Winner-take-all networks of O(N) complexity
Advances in neural information processing systems 1
Scanners for visualizing activity of analog VLSI circuitry
Analog Integrated Circuits and Signal Processing
Translinear circuits in subthreshold MOS
Analog Integrated Circuits and Signal Processing - Special issue: translinear circuits
Winner-Take-All Networks with Lateral Excitation
Analog Integrated Circuits and Signal Processing
Pseudo-Resistive Networks and their Applications to Analog Collective Computation
ICANN '97 Proceedings of the 7th International Conference on Artificial Neural Networks
Conjunction Search Using a 1-D, Analog VLSI-based, Attentional Search/Tracking Chip
ARVLSI '99 Proceedings of the 20th Anniversary Conference on Advanced Research in VLSI
A Reconfigurable Neuromorphic VLSI Multi-Chip System Applied to Visual Motion Computation
MICRONEURO '99 Proceedings of the 7th International Conference on Microelectronics for Neural, Fuzzy and Bio-Inspired Systems
Analog vlsi visual attention systems
Analog vlsi visual attention systems
2006 Special Issue: Modeling attention to salient proto-objects
Neural Networks
Computation with spikes in a winner-take-all network
Neural Computation
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We describe an aVLSI network consisting of a group of excitatory neurons and a global inhibitory neuron. The output of the inhibitory neuron is normalized with respect to the input strengths in a manner that is useful in any system where we wish the output signal to code only the strength of the inputs, and not be dependent on the number of active inputs. The circuitry in each neuron is equivalent to that in Lazzaro's winner-take-all (WTA) circuit [1] with one additional transistor and a voltage reference. As in Lazzaro's circuit, the outputs of the excitatory neurons code for the neuron with the largest input. The novel feature is that multiple winners can be chosen (soft-max). By varying one parameter, the network can operate in a soft-max regime or a WTA regime. We show results from two different fabricated networks.