Dynamic states in a network of type-I Morris–Lecar neurons characterized using the metric framework

Chaos. 2026. Vol. 36. No. 5. Article 053145.

D. Radushev, O. Dogonasheva, B. Gutkin, D. Zakharov

In recent decades, analysis of dynamic states in neural networks has become an important direction of the synchronization theory. One of the most interesting neuronal network states is the chimera state, in which coherent and incoherent activity clusters coexist. While chimera states have been shown to exist in various networks, their precise automatic identification in neuronal networks has turned out to be methodologically difficult. Traditional approaches that identify chimeras as regimes other than asynchronous state or full synchronization (based on diverse order parameters) appear to suffer from inconsistencies. For example, chimera states are poorly differentiated from other regimes such as traveling waves and multi-cluster synchronization. As a more reliable alternative, chimera states can be identified through the study of distinct coherence and incoherence regions present in the network -- the location of which is itself a matter of primary interest in neural activity analysis. 
In this paper, we use a recently introduced methodology allowing such chimera detection: the Metric Framework (MF) of coherent activity patterns identification. To illustrate the MF-based chimera identification method, we analyze dynamic states in the ring network of type-I Morris-Lecar neurons, which model the brain pyramidal neuron activity.
We account for multistability in the network, thereby discovering an abundance of traveling wave states beyond their previously reported existence areas.

Keywords: spiking neuronal networks, chimera state, химерное состояние, спайковые нейронные сети, Partial synchronization, частичная синхронизация, Activity patterns, metric framework, паттерны активности, метрический подход

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)