A mathematical language for linking fine-scale structure in spikes from hundreds to thousands of neurons with behaviour

TL;DR

This paper introduces a new mathematical language for analyzing complex neural spike patterns across large populations of neurons, enabling better linking of neural activity to behavior.

Contribution

The paper presents a novel mathematical operation that decomposes complex spike patterns into structured elements, facilitating comparison and behavioral correlation.

Findings

Revealed previously unseen neural structure in macaque prefrontal cortex

Predicted memory-guided decisions and errors using spike pattern analysis

Applied to large-scale neural data from awake, behaving animals

Abstract

Recent advances in neural recording technology allow simultaneously recording action potentials from hundreds to thousands of neurons in awake, behaving animals. However, characterizing spike patterns in the resulting data, and linking these patterns to behaviour, remains a challenging task. The lack of a rigorous mathematical language for variable numbers of events (spikes) emitted by multiple agents (neurons) is an important limiting factor. We introduce a new mathematical operation to decompose complex spike patterns into a set of simple, structured elements. This creates a mathematical language that allows comparing spike patterns across trials, detecting sub-patterns, and making links to behaviour via a clear distance measure. We apply the method to dual Utah array recordings from macaque prefrontal cortex, where this technique reveals previously unseen structure that can predict both memory-guided decisions and errors in a virtual-reality working memory task. These results demonstrate that this technique provides a powerful new approach to understand structure in the spike times of neural populations, at a scale that will continue to grow more and more rapidly in upcoming years.