Striatal GABAergic microcircuit, spatial scales of dynamics (Humphries et al, 2010)

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Accession:137502
The main thrust of this paper was the development of the 3D anatomical network of the striatum's GABAergic microcircuit. We grew dendrite and axon models for the MSNs and FSIs and extracted probabilities for the presence of these neurites as a function of distance from the soma. From these, we found the probabilities of intersection between the neurites of two neurons given their inter-somatic distance, and used these to construct three-dimensional striatal networks. These networks were examined for their predictions for the distributions of the numbers and distances of connections for all the connections in the microcircuit. We then combined the neuron models from a previous model (Humphries et al, 2009; ModelDB ID: 128874) with the new anatomical model. We used this new complete striatal model to examine the impact of the anatomical network on the firing properties of the MSN and FSI populations, and to study the influence of all the inputs to one MSN within the network.
Reference:
1 . Humphries MD, Wood R, Gurney K (2010) Reconstructing the three-dimensional GABAergic microcircuit of the striatum. PLoS Comput Biol 6:e1001011 [PubMed]
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Model Information (Click on a link to find other models with that property)
Model Type: Realistic Network; Neuron or other electrically excitable cell;
Brain Region(s)/Organism: Striatum;
Cell Type(s): Neostriatum fast spiking interneuron;
Channel(s):
Gap Junctions: Gap junctions;
Receptor(s): D1; D2; GabaA; AMPA; NMDA;
Gene(s):
Transmitter(s): Dopamine; Gaba; Glutamate;
Simulation Environment: MATLAB;
Model Concept(s): Activity Patterns; Spatio-temporal Activity Patterns; Winner-take-all; Connectivity matrix;
Implementer(s): Humphries, Mark D [m.d.humphries at shef.ac.uk]; Wood, Ric [ric.wood at shef.ac.uk];
Search NeuronDB for information about:  D1; D2; GabaA; AMPA; NMDA; Dopamine; Gaba; Glutamate; 
% -------------------------------------------------------------------------
% All default parameters for the striatal network model
% -------------------------------------------------------------------------
function SIMPARAMS = StriatumNetworkParameters(varargin)

% pass in argument with full path and filename to load an exisiting
% network.

% set the path to the network building functions
addpath ./CreateNetwork/
blnLoad = 0;
if nargin >= 1 
    blnLoad = 1; 
    network_fname = varargin{1};
end

% network parameters
% SIMPARAMS.net.PhysicalDimensions = [1000 1000 1000];   % slice dimensions for making spatial inputs within series of shells...
SIMPARAMS.net.PhysicalDimensions = [500 500 500];   % slice dimensions for making spatial inputs within single sphere...
% SIMPARAMS.net.PhysicalDimensions = [250 250 250];   % slice dimensions
% SIMPARAMS.net.PhysicalDimensions = [181 181 181];   % slice dimensions to get ~500 MSNs...
% SIMPARAMS.net.PhysicalDimensions = [106 106 106];   % slice dimensions to get ~100 MSNs...
SIMPARAMS.net.CellsPerMillimeterCube = 84900;       % number of MSNs per mm^3
SIMPARAMS.net.FSpercentage = 1;                     % percentage of FS neurons (N_MS * FSpercentage/100)
SIMPARAMS.net.ConnectMethod = 'physical';

% Figures from PLoS Comp Biol paper v1...
% SIMPARAMS.net.ConnectMethod = 'random';     % either 'physical' or 'random'. 
% SIMPARAMS.net.random.targetN_msms =  422;   % target number of MSNS INPUT TO 1 MSN connections for this % FSI..
% SIMPARAMS.net.random.targetN_fsms =  7.88;   % target number of FSIS INPUT TO 1 MSN connections for this % FSI..
% SIMPARAMS.net.random.targetN_fsfs =  3.29;     % target number of FSIS INPUT TO 1 FSI connections for this % FSI..
% SIMPARAMS.net.random.targetN_fsgap =  2.04;  % target number of gap junctions per FSI for this % of FSIs

% % Figures to get Ponzi-like network
% SIMPARAMS.net.ConnectMethod = 'random';     % either 'physical' or 'random'. 
% %SIMPARAMS.net.random.targetN_msms =  265;   % target number of MSNS INPUT TO 1 MSN connections for 250-on-side PW network
% %SIMPARAMS.net.random.targetN_msms =  100;   % target number of MSNS INPUT TO 1 MSN connections for 181-on-side PW network
% SIMPARAMS.net.random.targetN_msms =  20;   % target number of MSNS INPUT TO 1 MSN connections for 106-on-side PW network (p=0.2)
% SIMPARAMS.net.random.targetN_fsms =  1;   % irrelevant as only MSNs are connected
% SIMPARAMS.net.random.targetN_fsfs =  1;     % irrelevant as only MSNs are connected
% SIMPARAMS.net.random.targetN_fsgap =  1;  % irrelevant as only MSNs are connected

% Use the PhysicalDimensions to get MS.N, FS.N, D1inds, D2inds, and the
% neuron positions MS_P, and FS_P. *** NOTE: was CellsPerMillimeterCube
% based on just MSNs, or all cell types? ***
[SIMPARAMS.net] = GetNeuronPositions(SIMPARAMS.net); 

% Assign neurons to the two default channels
SIMPARAMS.net.CHAN1_MS = int32(0:(round(SIMPARAMS.net.MS.N / 2)-1))';
SIMPARAMS.net.CHAN1_FS = int32(0:(round(SIMPARAMS.net.FS.N / 2)-1))';
SIMPARAMS.net.CHAN2_MS = int32(round(SIMPARAMS.net.MS.N / 2):(SIMPARAMS.net.MS.N-1))';
SIMPARAMS.net.CHAN2_FS = int32(round(SIMPARAMS.net.FS.N / 2):(SIMPARAMS.net.FS.N-1))';

% -------------------------------------------------------------------------
% neuron parameters
SIMPARAMS.physiology.MSparams = getMSparams(SIMPARAMS);   % get the parameters for the DA MSN model
SIMPARAMS.physiology.FSparams = getFSparams(SIMPARAMS);   % get the parameters for the DA FSI model 

SIMPARAMS.physiology.Eglu = 0;             % msec : double
SIMPARAMS.physiology.Egaba = -60;          % msec : double
SIMPARAMS.physiology.ts_glu_AMPA = 6;      % msec : double
SIMPARAMS.physiology.ts_glu_NMDA = 160;    % msec : double
SIMPARAMS.physiology.ts_gaba = 4;          % msec : double - use 50ms for Ponzi-like network
SIMPARAMS.physiology.tau_fsgap = 11;       % msec : double

SIMPARAMS.physiology.glu_ratio = 0.5;  % based on DA paper, which was based on Moyer et al  
SIMPARAMS.physiology.DA = 0.0; 

% set the physiological parameters for the intrastriatal pathways
SIMPARAMS.physiology.MS_MS.maxdelay = 2; % msec
SIMPARAMS.physiology.MS_MS.baseweight = 4.36;
SIMPARAMS.physiology.MS_MS.baseweightSD = 0;

SIMPARAMS.physiology.FS_MS.maxdelay = 2; % msec
SIMPARAMS.physiology.FS_MS.baseweight = (4.36 * 5);
SIMPARAMS.physiology.FS_MS.baseweightSD = 0;

SIMPARAMS.physiology.FS_FS.maxdelay = 2; % msec
SIMPARAMS.physiology.FS_FS.baseweight = (4.36 * 5);
SIMPARAMS.physiology.FS_FS.baseweightSD = 0;

SIMPARAMS.physiology.FS_gap.baseweight = (150 / 5);
SIMPARAMS.physiology.FS_gap.baseweightSD = 0;

% -------------------------------------------------------------------------
% Simulation parameters
SIMPARAMS.sim.RANDSEED = int32(12345);
SIMPARAMS.sim.logfname = 'logfile.log';

SIMPARAMS.sim.tstart = 0;           % msec : double
SIMPARAMS.sim.tfinal = 100;         % msec : double
SIMPARAMS.sim.dt = 0.1;            % msec : double

% load a network from previous file, or build a new one 
if blnLoad
    SIMPARAMS.net = LoadStriatumNetwork(network_fname);
else
    SIMPARAMS.net = BuildStriatumNetwork(SIMPARAMS.net, SIMPARAMS.physiology, SIMPARAMS.sim);
end

SIMPARAMS.sim.RecordChan_MS = int32(0:49)';

SIMPARAMS.sim.MSspikebuffer = int32(2000000); % int32
SIMPARAMS.sim.FSspikebuffer = int32(2000000); % int32

SIMPARAMS.sim.initVms = ones(SIMPARAMS.net.MS.N, 1) .*  SIMPARAMS.physiology.MSparams(:,2);        % mV
SIMPARAMS.sim.initUms = zeros(SIMPARAMS.net.MS.N, 1); 
SIMPARAMS.sim.initVfs = ones(SIMPARAMS.net.FS.N, 1) .*  SIMPARAMS.physiology.FSparams(:,2);        % mV
SIMPARAMS.sim.initUfs = zeros(SIMPARAMS.net.FS.N, 1); 
SIMPARAMS.sim.initVgapfs = ones(length(SIMPARAMS.net.Pgapfs),1) .* SIMPARAMS.physiology.MSparams(1,2); % mV

SIMPARAMS.sim.SpikeEventQue_MS = zeros(SIMPARAMS.net.MS.N, (SIMPARAMS.physiology.MS_MS.maxdelay / SIMPARAMS.sim.dt)+1);
SIMPARAMS.sim.SpikeEventQue_FS = zeros(SIMPARAMS.net.FS.N, (SIMPARAMS.physiology.FS_FS.maxdelay / SIMPARAMS.sim.dt)+1);

SIMPARAMS.sim.Iinj_MS = zeros(SIMPARAMS.net.MS.N,1); 
SIMPARAMS.sim.Iinj_FS = zeros(SIMPARAMS.net.FS.N,1);

% -------------------------------------------------------------------------
% unstructured cortical input
SIMPARAMS.input.CTX.r_MSSEG = ones(SIMPARAMS.net.MS.N,1) .* 1.9; 
SIMPARAMS.input.CTX.N_MSSEG = int32(ones(SIMPARAMS.net.MS.N,1) .* 250);
SIMPARAMS.input.CTX.alpha_MSSEG = ones(SIMPARAMS.net.MS.N,1) .* 0.0;
SIMPARAMS.input.CTX.r_FSSEG = ones(SIMPARAMS.net.FS.N,1) .* 1.9;
SIMPARAMS.input.CTX.N_FSSEG = int32(ones(SIMPARAMS.net.FS.N,1) .* 250);
SIMPARAMS.input.CTX.alpha_FSSEG = ones(SIMPARAMS.net.FS.N,1) .* 0.0;

% The selection experiment parameters
SIMPARAMS.input.Selection.Pt =  int32([-1] ./ SIMPARAMS.sim.dt); % time of change in msec, converted into iteration
SIMPARAMS.input.Selection.Pch = int32([1]'); % channel
SIMPARAMS.input.Selection.Phz = SIMPARAMS.input.CTX.r_MSSEG(1); % rate of spkgen

% parameters for the Nisenbaum cortical pulse experiments
SIMPARAMS.input.PULSE.P = zeros(SIMPARAMS.sim.tfinal.*100,1);
SIMPARAMS.input.PULSE.r_ctx = 0; 
SIMPARAMS.input.PULSE.Nctx_ms = 0; 
SIMPARAMS.input.PULSE.Nctx_fs = 0; 
SIMPARAMS.input.PULSE.ts_spks = 50; % msec

SIMPARAMS.input.PULSE.pulsetimes = [250 750 1250 1750 2250 2750 3250 3750 4250 4750];
SIMPARAMS.input.PULSE.ISI = 0;
SIMPARAMS.input.PULSE.firstpulse = 0;
SIMPARAMS.input.PULSE.secondpulse = SIMPARAMS.input.PULSE.firstpulse + SIMPARAMS.input.PULSE.ISI;
% -------------------------------------------------------------------------
% parameters for the MS neurons - from Humphries et al (2009) NN paper
% -------------------------------------------------------------------------
function MSparams = getMSparams(SIMPARAMS)
MSparams = ones(SIMPARAMS.net.MS.N,14);

MSparams(:,1) = 50;       % C
MSparams(:,2) = -80;      % vr... -66 is approx Ponzi cells...
MSparams(:,3) = -33.8;    % vt 
MSparams(:,4) = 0.05;     % a
MSparams(:,5) = -20;      % b
MSparams(:,6) = -55;      % c
MSparams(:,7) = 377;      % d
MSparams(:,8) = 40;       % vp
MSparams(:,9) = 1.14;     % k
MSparams(:,10) = -68.4;   % ED_ms
MSparams(:,11) = 0.03;    % alpha_ms
MSparams(SIMPARAMS.net.MS.D1inds,11) = MSparams(SIMPARAMS.net.MS.D1inds,11) .* 0.0;   % alpha_ms (D1)
MSparams(:,12) = 3.75;    % beta1_ms
MSparams(SIMPARAMS.net.MS.D2inds,12) = MSparams(SIMPARAMS.net.MS.D2inds,12) .* 0.0;   % beta1_ms (D2)
MSparams(:,13) = 0.156;   % beta2_ms
MSparams(SIMPARAMS.net.MS.D1inds,13) = MSparams(SIMPARAMS.net.MS.D1inds,13) .* 0.0;   % beta2_ms (D1)
MSparams(:,14) = 22.7;    % gDAms
SIMPAMS.MSparams(SIMPARAMS.net.MS.D2inds,14) = MSparams(SIMPARAMS.net.MS.D2inds,14) .* 0.0;   % gDA_ms (D2)

% -------------------------------------------------------------------------
% parameters for the FS neurons
% -------------------------------------------------------------------------
function FSparams = getFSparams(SIMPARAMS)
FSparams = ones(SIMPARAMS.net.FS.N,12); 

FSparams(:,1) = 80;       % C
FSparams(:,2) = -70;      % vr
FSparams(:,3) = -50;      % vt
FSparams(:,4) = 1;        % k
FSparams(:,5) = 0.2;      % a
FSparams(:,6) = 0.025;    % b
FSparams(:,7) = -60;      % c
FSparams(:,8) = 0.0;      % d
FSparams(:,9) = 25;       % vpeak
FSparams(:,10) = -55;     % vb
FSparams(:,11) = 0.1;     % eta
FSparams(:,12) = 0.625;   % epsilon

function network = LoadStriatumNetwork(fname)

load(fname);
if exist('SIMPARAMS');
    network = SIMPARAMS.net;
else
    network = net;
end