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The Relevance of the Time Domain to Neural Network Models [electronic resource] /

edited by A. Ravishankar Rao, Guillermo A. Cecchi.

Book Cover
Names: Rao, A. Ravishankar. | Cecchi, Guillermo A.
Published: Boston, MA : Springer US, 2012.
Series: Springer Series in Cognitive and Neural Systems ; 3
Topics: Medicine. | Neurosciences. | Computer science. | Artificial intelligence. | Biomedicine. | Neurosciences. | Computation by Abstract Devices. | Artificial Intelligence (incl. Robotics).
Online Access: Link to The Relevance of the Time Domain to Neural Network Models (access limited to Benedictine University patrons)
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245 14|aThe Relevance of the Time Domain to Neural Network Models|h[electronic resource] /|cedited by A. Ravishankar Rao, Guillermo A. Cecchi.
264 1|aBoston, MA :|bSpringer US,|c2012.
300 |aXVIII, 226 p.|bonline resource.
336 |atext|btxt|2rdacontent
337 |acomputer|bc|2rdamedia
338 |aonline resource|bcr|2rdacarrier
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490 1 |aSpringer Series in Cognitive and Neural Systems ;|v3
520 |aA 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.
650 0|aMedicine.
650 0|aNeurosciences.
650 0|aComputer science.
650 0|aArtificial intelligence.
650 14|aBiomedicine.
650 24|aNeurosciences.
650 24|aComputation by Abstract Devices.
650 24|aArtificial Intelligence (incl. Robotics).
700 1 |aRao, A. Ravishankar.|eeditor.
700 1 |aCecchi, Guillermo A.|eeditor.
710 2 |aSpringerLink (Online service)
773 0 |tSpringer eBooks
776 08|iPrinted edition:|z9781461407232
830 0|aSpringer Series in Cognitive and Neural Systems ;|v3
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856 40|uhttp://libweb.ben.edu/login?source=opac&url=http://dx.doi.org/10.1007/978-1-4614-0724-9|yLink to The Relevance of the Time Domain to Neural Network Models|z(access limited to Benedictine University patrons)
912 |aZDB-2-SBL
950 |aBiomedical and Life Sciences (Springer-11642)

Staff View for: The Relevance of the Time Domain to Neur