Striatal GABAergic microcircuit, dopamine-modulated cell assemblies (Humphries et al. 2009)

 Download zip file 
Help downloading and running models
To begin identifying potential dynamically-defined computational elements within the striatum, we constructed a new three-dimensional model of the striatal microcircuit's connectivity, and instantiated this with our dopamine-modulated neuron models of the MSNs and FSIs. A new model of gap junctions between the FSIs was introduced and tuned to experimental data. We introduced a novel multiple spike-train analysis method, and apply this to the outputs of the model to find groups of synchronised neurons at multiple time-scales. We found that, with realistic in vivo background input, small assemblies of synchronised MSNs spontaneously appeared, consistent with experimental observations, and that the number of assemblies and the time-scale of synchronisation was strongly dependent on the simulated concentration of dopamine. We also showed that feed-forward inhibition from the FSIs counter-intuitively increases the firing rate of the MSNs.
1 . Humphries MD, Wood R, Gurney K (2009) Dopamine-modulated dynamic cell assemblies generated by the GABAergic striatal microcircuit. Neural Netw 22:1174-88 [PubMed]
Citations  Citation Browser
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:
Cell Type(s): Neostriatum fast spiking interneuron;
Gap Junctions: Gap junctions;
Receptor(s): D1; D2; GabaA; AMPA; NMDA; Dopaminergic Receptor;
Transmitter(s): Dopamine; Gaba; Glutamate;
Simulation Environment: MATLAB;
Model Concept(s): Activity Patterns; Temporal Pattern Generation; Synchronization; Spatio-temporal Activity Patterns; Parkinson's; Action Selection/Decision Making; Connectivity matrix;
Implementer(s): Humphries, Mark D [m.d.humphries at]; Wood, Ric [ric.wood at];
Search NeuronDB for information about:  D1; D2; GabaA; AMPA; NMDA; Dopaminergic Receptor; Dopamine; Gaba; Glutamate;
function  [R,D] = reachdist(CIJ);

% input:  CIJ     = connection/adjacency matrix
% output: R       = reachability matrix
%         D       = distance matrix
%         strconn = true if entire graph is strongly connected
%         mulcomp = true if graph contains multiple components

% This function yields the reachability matrix and the distance matrix
% based on the power of the adjacency matrix - this will execute a lot
% faster for matrices with low average distance between vertices.
% Another way to get the reachability matrix and the distance matrix uses 
% breadth-first search (see 'breadthdist.m').  'reachdist' seems a 
% little faster most of the time.  However, 'breadthdist' uses less memory 
% in many cases...
% Written by Olaf Sporns, Indiana University, 2002
% Olaf Sporns, Indiana University 2006

% initialize
R = CIJ;
D = CIJ;
powr = 2;
N = size(CIJ,1);
connected = 0;
CIJpwr = CIJ;

% Check for vertices that have no incoming or outgoing connections.
% These are "ignored" by 'reachdist'.
id = sum(CIJ,1);       % indegree = column sum of CIJ
od = sum(CIJ,2)';      % outdegree = row sum of CIJ
id_0 = find(id==0);    % nothing goes in, so column(R) will be 0
od_0 = find(od==0);    % nothing comes out, so row(R) will be 0
% Use these columns and rows to check for reachability:
col = setxor(1:N,id_0);
row = setxor(1:N,od_0);

[R,D,powr] = reachdist2(CIJ,CIJpwr,R,D,N,powr,col,row);

% "invert" CIJdist to get distances
D = powr - D+1;

% Put 'Inf' if no path found
D(D==(N+2)) = Inf;
D(:,id_0) = Inf;
D(od_0,:) = Inf;


function  [R,D,powr] = reachdist2(CIJ,CIJpwr,R,D,N,powr,col,row)

% Written by Olaf Sporns, Indiana University, 2002

CIJpwr = CIJpwr*CIJ;
R = (R | ((CIJpwr)~=0));
D = D+R;

if ((powr<=N)&(~isempty(nonzeros(R(row,col)==0)))) 
   powr = powr+1;
   [R,D,powr] = reachdist2(CIJ,CIJpwr,R,D,N,powr,col,row);