Active intrinsic conductances in networks, transients, activity, plasticity (Akosy and Shouval 2021)

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"... we show that by including a small number of additional active conductances we can produce recurrent networks that are both more robust and exhibit firing-rate statistics that are more consistent with experimental results. We show that this holds both for bi-stable recurrent networks, which are thought to underlie working memory and for slowly decaying networks which might underlie the estimation of interval timing. We also show that by including these conductances, such networks can be trained to using a simple learning rule to predict temporal intervals that are an order of magnitude larger than those that can be trained in networks of leaky integrate and fire neurons."
1 . Akosy T, Shouval HZ (2021) Active intrinsic conductances in recurrent networks allow for long-lasting transients and sustained activity with realistic firing rates as well as robust plasticity J Comp Neuro [PubMed]
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Simulation Environment: MATLAB;
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There are two Matlab files here:

1. W_vs_T_loop_AIF.m - has no plasticity it runs over different values of W and this results in differnt decay times T. 
For runing LIF simply set gmax=0. Weight parameters would also need to be adjusted.
Spikes rasters are simply plotted using imagesc, these images might be misleading. A better option is to use plotSpikeRaster.m (

2. Network_training_AIF.m - has one trace synaptic plasticity learning. 
It does not start with zero recurrent weights to reduce training epochs. 
For LIF set gmax=0. 
For training from scratch use: makenewnetwork=true;
Data can be saved if savedata=true. Can run from the final state of a previous run using makenewnetwork=false;

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