title: Inhibition-based rhythms: experimental and mathematical observations on network dynamics.
abstract: An increasingly large body of data exists which demonstrates that oscillations of frequency 12-80 Hz are a consequence of, or are inextricably linked to, the behaviour of inhibitory interneurons in the central nervous system. This frequency range covers the EEG bands beta 1 (12-20 Hz), beta 2 (20-30 Hz) and gamma (30-80 Hz). The pharmacological profile of both spontaneous and sensory-evoked EEG potentials reveals a very strong influence on these rhythms by drugs which have direct effects on GABA(A) receptor-mediated synaptic transmission (general anaesthetics, sedative/hypnotics) or indirect effects on inhibitory neuronal function (opiates, ketamine). In addition, a number of experimental models of, in particular, gamma-frequency oscillations, have revealed both common denominators for oscillation generation and function, and subtle differences in network dynamics between the different frequency ranges. Powerful computer and mathematical modelling techniques based around both clinical and experimental observations have recently provided invaluable insight into the behaviour of large networks of interconnected neurons. In particular, the mechanistic profile of oscillations generated as an emergent property of such networks, and the mathematical derivation of this complex phenomenon have much to contribute to our understanding of how and why neurons oscillate. This review will provide the reader with a brief outline of the basic properties of inhibition-based oscillations in the CNS by combining research from laboratory models, large-scale neuronal network simulations, and mathematical analysis.
authors: School of Biomedical Sciences, The Worsley Building, University of Leeds, LS2 9NL, Leeds, UK. m.whittington@leeds.ac.uk
authors: M A
authors: MA
authors: Whittington
authors: R D
authors: RD
authors: Traub
authors: N
authors: N
authors: Kopell
authors: B
authors: B
authors: Ermentrout
authors: E H
authors: EH
authors: Buhl
mesh_terms: D004569:Electroencephalography; D006801:Humans; D008954:Models, Biological; D016571:Neural Networks, Computer