Metric Framework of Coherent Activity Patterns Identification in Spiking Neuronal Networks

TL;DR

This paper introduces a novel metric framework for identifying and analyzing localized coherent activity patterns in spiking neuronal networks, focusing on single-neuron scale analysis rather than global measures.

Contribution

It presents a new perspective that models neural networks as metric spaces and detects coherent activity clusters through spatial continuity of activity functions.

Findings

Enables analysis of network coherence at the single-neuron level.

Provides a concise algorithmic profile of activity patterns.

Distinguishes localized coherent patterns from global synchrony.

Abstract

Partial synchronization plays a crucial role in the functioning of neuronal networks: selective, coordinated activation of neurons enables information processing that flexibly adapts to a changing computational context. Since the structure of coherent activity patterns reflects the network's current state, developing automated tools to identify them is a key challenge in neurodynamics. Existing methods for analyzing neuronal dynamics tend to focus on global characteristics of the network, such as its aggregated synchrony level. While this approach can distinguish between the network's main dynamical states, it cannot reveal the localization or properties of distinct coherent patterns. In this work, we propose a new perspective on neural dynamics analysis that enables the study of network coherence at the single-neuron scale. We interpret the network as a metric space of neurons and represent its instantaneous state as an activity function on that space. We identify specific coherent activity clusters as regions where the activity function exhibits spatial continuity. Each cluster's activity is further characterized using the analytical properties of the activity function within that region. This approach yields a concise yet detailed algorithmic profile of the network's activity patterns.