Analog VLSI and neural systems
Analog VLSI and neural systems
What does the retina know about natural scenes?
Neural Computation
Characterization of subthreshold MOS mismatch in transistors for VLSI systems
Analog Integrated Circuits and Signal Processing - Joint special issue on analog VLSI computation
Translinear circuits in subthreshold MOS
Analog Integrated Circuits and Signal Processing - Special issue: translinear circuits
Retinomorphic vision systems: reverse engineering the vertebrate retina
Retinomorphic vision systems: reverse engineering the vertebrate retina
Silicon retina with adaptive filtering properties
NIPS '97 Proceedings of the 1997 conference on Advances in neural information processing systems 10
The Retinomorphic Approach: Pixel-Parallel Adaptive Amplification,Filtering, and Quantization
Analog Integrated Circuits and Signal Processing
A Throughput-On-Demand Address-Event Transmitter for Neuromorphic Chips
ARVLSI '99 Proceedings of the 20th Anniversary Conference on Advanced Research in VLSI
MICRONEURO '96 Proceedings of the 5th International Conference on Microelectronics for Neural Networks and Fuzzy Systems
Analog VLSI Implementation of Artificial Neural Networks with Supervised On-Chip Learning
Analog Integrated Circuits and Signal Processing
Speed estimation with propagation maps
Neurocomputing
Visual system based on artificial retina for motion detection
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
A neural architecture for the symmetric-axis transform
Neurocomputing
An asynchronous spiking chaotic neuron integrated circuit
Neurocomputing
Visual processing platform based on artificial retinas
IWANN'07 Proceedings of the 9th international work conference on Artificial neural networks
Real-time bio-inspired contrast enhancement on GPU
Neurocomputing
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Retinomorphic chips may improve their spike-coding efficiency by emulating the primate retina's parallel pathways. To model the four predominant ganglion-cell types in the cat retina, I morphed outer and inner retina microcircuits into a silicon chip, Visio1. It has 104×96 photoreceptors, 4×52×48 ganglion-cells, a die size of 9.25×9.67 mm2 in 1.2 μm 5 V CMOS, and consumes 11.5 mW at 5 spikes/second/ganglion-cell. Visio1 includes novel subthreshold current-mode circuits that model horizontal-cell autofeedback, to decouple spatial filtering from local gain control, and model amacrine-cell loop-gain modulation, to adapt temporal filtering to motion. Different ganglion cells respond to motion in a quadrature sequence, making it possible to detect edges of one contrast or the other moving in one direction or the other. I present results from a multichip 2-D motion system, which implements Watson and Ahumada's model of human visual-motion sensing.