The Relevance of the Time Domain to Neural Network Models

  • A. Ravishankar Rao
  • Guillermo A. Cecchi

Part of the Springer Series in Cognitive and Neural Systems book series (SSCNS, volume 3)

Table of contents

  1. Front Matter
    Pages I-XVII
  2. Guillermo Cecchi, A. Ravishankar Rao
    Pages 1-7
  3. Pietro DeLellis, Mario di Bernardo, Giovanni Russo
    Pages 9-32
  4. A. Ravishankar Rao, Guillermo A. Cecchi
    Pages 75-97
  5. Ji Ryang Chung, Jaerock Kwon, Timothy A. Mann, Yoonsuck Choe
    Pages 99-115
  6. Ken Saito, Akihiro Matsuda, Katsutoshi Saeki, Fumio Uchikoba, Yoshifumi Sekine
    Pages 117-133
  7. Alexander D. Rast, M. Mukaram Khan, Xin Jin, Luis A. Plana, Steve B. Furber
    Pages 135-157
  8. N. A. P. Vasconcelos, W. Blanco, J. Faber, H. M. Gomes, T. M. Barros, S. Ribeiro
    Pages 179-198
  9. Marcio Junior Sturzbecher, Draulio Barros de Araujo
    Pages 199-217
  10. N. A. P. Vasconcelos, W. Blanco, J. Faber, H. M. Gomes, T. M. Barros, S. Ribeiro
    Pages 1-2
  11. Back Matter
    Pages 219-222

About this book

Introduction

A significant amount of effort in neural modeling is directed towards understanding the representation of external objects in the brain. There is also a rapidly growing interest in modeling the intrinsically-generated activity in the brain, as represented by the default mode network hypothesis, and the emergent behavior that gives rise to critical phenomena such as neural avalanches. Time plays a critical role in these intended modeling domains, from the exquisite discriminations in the mammalian auditory system to the precise timing involved in high-end activities such as competitive sports or professional music performance.

The growth in experimental high-throughput neuroscience techniques has allowed the multi-scale acquisition of neural signals, from individual electrode recordings to whole-brain functional magnetic resonance imaging activity, including the ability to manipulate neural signals with optogenetic approaches. This has created a deluge of experimental data, spanning multiple spatial and temporal scales, and posing the enormous challenge of its interpretation in terms of a predictive theory of brain function. In addition, there has been a massive growth in availability of computational power through parallel computing.

The Relevance of the Time Domain to Neural Network Models aims to develop a unified view of how the time domain can be effectively employed in neural network models. The book proposes that conceptual models of neural interaction are required in order to understand the data being collected. Simultaneously, these proposed models can be used to form hypotheses of neural interaction and system behavior that can be neuroscientifically tested. The book concentrates on a crucial aspect of brain modeling: the nature and functional relevance of temporal interactions in neural systems.

This book will appeal to a wide audience consisting of computer scientists and electrical engineers interested in brain-like computational mechanisms, computer architects exploring the development of high-performance computing systems to support these computations, neuroscientists probing the neural code and signaling mechanisms, mathematicians and physicists interested in modeling complex biological phenomena, and graduate students in all these disciplines who are searching for challenging research questions.

Editors and affiliations

  • A. Ravishankar Rao
    • 1
  • Guillermo A. Cecchi
    • 2
  1. 1.T.J. Watson Research CenterIBM CorporationYorktown HeightsUSA
  2. 2.Research Center, Dept. Silicon TechnologyIBM Thomas J. WatsonYorktown HeightsUSA

Bibliographic information

  • DOI https://doi.org/10.1007/978-1-4614-0724-9
  • Copyright Information Springer Science+Business Media, LLC 2012
  • Publisher Name Springer, Boston, MA
  • eBook Packages Biomedical and Life Sciences
  • Print ISBN 978-1-4614-0723-2
  • Online ISBN 978-1-4614-0724-9
  • About this book