Brain rhythms emerge from synchronization among interconnected spiking neurons. Key properties of such rhythms can be gleaned from the phase-resetting curve (PRC). Inferring the PRC and developing a systematic phase reduction theory for large-scale b...
International journal of neural systems
Jul 16, 2022
Spiking neural P systems (abbreviated as SNP systems) are models of computation that mimic the behavior of biological neurons. The spiking neural P systems with communication on request (abbreviated as SNQP systems) are a recently developed class of ...
Journal of computational neuroscience
Jul 14, 2022
Networks of spiking neurons with adaption have been shown to be able to reproduce a wide range of neural activities, including the emergent population bursting and spike synchrony that underpin brain disorders and normal function. Exact mean-field mo...
International journal of neural systems
Jul 12, 2022
Spiking neural membrane systems are models of computation inspired by the natural functioning of the brain using the concepts of neurons and synapses, and represent a way of building computational systems of a biological inspiration. A variant of suc...
Neural networks : the official journal of the International Neural Network Society
Jul 11, 2022
Spiking neural networks (SNNs) transmit information through discrete spikes that perform well in processing spatial-temporal information. Owing to their nondifferentiable characteristic, difficulties persist in designing SNNs that deliver good perfor...
IEEE transactions on neural networks and learning systems
Jul 6, 2022
In contrast with our everyday experience using brain circuits, it can take a prohibitively long time to train a computational system to produce the correct sequence of outputs in the presence of a series of inputs. This suggests that something import...
Advanced materials (Deerfield Beach, Fla.)
Jul 3, 2022
The representation of external stimuli in the form of action potentials or spikes constitutes the basis of energy efficient neural computation that emerging spiking neural networks (SNNs) aspire to imitate. With recent evidence suggesting that inform...
Proceedings of the National Academy of Sciences of the United States of America
Jun 27, 2022
Understanding how the brain learns throughout a lifetime remains a long-standing challenge. In artificial neural networks (ANNs), incorporating novel information too rapidly results in catastrophic interference, i.e., abrupt loss of previously acquir...
Sequence learning, prediction and replay have been proposed to constitute the universal computations performed by the neocortex. The Hierarchical Temporal Memory (HTM) algorithm realizes these forms of computation. It learns sequences in an unsupervi...
The field of recurrent neural networks is over-populated by a variety of proposed learning rules and protocols. The scope of this work is to define a generalized framework, to move a step forward towards the unification of this fragmented scenario. I...