Acetylcholine-modulated plasticity in reward-driven navigation (Zannone et al 2018)

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"Neuromodulation plays a fundamental role in the acquisition of new behaviours. In previous experimental work, we showed that acetylcholine biases hippocampal synaptic plasticity towards depression, and the subsequent application of dopamine can retroactively convert depression into potentiation. We also demonstrated that incorporating this sequentially neuromodulated Spike- Timing-Dependent Plasticity (STDP) rule in a network model of navigation yields effective learning of changing reward locations. Here, we employ computational modelling to further characterize the effects of cholinergic depression on behaviour. We find that acetylcholine, by allowing learning from negative outcomes, enhances exploration over the action space. We show that this results in a variety of effects, depending on the structure of the model, the environment and the task. Interestingly, sequentially neuromodulated STDP also yields flexible learning, surpassing the performance of other reward-modulated plasticity rules."
1 . Zannone S, Brzosko Z, Paulsen O, Clopath C (2018) Acetylcholine-modulated plasticity in reward-driven navigation: a computational study. Sci Rep 8:9486 [PubMed]
Model Information (Click on a link to find other models with that property)
Model Type: Realistic Network;
Brain Region(s)/Organism: Mouse;
Cell Type(s):
Gap Junctions:
Transmitter(s): Acetylcholine; Dopamine;
Simulation Environment: MATLAB;
Model Concept(s): Synaptic Plasticity; Learning; STDP; Reward-modulated STDP; Hebbian plasticity; Spatial Navigation;
Implementer(s): Zannone, Sara [s.zannone14 at]; Clopath, Claudia [c.clopath at];
Search NeuronDB for information about:  Acetylcholine; Dopamine;
function [ conv1_pre, conv1_post,tot_conv, trace, W] = weights_update_stdp(A_plus, A_minus, tau_plus, tau_minus, X, Y, conv1_pre, conv1_post, trace, tau_e)

[a,b] = size(conv1_pre);
% tot_conv = total change to apply to the synapse * learning rate
conv_pre_old = convolution_type2(conv1_pre, tau_plus,  A_plus, zeros(a, b)); %pre trace without spikes - used for coincident spikes
conv_post_old = convolution_type2(conv1_post, tau_minus,A_minus, zeros(a, b)); %post trace without spikes - used for coincident spikes

[conv_pre, conv1_pre] = convolution_type2 (conv1_pre, tau_plus,  A_plus, X); %trace given by pre-synaptic neuron, amplitude A+ and time constant tau+
[conv_post, conv1_post] = convolution_type2 (conv1_post, tau_minus,A_minus, Y); %trace given by post-synaptic neuron, amplitude A- and time constant tau-
W = (conv_pre.*Y + conv_post.*X).*(X+Y~=2)+((conv_pre_old.*Y + conv_post_old.*X)+(A_plus+A_minus)/2).*(X+Y==2); %total change in synapse due to stpd 

%%Eligibility Trace
[tot_conv, trace] = convolution_type2 (trace, tau_e,  1, W); %all weight changes filtered through the eligibility trace


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