GitHub - ModelDBRepository/245018: Acetylcholine-modulated plasticity in reward-driven navigation (Zannone et al 2018)

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

modeldb.science/245018

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Name		Name	Last commit message	Last commit date
Latest commit History 2 Commits
images		images
convolution.m		convolution.m
convolution_type2.m		convolution_type2.m
dynamic_reward_signal.m		dynamic_reward_signal.m
negative_feedback_signal.m		negative_feedback_signal.m
neuron.m		neuron.m
readme.html		readme.html
seq_neuromod_plasticity.m		seq_neuromod_plasticity.m
weights_update_stdp.m		weights_update_stdp.m

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This is the readme for the models associated with the paper:<br/>
Zannone S, Brzosko Z, Paulsen O, Clopath C (2018) Acetylcholine-modulated plasticity in reward-driven navigation: a computational study. Sci Rep 8:9486
<br/>
<a href="http://dx.doi.org/10.1038/s41598-018-27393-2">http://dx.doi.org/10.1038/s41598-018-27393-2</a>
<p/>
This matlab code was contributed by S Zannone.
<p/>
Instructions to run the model: 
<p/>
The file seq_neuromod_plasticity.m is needed to run the plasticity
rules called +ACh and -ACh (the presence or absence of acetylcholine
can be decided with the flag ACh_flag). This is needed for Figures 2,
3 and 4. This is the bit of the code that partially overlaps with the
elife paper:<p/>
Brzosko Z, Zannone S, Schultz W, Clopath C, Paulsen O (2017) Sequential neuromodulation of Hebbian plasticity offers mechanism for effective reward-based navigation. Elife
<br/><a href="http://dx.doi.org/10.7554/eLife.27756">http://dx.doi.org/10.7554/eLife.27756</a>
<br/>Note: This model accession 245018 archive extends ModelDB
accession number <a href="http://modeldb.yale.edu/233396">233396</a>
by including the new files dynamic_reward_signal.m and
negative_feedback_signal.m. The seq_neuromod_plasticity.m scripts are
essentially the same between the two modeldb entries, with color and
content edits to the plotting commands.
<p/>
The same file (seq_neuromod_plasticity.m), however, can be also used to run
simulations of the r-STDP rule by setting ACh_flag = 0 and changing
the STDP parameters (A_pre_post = pre-post window amplitude,
A_post_pre = post-pre window amplitude, tau_pre_post = pre-post window
time constant, tau_post_pre = post-pre window time constan). This is
needed to reproduce Figure 5.

<p/>The file dynamic_reward_signal.m was
used for Figure 6 and negative_feedback_signal.m is for Figure 7.
<p/>
Here is a sample screenshot from running negative_feedback_signal which displays trials 1-40 results as they run:
<p/>
<img src="images/screenshot.png" width="750">
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