A silicon model of auditory localization
Neural Computation
Derivation of encoding characteristics of layer ii cerebral cortex
Journal of Cognitive Neuroscience
A winner-take-all mechanism based on presynaptic inhibition feedback
Neural Computation
1994 Special Issue: Winner-take-all networks for physiological models of competitive learning
Neural Networks - Special issue: models of neurodynamics and behavior
Neural processing in the subsecond time range in the temporal cortex
Neural Computation
Category learning through multimodality sensing
Neural Computation
Selectively grouping neurons in recurrent networks of lateral inhibition
Neural Computation
The development of cortical models to enable neural-based cognitive architectures
Computational models for neuroscience
On the Computational Power of Winner-Take-All
Neural Computation
Derivation and Analysis of Basic Computational Operations of Thalamocortical Circuits
Journal of Cognitive Neuroscience
Neural Networks - Special issue: Computational theories of the functions of the hippocampus
Engines of the brain: the computational instruction set of human cognition
AI Magazine - Special issue on achieving human-level AI through integrated systems and research
State-dependent computation using coupled recurrent networks
Neural Computation
Competitive stdp-based spike pattern learning
Neural Computation
Computation with spikes in a winner-take-all network
Neural Computation
Brain derived vision algorithm on high performance architectures
International Journal of Parallel Programming
A biologically plausible winner-takes-all architecture
ICIC'09 Proceedings of the Intelligent computing 5th international conference on Emerging intelligent computing technology and applications
Competition through selective inhibitory synchrony
Neural Computation
A self-organized neural comparator
Neural Computation
Effects of ltp on response selectivity of simulated cortical neurons
Journal of Cognitive Neuroscience
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Simulations were performed of physiological interactions among excitatory and inhibitory neurons in anatomically realistic local-circuit architectures modeled after hippocampal field CA1. The simulated circuitry consists of several excitatory neurons jointly innervating and receiving feedback from a common inhibitory interneuron. Excitatory cells in the simulation receive input during a cycle of naturally-occurring rhythmic activity (the hippocampal theta rhythm), and the neuron receiving the most input activation is the first to reach its spiking threshold. Spiking excites the inhibitory cell, which in turn prevents other cells from responding. The result is the natural generation of a simple competitive or ''winner-take-all'' (WTA) mechanism, allowing only the most strongly-activated cell in a group or ''patch'' to respond with spiking activity. Formal mathematical characterization of the mechanism reveals specific physiological characteristics of the input to the network, which enable it to closely approximate an ideal winner-take-all mechanism. Unlike other, more abstract WTA mechanisms that have been proposed, the parameters of this biologically-derived WTA mechanism can be directly related to specific physiological and anatomical features of particular cortical circuits.