We propose a compact, low power VLSI network of spiking neurons which can learn to classify complex patterns of mean firing rates on–line and in real–time. The network of int...
Point process encoding models provide powerful statistical methods for understanding the responses of neurons to sensory stimuli. Although these models have been successfully appl...
Large networks of spiking neurons show abrupt changes in their collective dynamics resembling phase transitions studied in statistical physics. An example of this phenomenon is th...
Randomly connected recurrent neural circuits have proven to be very powerful models for online computations when a trained memoryless readout function is appended. Such Reservoir ...
Benjamin Schrauwen, Lars Buesing, Robert A. Legens...
For unsupervised clustering in a network of spiking neurons we develop a temporal encoding of continuously valued data to obtain arbitrary clustering capacity and precision with a...
Abstract. Stimulus4ependent changes have been observed in the correlations between the spike trains of simultaneously-recorded pairs of neurons from the auditory cortex of marmoset...
We discuss properties of high order neurons in competitive learning. In such neurons, geometric shapes replace the role of classic `point' neurons in neural networks. Complex ...
Abstract. We describe a set of preliminary experiments to evolve spiking neural controllers for a vision-based mobile robot. All the evolutionary experiments are carried out on phy...
In this paper, we make some analysis on the FitzHugh-Nagumo model and improve it to build a neural network, and the network is used to implement visual selection and attention shif...
We review the structure of cerebral cortex to find out the number of neurons and synapses and its modular structure. The organization of these neurons is then studied and mapped on...