Computational models in neuroscience usually take the form of systems of differential
equations. The behaviour of such systems is the subject of dynamical systems theory …

The collective dynamics regulated by the field energy has garnered significant attention at
the level of biological neuron networks, but this has not yet been involved in the brain-like …

In neural circuits, synaptic strengths influence neuronal activity by shaping network
dynamics, and neuronal activity influences synaptic strengths through activity-dependent …

As a schematic model of the complexity economic agents are confronted with, we introduce
the “Sherrington-Kirkpatrick game,” a discrete time binary choice model inspired from mean …

This paper investigates a fractional-order Rulkov neuron model with discrete memristor
under external electromagnetic radiation. The magnetic flux fluctuation crossing the neuron …

The coupling dynamics between different brain regions has long been a gap in the study of
neural dynamics. To overcome this bottleneck, the synaptic crosstalk characterized by …

We study a paradigmatic random recurrent neural network introduced by Sompolinsky,
Crisanti, and Sommers (SCS). In the infinite size limit, this system exhibits a direct transition …

Neural population dynamics are often highly coordinated, allowing task-related
computations to be understood as neural trajectories through low-dimensional subspaces …

Dynamical mean-field theory is a powerful physics tool used to analyze the typical behavior
of neural networks, where neurons can be recurrently connected, or multiple layers of …

Neural circuits construct internal 'world-models' to guide behavior. The predictive processing
framework posits that neural activity signaling sensory predictions and concurrently …