Basal ganglia-thalamocortical loop model of action selection (Humphries and Gurney 2002)

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We embed our basal ganglia model into a wider circuit containing the motor thalamocortical loop and thalamic reticular nucleus (TRN). Simulation of this extended model showed that the additions gave five main results which are desirable in a selection/switching mechanism. First, low salience actions (i.e. those with low urgency) could be selected. Second, the range of salience values over which actions could be switched between was increased. Third, the contrast between the selected and non-selected actions was enhanced via improved differentiation of outputs from the BG. Fourth, transient increases in the salience of a non-selected action were prevented from interrupting the ongoing action, unless the transient was of sufficient magnitude. Finally, the selection of the ongoing action persisted when a new closely matched salience action became active. The first result was facilitated by the thalamocortical loop; the rest were dependent on the presence of the TRN. Thus, we conclude that the results are consistent with these structures having clearly defined functions in action selection.
1 . Humphries MD (2003) High level modeling of dopamine mechanisms in striatal neurons Technical Report ABRG 3
2 . Humphries MD, Gurney KN (2002) The role of intra-thalamic and thalamocortical circuits in action selection. Network 13:131-56 [PubMed]
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Model Information (Click on a link to find other models with that property)
Model Type: Connectionist Network;
Brain Region(s)/Organism:
Cell Type(s):
Gap Junctions:
Transmitter(s): Dopamine;
Simulation Environment: MATLAB; Simulink;
Model Concept(s): Parkinson's; Action Selection/Decision Making;
Implementer(s): Humphries, Mark D [m.d.humphries at];
Search NeuronDB for information about:  Dopamine;
function output = DA_ramp_output(a,e,mI,da,type,gain,varargin)

%DA_RAMP_OUTPUT Dopamine affected unit output 
%   O = DA_RAMP_OUTPUT(A,E,M,DA,T,G,P) computes the output O of a LI unit layer,
%   given activation A, threshold E, and initial slope M arrays, dopamine level DA,
%   dopamine receptor type T (1 = D1, 2 = D2), and gain G. The pivot point
%   P is a optional parameter in that it only needs to be specified for D1
%   receptors.
%   Reference: Humphries, M.D. (2003). High level modeling of dopamine mechanisms 
%   in striatal neurons. Technical Report ABRG 3. Dept. Psychology
%   University of Sheffield, UK.
%   Mark Humphries 21/1/2005

% check for pivot
if type==1 & nargin < 7
    error('Must specify pivot parameter for D1 receptors')

%%% below is an optimised verison of this for arrays of activations...
% if a < e
%     output = 0;
% elseif a <= 1/m + e
%     output = m * (a - e);
% else 
%     output = 1;
% end

if type == 1    % D1 model
    p = varargin{1};
    m = mI + gain .* da; 
    % classify outputs
    limit = (1 - (1 - m) .* p) ./ m + e;
	case_zero = find(a < e);
	case_a = find(a >= e & a <= limit);
	case_one = find(a > limit);

	% compute outputs
	output(case_zero) = 0;
	output(case_a) = m .* (a(case_a) - e) + (1 - m) .* p;
	output(case_one) = 1;
elseif type == 2 % D2 model
    m = mI - gain .* da; 
    % case statement same as original ramp function
	case_zero = find(a < e);
	case_a = find(a >= e & a <= 1/m +e);
	case_one = find(a > 1/m + e);
	output(case_zero) = 0;
	output(case_a) = m .* (a(case_a) - e);
	output(case_one) = 1;
    error('Unknown dopamine receptor type specified')