The brain as a dynamic physical system

  title={The brain as a dynamic physical system},
  author={Thomas McKenna and Teresa Mcmullen and Michael F. Shlesinger},

Movement Enhances the Nonlinearity of Hippocampal Theta

The first explicit quantification regarding how behavior enhances the nonlinearity of the nervous system is described, demonstrating uniquely how theta changes with increasing speed due to the altered underlying neuronal dynamics and open new directions of research on the relationship between single-neuron activity and propagation of theta through the hippocampus.

Nonlinear coordination of cardiovascular autonomic control.

  • D. HoyerB. PompeH. HerzelU. Zwiener
  • Biology
    IEEE engineering in medicine and biology magazine : the quarterly magazine of the Engineering in Medicine & Biology Society
  • 1998
The conclusion is that the cardiovascular autonomic control system is more appropriately investigated by multivariate than by univariate data analysis.

A spatially continuous mean field theory of electrocortical activity

The results suggest that the classically described alpha may be instantiated in a number of qualitatively distinct dynamical regimes, all of which depend on the integrity of inhibitory-inhibitory population interactions.

Neuronal Oscillations Scale Up and Scale Down the Brain Dynamics Q1

This work proposes a physiological description of these multilevel interactions, based on phase–amplitude coupling of neuronal oscillations that operate at multiple frequencies and on different spatial scales, and expects that large-scale network oscillations in the low-frequency range, mediating downward effects, may play an important role in attention and consciousness.

Fundamental Dynamical Modes Underlying Human Brain Synchronization

Using large-scale intracranial recordings of epileptic patients during seizure-free periods, it is reported that the seemingly complex patterns of brain synchrony during the wake-sleep cycle can be represented by a small number of characteristic dynamic modes.

Nonlinear dynamics based machine learning: Utilizing dynamics-based flexibility of nonlinear circuits to implement different functions

This research focuses on nonlinear complex systems, and combines two powerful natural and biological phenomenon, Darwinian evolution and nonlinear dynamics and chaos, as a dynamics-oriented approach to designing intelligent, adaptive systems with applications.

Higher-Order Spectrum in Understanding Nonlinearity in EEG Rhythms

Higher-order spectrum is extended in order to indicate the hidden characteristics of EEG signals that simply do not arise from random processes, and shows utility of bispectral methods as an analytical tool in understanding neural process underlying human EEG patterns.



Nonlinear dynamics in a model neuron provide a novel mechanism for transient synaptic inputs to produce long-term alterations of postsynaptic activity.

These mode transitions provide an enduring response to a transient input, as well as a mechanism for phasic sensitivity (i.e., temporal specificity) in the role of nonlinear dynamics in information processing and storage at the level of the single neuron.


Pattern formation and switching between self-organized states are often associated with instabilities in open, nonequilibrium systems. We describe an experiment which shows that systematically

Variability and Chaos: Neurointegrative Principles in Self-Organization of Motor Patterns

The role of sensory feedback in the production of adaptive behavior of animals as they interact with complex and often unpredictable environments is discussed, and the chaotic neural activity provides a means for the nervous system to create informational space rendering animals more stably adaptable in such changing environments.

Spatio-Temporal EEG Patterns

Systems far from equilibrium exhibit the spontaneous emergence of spatial, temporal, and spatio-temporal patterns by the mechanism of self-organization [1], [2], [3]. Recently, considerable interest

Brain stem neuronal noise and neocortical “resonance”

We present a qualitative model and data in evidence for the selection and stabilization of neocortical brain-wave power spectral modes by slow periodic and fast noise driving by brain stem neurons.