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

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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]
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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:
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 [h] = raster_plot(events,times,varargin)

% RASTER_PLOT raster plot
%   RASTER_PLOT(E,T) plots the spike events in E at corresponding times T as a raster plot, with one point per spike, one row
%   per event index (i.e. either per neuron or per sweep)
%   RASTER_PLOT(E,T,FLAG) where FLAG is:
%       'r': randomises the order in which the events are plotted - this is useful for removing any potentially arbitrary structure
%       imposed by the order of event indexing (e.g. in the BG models, the events are ordered by channel)
%       's': puts the rasterplot as the top window of a 2x1 subplot [put 'rs' to get both]
%   RASTER_PLOT(E,T,FLAG,STRING) adds the STRING as the title of the plot (put FLAG=[] to omit)
%   Returns the handle to the figure window
%   Mark Humphries 9/10/2009

new_events = events;
if nargin >= 3 & findstr(varargin{1},'r')
    % new_times = [];
    event_idxs = unique(events);               % array of indices
    rand_seq = randperm(length(event_idxs));
    map = event_idxs(rand_seq);                % array of indices to re-map to
    for loop=1:length(map)
        new_events(events==event_idxs(loop)) = map(loop);   % replace     

h = figure 
if nargin >= 3 & findstr(varargin{1},'s')
%axis([min(new_times) max(new_times) min(new_events) max(new_events)]);

if nargin==4