Simulation of neural behavior on digital architectures often requires the solution of ordinary differential equations (ODEs) at each step of the simulation. For some neural models, this is a significant computational burden, so efficiency is importan...
In this letter, we present the dynamical equation and corresponding artificial recurrent neural network for computing the Drazin inverse for arbitrary square real matrix, without any restriction on its eigenvalues. Conditions that ensure the stabilit...
In higher animals, complex and robust behaviors are produced by the microscopic details of large structured ensembles of neurons. I describe how the emergent computational dynamics of a biologically based neural network generates a robust natural sol...
Newton methods can be applied in many supervised learning approaches. However, for large-scale data, the use of the whole Hessian matrix can be time-consuming. Recently, subsampled Newton methods have been proposed to reduce the computational time by...
The abundance of real-world data and limited labeling budget calls for active learning, an important learning paradigm for reducing human labeling efforts. Many recently developed active learning algorithms consider both uncertainty and representativ...
Primary visual cortical complex cells are thought to serve as invariant feature detectors and to provide input to higher cortical areas. We propose a single model for learning the connectivity required by complex cells that integrates two factors tha...
Neural network models of early sensory processing typically reduce the dimensionality of streaming input data. Such networks learn the principal subspace, in the sense of principal component analysis, by adjusting synaptic weights according to activi...
Supervised learning in recurrent neural networks involves two processes: the neuron activity from which gradients are estimated and the process on connection parameters induced by these measurements. A problem such algorithms must address is how to b...
Perception of supraliminal stimuli might in general be reflected in bursts of action potentials (spikes), and their memory traces could be formed through spike-timing-dependent plasticity (STDP). Memory traces for subliminal stimuli might be formed i...
Because different parts of the brain have rich interconnections, it is not possible to model small parts realistically in isolation. However, it is also impractical to simulate large neural systems in detail. This article outlines a new approach to m...