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;
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     
    end
end

h = figure 
if nargin >= 3 & findstr(varargin{1},'s')
    subplot(211)
end
plot(times,new_events,'k.')
min(new_events);
%axis([min(new_times) max(new_times) min(new_events) max(new_events)]);

if nargin==4
    title(varargin{2});
end
ylabel('event')
xlabel('time');