Sequence learning via biophysically realistic learning rules (Cone and Shouval 2021)

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This work proposes a substrate for learned sequential representations, via a network model that can robustly learn and recall discrete sequences of variable order and duration. The model consists of a network of spiking leaky-integrate-and-fire model neurons placed in a modular architecture designed to resemble cortical microcolumns. Learning is performed via a biophysically realistic learning rule based on “eligibility traces”, which hold a history of synaptic activity before being converted into changes in synaptic strength upon neuromodulator activation. Before training, the network responds to incoming stimuli, and contains no memory of any particular sequence. After training, presentation of only the first element in that sequence is sufficient for the network to recall an entire learned representation of the sequence. An extended version of the model also demonstrates the ability to successfully learn and recall non-Markovian sequences.
1 . Cone I, Shouval HZ (2021) Learning precise spatiotemporal sequences via biophysically realistic learning rules in a modular, spiking network. Elife [PubMed]
Model Information (Click on a link to find other models with that property)
Model Type:
Brain Region(s)/Organism: Visual cortex;
Cell Type(s): Abstract integrate-and-fire leaky neuron;
Gap Junctions:
Simulation Environment: MATLAB;
Model Concept(s): Sequence learning; Eligibility traces;
Implementer(s): Cone, Ian [iancone at rice dot edu];
Final Code for Sequence Project
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