Synaptic learning in VLSI-based artificial nerve cells

Andrew J. Laffely; Seth Wolpert

Synaptic learning in VLSI-based artificial nerve cells

Andrew J. Laffely
, Seth Wolpert

School of Science, Engineering & Technology (Harrisburg)

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

1 Scopus citations

Abstract

In this paper we present a VLSI method for analog synaptic learning in an electronic neuronal model. This method reduces the size and complexity involved in implementing adaptive neuronally-based controllers for robotic motion. It also provides for a continuous range of synaptic weights at both excitatory and inhibitory inputs while anticipating the need to interface to a pulsedriven system. The system is described, and test results indicate that it is able to alter the synaptic coupling on an inhibitory or an excitatory input over a wide range.

Original language	English (US)
Title of host publication	1993 IEEE 19th Annual Northeasrt Bioengineering Conference
Publisher	Publ by IEEE
Pages	103-105
Number of pages	3
ISBN (Print)	0780309251
State	Published - 1993
Event	Proceedings of the 1993 IEEE 19th Annual Northeast Bioengineering Conference - Newark, NJ, USA Duration: Mar 18 1993 → Mar 19 1993

Other

Other	Proceedings of the 1993 IEEE 19th Annual Northeast Bioengineering Conference
City	Newark, NJ, USA
Period	3/18/93 → 3/19/93

All Science Journal Classification (ASJC) codes

General Chemical Engineering

Cite this

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title = "Synaptic learning in VLSI-based artificial nerve cells",

abstract = "In this paper we present a VLSI method for analog synaptic learning in an electronic neuronal model. This method reduces the size and complexity involved in implementing adaptive neuronally-based controllers for robotic motion. It also provides for a continuous range of synaptic weights at both excitatory and inhibitory inputs while anticipating the need to interface to a pulsedriven system. The system is described, and test results indicate that it is able to alter the synaptic coupling on an inhibitory or an excitatory input over a wide range.",

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note = "Proceedings of the 1993 IEEE 19th Annual Northeast Bioengineering Conference ; Conference date: 18-03-1993 Through 19-03-1993",

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T1 - Synaptic learning in VLSI-based artificial nerve cells

AU - Laffely, Andrew J.

AU - Wolpert, Seth

PY - 1993

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N2 - In this paper we present a VLSI method for analog synaptic learning in an electronic neuronal model. This method reduces the size and complexity involved in implementing adaptive neuronally-based controllers for robotic motion. It also provides for a continuous range of synaptic weights at both excitatory and inhibitory inputs while anticipating the need to interface to a pulsedriven system. The system is described, and test results indicate that it is able to alter the synaptic coupling on an inhibitory or an excitatory input over a wide range.

AB - In this paper we present a VLSI method for analog synaptic learning in an electronic neuronal model. This method reduces the size and complexity involved in implementing adaptive neuronally-based controllers for robotic motion. It also provides for a continuous range of synaptic weights at both excitatory and inhibitory inputs while anticipating the need to interface to a pulsedriven system. The system is described, and test results indicate that it is able to alter the synaptic coupling on an inhibitory or an excitatory input over a wide range.

UR - https://www.scopus.com/pages/publications/0027238947

UR - https://www.scopus.com/pages/publications/0027238947#tab=citedBy

M3 - Conference contribution

AN - SCOPUS:0027238947

SN - 0780309251

SP - 103

EP - 105

BT - 1993 IEEE 19th Annual Northeasrt Bioengineering Conference

PB - Publ by IEEE

T2 - Proceedings of the 1993 IEEE 19th Annual Northeast Bioengineering Conference

Y2 - 18 March 1993 through 19 March 1993

ER -

Synaptic learning in VLSI-based artificial nerve cells

Abstract

Other

All Science Journal Classification (ASJC) codes

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