%%%% BASIC PARAMETERS SCRIPT  Mark Humphries 9/1/2006
%%% Condition#4 of Magill et al. (2001) No Ctx, No DA. No Ca2+ in STN
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%% PARAMETERS THAT MOST OFTEN CHANGE %%%%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%% random seed
rand('state',seed1); randn('state',seed1);
%%%% simulation time
time_seconds = 10; %total simulation time in seconds (usually 10)
do_urethane = 1;
%%%% dopamine
dop1 = 0.0; % tonic dopamine level (in [0 1] range) (0.3)
% same weight calculation as in systems level model
dop2 = 0.0;
%%%% 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
% 303012 for GPeGPiSTN resp)
% slow wave xpt  'slow' (anaestheticlike correlated slowwave, uses the fixed train+jitter model)
% organo xpt  'organo' (correlated tonic, uses the fixed train+jitter model)
input_type = 'slow';
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 24Hz)  should probably set 'savestate' flag too
% slow wave xpt  'tonic' (with 24Hz after Steriade)
% organo xpt  'tonic' (rate?)
input_method = 'tonic';
tonic_rate = 32; % in Hz
rate_scaling = 0.2; % scale rates in input grid by this amount
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% specify basic network parameters
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
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
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
GPe_GPiw = 1.0; %1;
% omit collaterals here
GPe_GPew = 1; % 1 collaterals
GPi_GPiw = 1; % 1 collaterals
EXT_w = 0; % ctx input 1.0
STN_ext_ratio = 1.0; % corticosubthalamic 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
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%% INTRINSIC CURRENTS %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%% 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)*(1e11) + (9e11);
if do_urethane
spon(STN) = 5e10;
else
spon(STN) = 11e10; % 10.0e10; % 11e10 with GP collaterals @ 1
end
spon(GPe) = 3.8e10; %2.7e10; %4.6e10 with GP collaterals @ 1
%spon(GPe) = randn(length(GPe),1)*(2e11) + (4e10);
%spon(GPi) = randn(length(GPi),1)*(2.5e11) + (2.5e10);
spon(GPi) = 3.9e10; %2.8e10; %3.2e10 with GP and GPi collaterals @ 1
spon(SD1) = 2.5e10; % 2.5e10
spon(SD2) = 2.5e10; % 2.5e10
%% burstcurrent 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 = 9e10; % 9e10. amperes  work out from membrane potential equation (e.g. set Hz=80)
% may need adjustmentin a circuit to set
% intraburst freq = 80Hz (from Plenz and Kita)
std_alphaCA = 9e11; % 9e11
ca_cells = []; % array of cells with burst pseudocurrent
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%%% INPUTS %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% 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 = 1e8; % 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
