A subthreshold aVLSI implementation of the Izhikevich simple neuron model

Abstract

We present a circuit architecture for compact analog VLSI implementation of the Izhikevich neuron model, which efficiently describes a wide variety of neuron spiking and bursting dynamics using two state variables and four adjustable parameters. Log-domain circuit design utilizing MOS transistors in subthreshold results in high energy efficiency, with less than 1pJ of energy consumed per spike. We also discuss the effects of parameter variations on the dynamics of the equations, and present simulation results that replicate several types of neural dynamics. The low power operation and compact analog VLSI realization make the architecture suitable for human-machine interface applications in neural prostheses and implantable bioelectronics, as well as large-scale neural emulation tools for computational neuroscience.

VLSI circuits implementing computational models of neocortical circuits

“…Since the pioneering work on neuromorphic circuits in the late 1980s (Mead, 1990), a number of CMOS implementations of 'silicon neurons' (Mahowald and Douglas, 1991;Linares-Barranco et al, 1991;Schultz and Jabri, 1995;Patel and DeWeerth, 1997;Simoni and DeWeerth, 1999;Indiveri, 2003;Nakada et al, 2005;Rangan et al, 2010;van Schaik et al, 2010;Indiveri et al, 2011) and 'silicon synapses' (Hafliger et al, 1997;Bofill-i Petit and Murray, 2004;Indiveri et al, 2006;Koickal et al, 2007;Tanaka et al, 2007) have been presented. Recently, a number of systems have been proposed (Arthur and Boahen, 2004;Vogelstein et al, 2007;Merolla et al, 2007Merolla et al, , 2011Giulioni et al, 2008;Schemmel et al, 2010;Sharp et al, 2011) that attempt to facilitate the implementation of large-scale hardware neural networks, through the integration of thousands of silicon neurons and synapses in a single microelectronic IC.…”

An analog circuit implementation of a Huber-Braun cold receptor neuron model

“…Izhikevich neuron model, recently implemented in sub-threshold VLSI [2], has an excellent trade-off between implementation cost and biological plausibility, but this model is not biophysically meaningful [3].…”

A superposable silicon synapse with programmable reversal potential

“…Having succeeded in morphing visual and auditory sensory systems into mixed-analog-digital circuits, engineers are entering the arena of cortical modeling [2], [3], [4]; an arena in which neuromorphic systems' parallel operation and low energy consumption give them distinct advantages over software simulation. The neuron model of choice for large-scale cortical simulations [5], the quadratic integrate-and-fire (QIF) neuron, has been implemented successfully with log-domain circuits [6], [7], [8], [9]. The corresponding synapse model, a conductance tied to a programmable reversal potential, is however yet to be fully implemented in the log-domain.…”