The computational power of formal models for networks of spiking neurons is compared with
that of other neural network models based on McCulloch Pitts neurons (ie, threshold gates), …

A key challenge for neural modeling is to explain how a continuous stream of multimodal input
from a rapidly changing environment can be processed by stereotypical recurrent circuits …

Recurrently connected networks of spiking neurons underlie the astounding information
processing capabilities of the brain. Yet in spite of extensive research, how they can learn …

A conspicuous ability of the brain is to seamlessly assimilate and process spatial and
temporal features of sensory stimuli. This ability is indispensable for the recognition of natural …

Recurrent networks of spiking neurons (RSNNs) underlie the astounding computing and
learning capabilities of the brain. But computing and learning capabilities of RSNN models …

Modern computation based on von Neumann architecture is now a mature cutting-edge
science. In the von Neumann architecture, processing and memory units are implemented as …

This article initiates a rigorous theoretical analysis of the computational power of circuits that
employ modules for computing winner-take-all. Computational models that involve …

A unified and powerful approach is presented for devising polynomial approximation
schemes for many strongly NP-complete problems. Such schemes consist of families of …

We analyze in this article the significance of the edge of chaos for real-time computations in
neural microcircuit models consisting of spiking neurons and dynamic synapses. We find …

It has previously been shown that generic cortical microcircuit models can perform complex
real-time computations on continuous input streams, provided that these computations can …