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Critical Reviews™ in Biomedical Engineering
SJR: 0.207 SNIP: 0.376 CiteScore™: 0.79

ISSN Print: 0278-940X
ISSN Online: 1943-619X

Critical Reviews™ in Biomedical Engineering

DOI: 10.1615/CritRevBiomedEng.2013006841
pages 459-470

Complexity in Neurobiology: Perspectives from the study of noise in human motor systems

Ramesh Balasubramaniam
Sensorimotor Neuroscience Laboratory, McMaster University
Kjerstin Torre
M2H Laboratory, University of Montpellier, Montpellier, France

ABSTRACT

This article serves as an introduction to the themed special issue on "Complex Systems in Neurobiology." The study of complexity in neurobiology has been sensitive to the stochastic processes that dominate the micro-level architecture of neurobiological systems and the deterministic processes that govern the macroscopic behavior of these systems. A large body of research has traversed these scales of interest, seeking to determine how noise at one spatial or temporal scale influences the activity of the system at another scale. In introducing this special issue, we pay special attention to the history of inquiry in complex systems and why scientists have tended to favor linear, causally driven, reductionist approaches in Neurobiology. We follow this with an elaboration of how an alternative approach might be formulated. To illustrate our position on how the sciences of complexity and the study of noise can inform neurobiology, we use three systematic examples from the study of human motor control and learning: 1) phase transitions in bimanual coordination; 2) balance, intermittency, and discontinuous control; and 3) sensorimotor synchronization and timing. Using these examples and showing that noise is adaptively utilized by the nervous system, we make the case for the studying complexity with a perspective of understanding the macroscopic stability in biological systems by focusing on component processes at extended spatial and temporal scales. This special issue continues this theme with contributions in topics as diverse as neural network models, physical biology, motor learning, and statistical physics.


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