%%%% BASIC PARAMETERS SCRIPT - Mark Humphries 18/8/2005
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%%%% PARAMETERS THAT MOST OFTEN CHANGE %%%%%%%%%%%%%%%%%%%%%
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%%% random seed
rand('state',seed1); randn('state',seed1);
%%%% simulation time
time_seconds = 10; %total simulation time in seconds (usually 10)
do_urethane = 0;
%%%% dopamine
dop1 = 0.3; % tonic dopamine level (in [0 1] range) (0.3)
% same weight calculation as in systems level model
dop2 = 0.3;
%%%% inputs
% In the following, for
% selection xpt - 'vivo' (basic tonic with poisson stats)
% tonic rate calibration - 'vivo'
% (for tonic rates a rule of thumb is
% 30-30-12 for GPe-GPi-STN resp)
% slow wave xpt - 'slow' (anaesthetic-like correlated slow-wave, uses the fixed train+jitter model)
% organo xpt - 'organo' (correlated tonic, uses the fixed train+jitter model)
input_type = 'vivo';
switches = [0.2, 0.5, 1] .* time_seconds; % in seconds
% In the following, for
% selection xpt - 'switch' (switches salience at switch points using
% conventional 'salience grid' paradigm
% alternative is 'simultaneous' (switch
% both channels at same time)
% tonic rate calibration - 'tonic' (with 2-4Hz) - should probably set 'save-state' flag too
% slow wave xpt - 'tonic' (with 24Hz after Steriade)
% organo xpt - 'tonic' (rate?)
input_method = 'tonic';
tonic_rate = 15; % in Hz
rate_scaling = 0.2; % scale rates in input grid by this amount
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% specify basic network parameters
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n_nuclei = 5; %SD1, SD2, STN, GPe, GPi
n_channels = 3;
neurons_per_channel = 64;
neurons_per_nucleus = neurons_per_channel*n_channels;
n_neurons = n_nuclei*neurons_per_nucleus;
% specify connection proportion
p_connect = 0.25; % cf 'rho'
%specify nuclei indices
SD1 = 1:neurons_per_nucleus; %Striatal D1 neurons
SD2 = neurons_per_nucleus+1:2*neurons_per_nucleus; %Striatal D2 neurons
STN = 2*neurons_per_nucleus+1:3*neurons_per_nucleus; %Subthalamic neurons
GPe = 3*neurons_per_nucleus+1:4*neurons_per_nucleus; %Globus Pallidus internus
GPi = 4*neurons_per_nucleus+1:5*neurons_per_nucleus; %Globus Pallidus externus
EXT = 5*neurons_per_nucleus+1:6*neurons_per_nucleus; %Extrinsic input
trace_n = GPe(75); % index of neuron to record in detail membrane potential, epsp, J,...
% (number has to be less than No neurosn per nucleus)
%specify time parameters
dt = 0.0001; %time steps in seconds
% other neuron parameters
sigma_bg = 0.0003; %noise std dev - in V in solution of membrane eqn.
ref = 0; %refractory membrane reset potential (in V) - (V_reset)
ref_period = 0.002; %Absolute refractory period in seconds
theta = ones(n_neurons,1)* 0.03; %thresholds given in V above resting potential
theta(STN) = 0.02; % STN has lower firing threshold
mlimit = ones(n_neurons,1) * -0.02; % limiting threshold below which membrane potential can't go:
% mimics limiting effect of GABA reversal potential
%time constants - one per neuron
mean_tau_AMPA = 0.002; % AMPA component - 2ms excitatory current % 0.002
mean_tau_NMDA = 0.100; % NMDA component - 120ms excitatory current
mean_tau_GABAa = 0.003; % GABAa - 2ms inhibitory current
mean_tau_m = {0.025, ... % SD1
0.025, ... % SD2
0.006, ... % STN
0.014, ... % GPe
0.008}; % GPi/SNr 0.008
% adding noise to the time membrane constants
% all nuclei have gaussian dist. except STN which is gamma (outside
% parameter control block)
std_tau_m = cell2mat(mean_tau_m) .* 0.1; % 10% std dev
% variable resistances
mean_R = {42e6, ... % SD1
42e6, ... % SD2
18e6, ... % STN
88e6, ... % GPe
112e6}; % GPi/SNr
std_R = cell2mat(mean_R) .* 0.1; % 10% std dev
% weights and associated parameters
% the factors w_ij / tau_s are calculated to give currents that yield PSPs
% of measured size. These are then *relative* weighting factors multiplied
% by w_ij / tau_s
SD1_w = -4; %-4;
SD2_w = -4; %-4;
STN_GPiw = 1.0; % 1
%%%%%%%%% STN GP loop %%%%%%
STN_GPew = 1.0; %1.5
GPe_STNw = -1.0; % (1) set to -2 to get consistent bursting
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GPe_GPiw = -1.0; %-1;
% omit collaterals here
GPe_GPew = -1; % -1 collaterals
GPi_GPiw = -1; % -1 collaterals
EXT_w = 1.0; % ctx input 1.0
STN_ext_ratio = 1.0; % cortico-subthalamic weight is EXT_w * STN_ext_ratio (1)
%%%%%%%%% do urethane manipulation %%%%%%%%%%%
scale = 1.0; %for historical reasons in code
if do_urethane
glut_scale = 0.65; % turn all glut wgts down (0.65? - KG)
gaba_scale = 1.5; % turn all gaba wgts up
else
glut_scale = 1.0;
gaba_scale = 1.0;
end
SD1_w = SD1_w .* gaba_scale;
SD2_w = SD2_w .* gaba_scale;
GPe_STNw = GPe_STNw .* gaba_scale;
GPe_GPiw = GPe_GPiw .* gaba_scale;
GPe_GPew = GPe_GPew .* gaba_scale;
GPi_GPiw = GPi_GPiw .* gaba_scale;
STN_GPiw = STN_GPiw .* glut_scale;
STN_GPew = STN_GPew .* glut_scale;
EXT_w = EXT_w .* glut_scale;
%scale = (1/neurons_per_channel * p_connect);
AMPA_PSP_size = 0.003; % max size in V (for all populations
NMDA_PSP_size = 0.0001; % (0.0001)
GABAa_PSP_size = 0.003;
%PSP_sigma = PSP_size .* 0.1; % noise std is 10% of PSP
PSP_sigma = 0.000; %0
% delays in seconds - axonal delays
SD12GPi_d = 0.004;
SD22GPe_d = 0.005;
STN2GPe_d = 0.002;
STN2GPi_d = 0.0015;
GPe2STN_d = 0.004; %0.004
GPe2GPi_d = 0.003;
GPe2GPe_d = 0.001;
GPi2GPi_d = 0.001;
EXT2SD1_d = 0.01;
EXT2SD2_d = 0.01;
EXT2STN_d = 0.0025;
% dopamine coefficients
stnda = [0.25 0.5 0]; % STN coefficients for proportion of dopamine that affects [AMPA GABAa Spon]
% currents (all < 1) (don't have to sum to 1)
% maybe spon = 0?
% nominally [0.25 0.5 0.1];
gpeda = [0.5 0.5]; % GPe coefficients for proportion of dopamine that affects [SD2 STN] input - NOT specific currents as above
%(don't have to sum to 1) nominally [0.5 0.5]
%stnda = [0 0 0];
%%%%% shunting inhibition (current size computed in main code)
% synaptic distributions [distal, prox, soma] - must sum to 1! (only
% required for inhibitory synapses)
SD12GPi_p = [1 0 0]; % distal only
SD22GPe_p = [0.33 0.34 0.33]; % distal only
GPe2STN_p = [0.3 0.4 0.3]; % [0.3 0.4 0.3];
GPe2GPi_p = [0 0.5 0.5];
GPe2GPe_p = [0 0.5 0.5];
GPi2GPi_p = [0 0.5 0.5];
max_scale = 0.5; % scale of maximum proximal and somatic inputs (beta in maths)
shunt_to = -0.02; % target membrane potential (in volts) of shunting current
% the value to which V is driven by I_shunt with
% maximal inhibition
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%%%% INTRINSIC CURRENTS %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
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%%%% spontaneous currents (limited by Heaviside in main code)
spon = zeros(n_neurons,1); % constant spontaneous input current for each neuron
%spon(STN) = randn(length(STN),1)*(1e-11) + (9e-11);
if do_urethane
spon(STN) = 5e-10; %11.0e-10 for awake
else
spon(STN) = 11e-10;
end
spon(GPe) = 3.8e-10; %4.6e-10
%spon(GPe) = randn(length(GPe),1)*(2e-11) + (4e-10);
%spon(GPi) = randn(length(GPi),1)*(2.5e-11) + (2.5e-10);
spon(GPi) = 3.9e-10; %3.2e-10
spon(SD1) = -2.5e-10; % -2.5e-10
spon(SD2) = -2.5e-10; % -2.5e-10
%% burst-current parameters
mean_t1 = 0.2; % seconds 0.1
std_t1 = 0.01;
mean_t2 = 1.0; % 0.5
std_t2 = 0.22; %~10% variance
mean_thetaCA = -0.01; % volts (below rest) -0.01
std_thetaCA = -0.001;
mean_alphaCA = 9e-10; % 9e-10. amperes - work out from membrane potential equation (e.g. set Hz=80)
% may need adjustmentin a circuit to set
% intra-burst freq = 80Hz (from Plenz and Kita)
std_alphaCA = 9e-11; % 9e-11
ca_cells = STN; % array of cells with burst pseudo-current
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%%%%%% INPUTS %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
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% current pulse
pulse_Hz = 0.0; % frequency of pulses
t_offset = 1; % start of pulse train (in seconds)
t_width = 0.5; % width of pulse (in seconds)
step_size = -1e-8; % in Amperes
pulse_cells = []; % array of cells receiving pulse trains
% state variable flags
load_state = 1; % if = 1, loads initial state from state.mat
save_state = 0; % if = 1, saves final state to state.mat