Tunable neuromimetic integrated system for
emulating cortical neuron models.
Filippo Grassia, Laure Buhry, Timothée Lévi,
Jean Tomas, Alain Destexhe and Sylvain Saïghi
Frontiers in Neuroscience 5: 134, 2011.
Nowadays, many software solutions are currently available for
simulating neuron models. Less conventional than software-based
systems, hardware-based solutions generally combine digital and
analog forms of computation. In previous work, we designed
several neuromimetic chips, including the Galway chip that we used
for this paper. These silicon neurons are based on the
Hodgkin-Huxley formalism and they are optimized for reproducing
a large variety of neuron behaviors thanks to tunable parameters.
Due to process variation and device mismatch in analog chips, we
use a full-custom fitting method in voltage-clamp mode to tune our
neuromimetic integrated circuits. By comparing them with
experimental electrophysiological data of these cells, we show
that the circuits can reproduce the main firing features of cortical
cell types. In this paper, we present the experimental measurements
of our system which mimic the four most prominent biological
cells: fast spiking, regular spiking, intrinsically bursting, and
low-threshold spiking neurons into analog neuromimetic integrated
circuit dedicated to cortical neuron simulations. This hardware
and software platform will allow to improve the hybrid technique,
also called "dynamic-clamp," that consists of connecting artificial
and biological neurons to study the function of neuronal circuits.
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