Stochastic Computations in Cortical Microcircuit Models

Stochastic Computations in Cortical Microcircuit Models

Experimental data from neuroscience suggest that a substantial amount of knowledge is stored in the brain in the form of probability distributions over network states and trajectories of network states. We provide a theoretical foundation for this hypothesis by showing that even very detailed models for cortical microcircuits, with data-based diverse nonlinear neurons and synapses, have a stati...

Fading memory and kernel properties of generic cortical microcircuit models.

It is quite difficult to construct circuits of spiking neurons that can carry out complex computational tasks. On the other hand even randomly connected circuits of spiking neurons can in principle be used for complex computational tasks such as time-warp invariant speech recognition. This is possible because such circuits have an inherent tendency to integrate incoming information in such a wa...

Mixed Signal VLSI Circuit Implementation of the Cortical Microcircuit Models

Structure of cortical microcircuit theory.

Recent experimental and theoretical investigations have made considerable advances in three major areas relating to the structural basis of quantitative cortical microcircuit theory. The first concerns the nature of the cellular units, encompassing the increasingly precise identification and progressively more complete listing of the individual cellular species that constitute the various corti...

Towards a “canonical” agranular cortical microcircuit

Based on regularities in the intrinsic microcircuitry of cortical areas, variants of a "canonical" cortical microcircuit have been proposed and widely adopted, particularly in computational neuroscience and neuroinformatics. However, this circuit is founded on striate cortex, which manifests perhaps the most extreme instance of cortical organization, in terms of a very high density of cells in ...