Learning with precise spike times: A new decoding algorithm for liquid state machines

TL;DR

This paper introduces a novel decoding algorithm for liquid state machines that leverages precise spike timing, resulting in improved performance in neural decoding and classification tasks.

Contribution

A new LSM architecture and a forward orthogonal regression algorithm that utilize spike timing for enhanced neural decoding and learning.

Findings

Significant performance improvement in binary classification tasks.

Enhanced decoding accuracy of neural activity from multielectrode recordings.

Effective use of spike timing information over traditional methods.

Abstract

There is extensive evidence that biological neural networks encode information in the precise timing of the spikes generated and transmitted by neurons, which offers several advantages over rate-based codes. Here we adopt a vector space formulation of spike train sequences and introduce a new liquid state machine (LSM) network architecture and a new forward orthogonal regression algorithm to learn an input-output signal mapping or to decode the brain activity. The proposed algorithm uses precise spike timing to select the presynaptic neurons relevant to each learning task. We show that using precise spike timing to train the LSM and selecting the readout neurons leads to a significant increase in performance on binary classification tasks as well as in decoding neural activity from multielectrode array recordings, compared with what is achieved using the standard architecture and training method.