.Iinject', [0 0] ) ;

% Create the connections in the network
[nmc, cn] = add( nmc, 'Conn', 'dest', pool, 'src', pool, 'type', ...                    % connect pool with itself
                 'StaticSpikingCbSynapse', 'lambda', Inf, 'C', ConnP * ones(1,4), ...   % connectivity does not depend on distance
                 'SH_W', 0, 'SH_delay', 0, 'rescale', 0, 'constW', 0, ...               % no synaptic heterogeneity (SH)
                 'Synapse.delay', 0, ...                                                % no transmission delay
                 'Synapse([EE IE]).W',  6e-9, 'Synapse([EE IE]).E',   0e-3, 'Synapse([EE IE]).tau',  5e-3, ... % excitatory synapses
                 'Synapse([EI II]).W', 67e-9, 'Synapse([EI II]).E', -80e-3, 'Synapse([EI II]).tau', 10e-3 );   % inhibitory synapses

% Create the input neurons for the inital stimulation
[nmc, inp] = add(nmc, 'pool', 'origin', [1 nInputNeurons 1], 'size', [1 nInputNeurons 1], ...
                      'type', 'SpikingInputNeuron', 'frac_EXC', 1);

% Connect the input neurons to the network
[nmc, cinp] = add( nmc, 'Conn', 'src', inp, 'dest', pool, ...
                   'type', 'StaticSpikingCbSynapse', 'lambda', Inf, 'C', inpConnP*ones(1,4), ...
                   'SH_W', 0, 'SH_delay', 0, 'rescale', 0, 'constW', 0, ...
                   'Synapse.W', 6e-9, 'Synapse.E', 0, 'Synapse.tau', 5e-3, 'Synapse.delay', 0);

% Create the stimulus
S = generate( constant_rate('nChannels', nInputNeurons, 'f', inputFiringRate, 'Tstim', Tinp) );

% Record the spikes of some random neurons
nmc = record(nmc, 'Volume', [20 1 1 ; 30 20 1 ], 'Field', 'spikes', 'dt', DTsim);

% Record also the membrane potential of two neurons
nmc = record(nmc, 'Volume', [30 10 5 ; 30 10 5], 'Field', 'Vm', 'dt', DTsim);
nmc = record(nmc, 'Volume', [25 15 5 ; 25 15 5], 'Field', 'Vm', 'dt', DTsim);

% Simulate the network
tic; fprintf('Running simulation: ');
R = simulate(nmc, Tsim, S);
fprintf('Done. %gsec CPU time for %gms simulation time\n', round(toc), Tsim*1000 );

% Finally make some plots.
% Note that plot_response is part of the circuit tools