##### https://github.com/nwbarendregt/AdaptNormThresh

Tip revision:

**2878a3d9f5a3b9b89a0084a897bef3414e9de4a2**authored by**nwbarendregt**on**03 May 2022, 15:08:29 UTC****Add files via upload** Tip revision:

**2878a3d** SC_Threshold_Motifs.m

```
% SC_Threshold_Motifs.m
% Script used to generate data for Fig. 3C in Barendregt et al., 2022.
clear
% Define simulation parameters:
T = 5; dt = 0.005; t_i = 1;
dg = 0.001;
R = 5; c = @(t) 1;
% Define pre- and post-change rewards:
m_1 = linspace(1,10); m_2 = linspace(1,10);
% Pre-allocate threshold classification matrix and threshold storage:
type = NaN(length(m_1),length(m_2));
thresh = NaN(length(m_1),length(m_2),201);
for i = 1:length(m_1)
type_i = NaN(1,length(m_2));
thresh_i = NaN(length(m_2),201);
for j = 1:length(m_2)
% Construct SNR timeseries:
m = NaN(1,T/dt+1); m(1:100) = m_1(i); m(101:end) = m_2(j);
% Calculate normative thresholds using Bellman's equation:
theta = SC_Bellmans(T,dt,t_i,dg,m,c,R); theta = theta(1:201);
thresh_i(j,:) = theta;
% Classify threshold motif:
if sum(diff(theta)==0)==(length(theta)-1)
type_i(j) = 1; % Constant Threshold
elseif (sum(diff(theta)<=0)==(length(theta)-1))
type_i(j) = 2; % Decreasing
else
type_i(j) = 3; % Increasing
end
end
type(i,:) = type_i;
thresh(i,:,:) = thresh_i;
end
save('SC_Thresholds_Motifs.mat');
```