Metric framework of coherent activity patterns identification in spiking neuronal networks

Chaos, Solitons and Fractals. 2025. Vol. 203. P. 1–11.

Радушев Д. О., Dogonasheva O., Gutkin B., Zakharov D.

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 novel 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 coherence patterns, opening new perspectives for the automated analysis of neural activity at the functional region organization level.

Keywords: metric space, метрическое пространство, synchronous clusters, spiking neuronal networks, химерное состояние, спайковые нейронные сети, chimera states, синхронные кластеры, Partial synchronization, частичная синхронизация

Publication based on the results of: Multidisciplinary study of behavior and decision-making in health population and patients using behavioral, economic, neurocognitive, neuroeconomic, neurocomputational and neural network approaches (2025)