Advances in Neural Population Coding by M.A.L. Nicolelis

By M.A.L. Nicolelis

A collective examine attempt through scientists operating in laboratories all over the world, this e-book addresses matters overlaying all elements of neural inhabitants coding. This quantity, with contributions from individual neuroscientists, is split into six sections containing a ancient assessment of the concept that of neural inhabitants, introducing a sequence of latest experimental paradigms and analytical suggestions for investigating capability neural coding schemes and 4 sections concentrating on contemporary advances in inhabitants coding in a huge diversity of components of mind learn.

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In this article, we show that it is not only possible but also desirable to analyze more than two structures simultaneously. Furthermore, we show also that effective structural inference is only possible if simultaneous signals from many (representative) structures are jointly analyzed. To handle many simultaneous structures, we employ the recently introduced notion of partial directed coherence (PDC). This is a novel frequency domain approach for simultaneous multichannel data analysis based on Granger causality that employs multivariate auto-regressive (MAR) models for computational purposes (Baccal~i and Sameshima, 2001).

More importantly perhaps, as our examples show, is the fact that weak connections can be barely detectable. This is specially true in the case of DTF where the effect of weakly connected signal pathways is compounded (for example see Fig. 6a,c). Also, the number of simultaneously processed structures affects the achievable temporal resolution as more data points become necessary to insure statistically reliable detection of weak connections. Finally, it is important to have in mind that, though based on linear time series modelling, PDC proves applicable and useful for the analysis of multiple structures that involve some levels of nonlinear interactions as discussed in Sameshima and Baccakl (2001).

All rights reserved CHAFFER 4 Distributed processing in cultured neuronal networks Steve M. Potter * Division of Biology 156-29, California Institute of Technology, Pasadena, CA 91125, USA Introduction Dissociated neuronal networks Thanks to a number of recent technical advances, it will become increasingly popular to study the very basics of distributed information processing using cultured neuronal networks. Most researchers studying population coding are working with intact, living animals. Clearly, cultured neuronal networks lack many features of real brains, but they retain many others.

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