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• W3136204906 abstract "Multiple brain regions are able to learn and express temporal sequences, and this functionality is an essential component of learning and memory. We propose a substrate for such representations via a network model that learns and recalls discrete sequences of variable order and duration. The model consists of a network of spiking neurons placed in a modular microcolumn based architecture. Learning is performed via a biophysically realistic learning rule that depends on synaptic ‘eligibility traces’. Before training, the network 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. This model provides a possible framework for biologically plausible sequence learning and memory, in agreement with recent experimental results." @default.
• W3136204906 created "2021-03-29" @default.
• W3136204906 creator A5046824302 @default.
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• W3136204906 date "2021-03-18" @default.
• W3136204906 modified "2023-10-06" @default.
• W3136204906 title "Learning precise spatiotemporal sequences via biophysically realistic learning rules in a modular, spiking network" @default.
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• W3136204906 doi "https://doi.org/10.7554/elife.63751" @default.
• W3136204906 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/7972481" @default.
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