Competitive and Temporal Inhibition Structures with Spiking Neurons

Authors:
E. Ros;F. J. Pelayo;P. Martin-Smith;I. Rojas;D. Palomar;A. Prieto
Affiliations:
Department of Architecture and Computer Technology, University of Granada, Spain. E-mail: eros@atc.ugr.es;Department of Architecture and Computer Technology, University of Granada, Spain;Department of Architecture and Computer Technology, University of Granada, Spain;Department of Architecture and Computer Technology, University of Granada, Spain;Department of Architecture and Computer Technology, University of Granada, Spain;Department of Architecture and Computer Technology, University of Granada, Spain. E-mail: aprieto@goliat.ugr.es
Venue:
Neural Processing Letters
Year:
2000

Citing 4
Cited 1

Wiring considerations in analog VLSI systems, with application to field-programmable networks

Wiring considerations in analog VLSI systems, with application to field-programmable networks
Analog VLSI Excitatory Feedback Circuits for AttentionalShifts and Tracking

Analog Integrated Circuits and Signal Processing
A Pulsed Communication/Computation Framework for AnalogVLSI Perceptive Systems

Analog Integrated Circuits and Signal Processing
VLSI Implementation of a Neural Model Using Spikes

Analog Integrated Circuits and Signal Processing

Lookup table powered neural event-driven simulator

IWANN'05 Proceedings of the 8th international conference on Artificial Neural Networks: computational Intelligence and Bioinspired Systems

Quantified Score

Hi-index	0.00

Visualization

Abstract

The paper describes the implementation of competitive neural structures based on a spiking neural model that includes multiplicative or shunting synapses enabling non-saturated stable states in response to different stationary inputs as well as controllable transient responses. A VLSI-viable implementation of this model has been previously proposed and tested [1]. It has the possibility of modulating the output spike frequency by an additional input without affecting other neuron variables such as the membrane potential. This feature is exploited in the simulation of a Selective Temporal Inhibition network that is suitable for implementing attentional control systems.