Format

Send to

Choose Destination
Front Neurosci. 2011 May 31;5:73. doi: 10.3389/fnins.2011.00073. eCollection 2011.

Neuromorphic silicon neuron circuits.

Author information

1
Institute of Neuroinformatics, University of Zurich and ETH Zurich Zurich, Switzerland.

Abstract

Hardware implementations of spiking neurons can be extremely useful for a large variety of applications, ranging from high-speed modeling of large-scale neural systems to real-time behaving systems, to bidirectional brain-machine interfaces. The specific circuit solutions used to implement silicon neurons depend on the application requirements. In this paper we describe the most common building blocks and techniques used to implement these circuits, and present an overview of a wide range of neuromorphic silicon neurons, which implement different computational models, ranging from biophysically realistic and conductance-based Hodgkin-Huxley models to bi-dimensional generalized adaptive integrate and fire models. We compare the different design methodologies used for each silicon neuron design described, and demonstrate their features with experimental results, measured from a wide range of fabricated VLSI chips.

KEYWORDS:

adaptive exponential; analog VLSI; circuit; conductance based; integrate and fire; log-domain; spiking; subthreshold

Supplemental Content

Full text links

Icon for Frontiers Media SA Icon for PubMed Central
Loading ...
Support Center