Imperial College London
Alternate

Professor Claudia Clopath

Faculty of Engineering, Department of Bioengineering

Professor of Computational Neuroscience
 
 
 
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Contact

 

+44 (0)20 7594 1435c.clopath Website

 
 
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Location

 

Royal School of Mines 4.09Royal School of MinesSouth Kensington Campus

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Summary

 

Publications

Citation

BibTex format

@article{Maes:2019:10.1371/journal.pcbi.1007606,
author = {Maes, A and Barahona, M and Clopath, C},
doi = {10.1371/journal.pcbi.1007606},
journal = {PLOS Computational Biology},
pages = {e1007606--e1007606},
title = {Learning spatiotemporal signals using a recurrent spiking network that discretizes time},
url = {http://dx.doi.org/10.1371/journal.pcbi.1007606},
volume = {16},
year = {2019}
}
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RIS format (EndNote, RefMan)

TY  - JOUR
AB  - <jats:title>Abstract</jats:title><jats:p>Learning to produce spatiotemporal sequences is a common task the brain has to solve. The same neural substrate may be used by the brain to produce different sequential behaviours. The way the brain learns and encodes such tasks remains unknown as current computational models do not typically use realistic biologically-plausible learning. Here, we propose a model where a spiking recurrent network of excitatory and inhibitory biophysical neurons drives a read-out layer: the dynamics of the recurrent network is constrained to encode time while the read-out neurons encode space. Space is then linked with time through plastic synapses that follow common Hebbian learning rules. We demonstrate that the model is able to learn spatiotemporal dynamics on a timescale that is behaviourally relevant. Learned sequences are robustly replayed during a regime of spontaneous activity.</jats:p><jats:sec><jats:title>Author summary</jats:title><jats:p>The brain has the ability to learn flexible behaviours on a wide range of time scales. Previous studies have successfully build spiking network models that learn a variety of computational tasks. However, often the learning involved is not local. Here, we investigate a model using biological-plausible plasticity rules for a specific computational task: spatiotemporal sequence learning. The architecture separates time and space into two different parts and this allows learning to bind space to time. Importantly, the time component is encoded into a recurrent network which exhibits sequential dynamics on a behavioural time scale. This network is then used as an engine to drive spatial read-out neurons. We demonstrate that the model can learn complicated spatiotemporal spiking dynamics, such as the song of a bird, and replay the song robustly.</jats:p></jats:sec>
AU  - Maes,A
AU  - Barahona,M
AU  - Clopath,C
DO  - 10.1371/journal.pcbi.1007606
EP  - 1007606
PY  - 2019///
SP  - 1007606
TI  - Learning spatiotemporal signals using a recurrent spiking network that discretizes time
T2  - PLOS Computational Biology
UR  - http://dx.doi.org/10.1371/journal.pcbi.1007606
UR  - http://hdl.handle.net/10044/1/75610
VL  - 16
ER  -
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