Introduction to the theory of neural computation
Introduction to the theory of neural computation
On contraction analysis for non-linear systems
Automatica (Journal of IFAC)
On the Computational Power of Winner-Take-All
Neural Computation
Biophysics of Computation: Information Processing in Single Neurons (Computational Neuroscience Series)
Stable concurrent synchronization in dynamic system networks
Neural Networks
State-dependent computation using coupled recurrent networks
Neural Computation
Locally excitatory globally inhibitory oscillator networks
IEEE Transactions on Neural Networks
Collective stability of networks of winner-take-all circuits
Neural Computation
| Hi-index | 0.00 |
Models of cortical neuronal circuits commonly depend on inhibitory feedback to control gain, provide signal normalization, and selectively amplify signals using winner-take-all (WTA) dynamics. Such models generally assume that excitatory and inhibitory neurons are able to interact easily because their axons and dendrites are colocalized in the same small volume. However, quantitative neuroanatomical studies of the dimensions of axonal and dendritic trees of neurons in the neocortex show that this colocalization assumption is not valid. In this letter, we describe a simple modification to the WTA circuit design that permits the effects of distributed inhibitory neurons to be coupled through synchronization, and so allows a single WTA to be distributed widely in cortical space, well beyond the arborization of any single inhibitory neuron and even across different cortical areas. We prove by nonlinear contraction analysis and demonstrate by simulation that distributed WTA subsystems combined by such inhibitory synchrony are inherently stable. We show analytically that synchronization is substantially faster than winner selection. This circuit mechanism allows networks of independent WTAs to fully or partially compete with other.