In this section
@article{Maes:2021:10.1371/journal.pcbi.1008866,
author = {Maes, A and Barahona, M and Clopath, C},
doi = {10.1371/journal.pcbi.1008866},
journal = {PLoS Computational Biology},
title = {Learning compositional sequences with multiple time scales through a hierarchical network of spiking neurons},
url = {http://dx.doi.org/10.1371/journal.pcbi.1008866},
volume = {17},
year = {2021}
}
TY - JOUR
AB - Sequential behaviour is often compositional and organised across multiple time scales: a set of individual elements developing on short time scales (motifs) are combined to form longer functional sequences (syntax). Such organisation leads to a natural hierarchy that can be used advantageously for learning, since the motifs and the syntax can be acquired independently. Despite mounting experimental evidence for hierarchical structures in neuroscience, models for temporal learning based on neuronal networks have mostly focused on serial methods. Here, we introduce a network model of spiking neurons with a hierarchical organisation aimed at sequence learning on multiple time scales. Using biophysically motivated neuron dynamics and local plasticity rules, the model can learn motifs and syntax independently. Furthermore, the model can relearn sequences efficiently and store multiple sequences. Compared to serial learning, the hierarchical model displays faster learning, more flexible relearning, increased capacity, and higher robustness to perturbations. The hierarchical model redistributes the variability: it achieves high motif fidelity at the cost of higher variability in the between-motif timings.
AU - Maes,A
AU - Barahona,M
AU - Clopath,C
DO - 10.1371/journal.pcbi.1008866
PY - 2021///
SN - 1553-734X
TI - Learning compositional sequences with multiple time scales through a hierarchical network of spiking neurons
T2 - PLoS Computational Biology
UR - http://dx.doi.org/10.1371/journal.pcbi.1008866
UR - http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000634792900004&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=1ba7043ffcc86c417c072aa74d649202
UR - https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1008866
UR - http://hdl.handle.net/10044/1/91397
VL - 17
ER -
Artificial Intelligence Network
South Kensington Campus
Imperial College London
SW7 2AZ
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