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- function [LH, probSpike, V, mean_predictedSpikes, RPE] = ott_RW_RPE_base_asymm_cue(startValues, spikeCounts, rewards, timeLocked, cueInfo)
- % cued experiment
- alphaPPE = startValues(1);
- alphaNPE = startValues(2);
- slope = startValues(3);
- intercept = startValues(4);
- V_sucCue = startValues(5);
- V_malCue = startValues(6);
- Vinit = alphaPPE / (alphaPPE + alphaNPE);
- trials = length(rewards);
- V = zeros(trials + 1, 1);
- RPE = zeros(trials, 1);
- rateParam = zeros(trials, 1);
- V(1) = Vinit;
- % Call learning rule
- for t = 1:trials
- RPE(t) = rewards(t) - V(t);
- if RPE(t) >= 0
- V(t + 1) = V(t) + alphaPPE*RPE(t);
- else
- V(t + 1) = V(t) + alphaNPE*RPE(t);
- end
-
- if cueInfo(t, 1) == 1 % sucrose cue
- rateParam(t) = exp(slope*RPE(t) + intercept + V_sucCue);
- elseif cueInfo(t, 2) == 1 % malto cue
- rateParam(t) = exp(slope*RPE(t) + intercept + V_malCue);
- elseif cueInfo(t, 3) == 1 % nonpredictive cue
- rateParam(t) = exp(slope*RPE(t) + intercept);
- else
- error('cueInfo is 0 for all columns\n');
- end
- end
- probSpike = poisspdf(spikeCounts, rateParam(timeLocked)); % mask rateParam to exclude trials where the animal didn't lick fast enough
- mean_predictedSpikes = rateParam(timeLocked);
- V = V(1:trials);
- V = V(timeLocked);
- RPE = RPE(timeLocked);
- if any(isinf(log(probSpike)))
- LH = 1e9;
- else
- LH = -1 * sum(log(probSpike));
- end
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