@inproceedings{2f50f78b93704723920d9ba447366613,
title = "Characterizing the firing properties of an adaptive analog VLSI neuron",
abstract = "We describe the response properties of a compact, low power, analog circuit that implements a model of a leaky-Integrate & Fire (I&F) neuron, with spike-frequency adaptation, refractory period and voltage threshold modulation properties. We investigate the statistics of the circuit's output response by modulating its operating parameters, like refractory period and adaptation level and by changing the statistics of the input current. The results show a clear match with theoretical prediction and neurophysiological data in a given range of the parameter space. This analysis defines the chip's parameter working range and predicts its behavior in case of integration into large massively parallel very-large-scale-integration (VLSI) networks.",
keywords = "SPIKE-FREQUENCY ADAPTATION, FIRE NEURONS, NETWORKS",
author = "Rubin, {Daniel Ben} and Elisabetta Chicca and Giacomo Indiveri",
year = "2004",
doi = "10.1007/978-3-540-27835-1_15",
language = "English",
isbn = "978-3-540-23339-8",
series = "Lecture Notes in Computer Science",
publisher = "Springer-Verlag Berlin Heidelberg",
pages = "189--200",
editor = "AJ Ijspeert and M Murata and N Wakamiya",
booktitle = "Biologically Inspired Approaches to Advanced Information Technology",
note = "1st International Workshop on Biologically Inspired Approaches to Advanced Information Technology ; Conference date: 29-01-2004 Through 30-01-2004",
}