New technical approaches to monitoring and interpreting the dynamics of real neural networks

James M. Bower; Matthew Wilson; Jashojiban Banik; Mark E Nelson; Brian Rasnow

New technical approaches to monitoring and interpreting the dynamics of real neural networks

James M. Bower, Matthew Wilson, Jashojiban Banik, Mark E Nelson, Brian Rasnow

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

Understanding the functional organization of large-scale real neural networks will require the development of techniqes capable of predicting, recording, and interpreting the activities of the very large numbers of neurons that participate in their operation. This presents a considerable technical problem for systems-level neurobiologists and associated biomedical engineers. The authors describe their recent work using silicon manufacturing techniques to make multineuron in vivo recording devices, as well as the software tools developed for realistically simulating the neural networks studied.

Original language	English (US)
Title of host publication	IEEE/Engineering in Medicine and Biology Society Annual Conference
Publisher	Publ by IEEE
Pages	1921-1922
Number of pages	2
Volume	10 pt 4
State	Published - 1988
Externally published	Yes
Event	Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society - New Orleans, LA, USA Duration: Nov 4 1988 → Nov 7 1988

Other

Other	Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society
City	New Orleans, LA, USA
Period	11/4/88 → 11/7/88

ASJC Scopus subject areas

General Engineering

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Bower, JM, Wilson, M, Banik, J, Nelson, ME & Rasnow, B 1988, New technical approaches to monitoring and interpreting the dynamics of real neural networks. in IEEE/Engineering in Medicine and Biology Society Annual Conference. vol. 10 pt 4, Publ by IEEE, pp. 1921-1922, Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, New Orleans, LA, USA, 11/4/88.

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author = "Bower, {James M.} and Matthew Wilson and Jashojiban Banik and Nelson, {Mark E} and Brian Rasnow",

year = "1988",

language = "English (US)",

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pages = "1921--1922",

booktitle = "IEEE/Engineering in Medicine and Biology Society Annual Conference",

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note = "Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society ; Conference date: 04-11-1988 Through 07-11-1988",

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AU - Wilson, Matthew

AU - Banik, Jashojiban

AU - Nelson, Mark E

AU - Rasnow, Brian

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Y1 - 1988

N2 - Understanding the functional organization of large-scale real neural networks will require the development of techniqes capable of predicting, recording, and interpreting the activities of the very large numbers of neurons that participate in their operation. This presents a considerable technical problem for systems-level neurobiologists and associated biomedical engineers. The authors describe their recent work using silicon manufacturing techniques to make multineuron in vivo recording devices, as well as the software tools developed for realistically simulating the neural networks studied.

AB - Understanding the functional organization of large-scale real neural networks will require the development of techniqes capable of predicting, recording, and interpreting the activities of the very large numbers of neurons that participate in their operation. This presents a considerable technical problem for systems-level neurobiologists and associated biomedical engineers. The authors describe their recent work using silicon manufacturing techniques to make multineuron in vivo recording devices, as well as the software tools developed for realistically simulating the neural networks studied.

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AN - SCOPUS:0024178814

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T2 - Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society

Y2 - 4 November 1988 through 7 November 1988

ER -

New technical approaches to monitoring and interpreting the dynamics of real neural networks

Abstract

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