Learning a sparse code for temporal sequences using STDP and sequence compression

S Byrnes; AN Burkitt; DB Grayden; H Meffin

Journal article

Learning a sparse code for temporal sequences using STDP and sequence compression

S Byrnes, AN Burkitt, DB Grayden, H Meffin

Neural Computation | Published : 2011

DOI: 10.1162/NECO_a_00184

Abstract

A spiking neural network that learns temporal sequences is described. A sparse code in which individual neurons represent sequences and subsequences enables multiple sequences to be stored without interference. The network is founded on a model of sequence compression in the hippocampus that is robust to variation in sequence element duration and well suited to learn sequences through spike-timing dependent plasticity (STDP). Three additions to the sequence compression model underlie the sparse representation: synapses connecting the neurons of the network that are subject to STDP, a competitive plasticity rule so that neurons specialize to individual sequences, and neural depolarization aft..

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University of Melbourne Researchers

Anthony Burkitt Author

David Grayden Author

Hamish Meffin Author

Related Projects (1)

Temporal Pattern Learning and Recognition in Neural Systems

This project is relevant to the National Research Priority area of Frontier Technologies and addresses fundamental cross-disciplinary issues..

Grants

Awarded by Australian Research Council (ARC)

Awarded by Australian Research Council

Funding Acknowledgements

We thank Matthieu Gilson for assisting in the development of the neuronal network simulation program. This work was funded by the Australian Research Council (ARC Discovery Project, no. DP0771815). The Bionic Ear Institute acknowledges the support it receives from the Victorian Government through its Operational Infrastructure Support Program.