Sentence examples for description of brain from inspiring English sources

This argument proceeds by comparing computational, dynamical, statistical and control theoretic approaches, and identifying criteria for a good description of brain function.

For a corresponding description of brain states, Freeman and Vitiello 2006, 2008, 2010) studied neurobiologically relevant observables such as electric and magnetic field amplitudes and neurotransmitter concentration.

The extensive presence of high-order chaos in a well-established physiological model of electrorhythmogenesis further emphasizes the applicability and relevance of mean field mesoscopic models in the description of brain activity at theoretical, experimental, and clinical levels.

Moreover, nonlinear nonequilibrium models also exhibit SDIC, so there are a number of possibilities in such approaches for very rich dynamical description of brain operations and cognitive phenomena (e.g., Juarrero 1999; Chemero and Silberstein 2008).

This way, quantum field theory provides formal elements from which a standard classical description of brain activity can be inferred, and this is its main role in the model.

The description of brain GAPDH mRNA mean expression values of different groups at different time intervals showed that the highest expression values were represented at the 0-time (control group) and the lowest values at 96 h with an exception in group 4 that showed higher expression level at 24 h relative to the control.

A major distinction among descriptions of brain networks is that of structural, functional, and effective connectivity.

Across many temporal and spatial scales, network neuroscience has emerged as a central focus of intellectual efforts, seeking meaningful descriptions of brain networks and explanatory sets of network features that underlie circuit function in health and dysfunction in disease.

Given the computational overheads in simulating the full neural field model this enhances our ability to study pattern formation and suggests more generally that modeling the interfaces of patterns, rather than the patterns themselves, may lead to novel, efficient descriptions of brain activity.

Moreover, multi-dimensional descriptions of brain areas based on multiple atlases, each of different modality (e.g. cytoarchitecture, folding patterns, connectivity, gene expression patterns), are ultimately likely to provide the best windows into brain organization.

However, many area parcellations have been found to be either incomplete or inaccurate; accordingly, descriptions of brain areas are continually revised and modified as parcellation methods continue to be developed (Toga et al. 2006; Zilles and Amunts 2010).