Title :
Optic nerve signals in a neuromorphic chip II: testing and results
Author :
Zaghloul, Kareem A. ; Boahen, Kwabena
Author_Institution :
Dept. of Neurosurg., Univ. of Pennsylvania, Philadelphia, PA, USA
fDate :
4/1/2004 12:00:00 AM
Abstract :
Seeking to match the brain´s computational efficiency , we draw inspiration from its neural circuits. To model the four main output (ganglion) cell types found in the retina, we morphed outer and inner retina circuits into a 96×60-photoreceptor, 3.5×3.3 mm2, 0.35 μm-CMOS chip. Our retinomorphic chip produces spike trains for 3600 ganglion cells (GCs), and consumes 62.7 mW at 45 spikes/s/GC. This chip, which is the first silicon retina to successfully model inner retina circuitry, approaches the spatial density of the retina. We present experimental measurements showing that the chip´s subthreshold current-mode circuits realize luminance adaptation, bandpass spatiotemporal filtering, temporal adaptation and contrast gain control. The four different GC outputs produced by our chip encode light onset or offset in a sustained or transient fashion, producing a quadrature-like representation. The retinomorphic chip´s circuit design is described in a companion paper [Zaghloul and Boahen (2004)].
Keywords :
CMOS analogue integrated circuits; band-pass filters; brightness; cellular biophysics; elemental semiconductors; eye; neural chips; neurophysiology; prosthetics; silicon; spatiotemporal phenomena; vision; 0.35 mum; 62.7 mW; CMOS chip; Si; adaptive circuits; bandpass spatiotemporal filtering; brain computational efficiency; contrast gain control; current-mode circuits; ganglion cell; inner retina circuits; luminance adaptation; neural circuits; neural systems; neuromorphic chip II; optic nerve signals; outer retina circuits; photoreceptor; prosthetics; retina; retinomorphic chip; silicon retina; temporal adaptation; vision; Circuit testing; Computational efficiency; Current measurement; Current mode circuits; Gain measurement; Neuromorphics; Optical filters; Retina; Semiconductor device measurement; Silicon; Action Potentials; Adaptation, Physiological; Animals; Artificial Intelligence; Biomimetic Materials; Computer Simulation; Cones (Retina); Electronics; Equipment Design; Equipment Failure Analysis; Humans; Miniaturization; Models, Neurological; Nerve Net; Optic Nerve; Retina; Retinal Ganglion Cells; Semiconductors; Signal Processing, Computer-Assisted; Synaptic Transmission; Vision;
Journal_Title :
Biomedical Engineering, IEEE Transactions on
DOI :
10.1109/TBME.2003.821040