Efficient Discovery of Large Synchronous Events in Neural Spike Streams

TL;DR

This paper introduces a scalable data mining approach to efficiently identify significant synchronous firing patterns in large multi-neuronal spike train data, aiding neuroscience research and brain-computer interface development.

Contribution

It presents a novel temporal data mining scheme that reduces computational complexity by focusing on candidate patterns from smaller sets and assessing their statistical significance.

Findings

The method outperforms existing techniques in computational efficiency.

It successfully identifies significant synchronous patterns in large neural datasets.

The approach automatically determines thresholds for pattern significance.

Abstract

We address the problem of finding patterns from multi-neuronal spike trains that give us insights into the multi-neuronal codes used in the brain and help us design better brain computer interfaces. We focus on the synchronous firings of groups of neurons as these have been shown to play a major role in coding and communication. With large electrode arrays, it is now possible to simultaneously record the spiking activity of hundreds of neurons over large periods of time. Recently, techniques have been developed to efficiently count the frequency of synchronous firing patterns. However, when the number of neurons being observed grows they suffer from the combinatorial explosion in the number of possible patterns and do not scale well. In this paper, we present a temporal data mining scheme that overcomes many of these problems. It generates a set of candidate patterns from frequent patterns of smaller size; all possible patterns are not counted. Also we count only a certain well defined subset of occurrences and this makes the process more efficient. We highlight the computational advantage that this approach offers over the existing methods through simulations. We also propose methods for assessing the statistical significance of the discovered patterns. We detect only those patterns that repeat often enough to be significant and thus be able to automatically fix the threshold for the data-mining application. Finally we discuss the usefulness of these methods for brain computer interfaces.