Patterns of local connectivity in the neocortex
Neural Computation
A canonical neural circuit for cortical nonlinear operations
Neural Computation
Change-based inference in attractor nets: Linear analysis
Neural Computation
Synaptic information transfer in computer models of neocortical columns
Journal of Computational Neuroscience
BVAI'05 Proceedings of the First international conference on Brain, Vision, and Artificial Intelligence
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We have used microanatomy derived from single neurons, and in vivo intracellular recordings to develop a simplified circuit of the visual cortex. The circuit explains the intracellular responses to pulse stimulation in terms of the interactions between three basic populations of neurons, and reveals the following features of cortical processing that are important to computational theories of neocortex. First, inhibition and excitation are not separable events. Activation of the cortex inevitably sets in motion a sequence of excitation and inhibition in every neuron. Second, the thalamic input does not provide the major excitation arriving at any neuron. Instead the intracortical excitatory connections provide most of the excitation. Third, the time evolution of excitation and inhibition is far longer than the synaptic delays of the circuits involved. This means that cortical processing cannot rely on precise timing between individual synaptic inputs.