Neurodynamics

Neurodynamics is the study of time-varying activity in nervous systems, from individual neurons to large-scale brain networks, using concepts from dynamical systems theory, computational modeling, and statistics. It emphasizes how intrinsic cellular properties, synaptic interactions, and network architecture produce patterns such as spikes, bursts, rhythms, and transient events, and how these patterns relate to perception, cognition, and behavior.

A variety of models are used. Biophysically detailed neuron models (for example Hodgkin-Huxley-type) capture membrane dynamics, while simplified neuron models (FitzHugh-Nagumo, integrate-and-fire) emphasize essential excitability. Population-level approaches—neural mass and neural field models (such as Jansen-Rit, Wilson-Cowan)—describe average activity in cortical ensembles and can exhibit oscillations, synchronization, and traveling waves. The study often focuses on attractor states, bifurcations, multistability, and noise- and input-driven transitions.

Methods combine experimental data with theory and computation. Electrophysiology, electroencephalography, magnetoencephalography, and functional imaging provide constraints on model dynamics, while analysis uses phase space, stability analysis, spectral methods, and numerical simulations. Neurodynamics seeks to link cellular properties to network rhythms and to explain how dynamic patterns support information processing.

Applications include understanding normal brain function—sensory processing, decision making, motor control, and sleep—and clinical phenomena such as epilepsy, anesthesia, and neuromodulation effects. The field is interdisciplinary, bridging neuroscience, mathematics, physics, and engineering, and continues to integrate data-driven approaches with mechanistic modeling to illuminate how dynamic brain activity gives rise to behavior.