Short-term plasticity (STP) comprises several rapid synaptic processes that operate on
millisecond-to-minute timescales and modulate synaptic efficacy in an activity-dependent …

Neuromorphic architectures offer great promise for achieving computation capacities
beyond conventional Von Neumann machines. The essential elements for achieving this …

General results from statistical learning theory suggest to understand not only brain
computations, but also brain plasticity as probabilistic inference. But a model for that has …

Artificial neural networks are often trained by using the back propagation algorithm to
compute the gradient of an objective function with respect to the synaptic strengths. For a …

Short-term synaptic plasticity (STP) is widely thought to play an important role in information
processing. This major function of STP has recently been challenged, however, by several …

The synapse is typically viewed as a single compartment, which acts as a linear gain
controller on incoming input. Traditional plasticity rules enable this gain control to be …

We have examined a role of dynamic synapses in the stochastic Hopfield-like network
behavior. Our results demonstrate an appearance of a novel phase characterized by quick …

This paper proposes a physical model involving the key structures within the neural
cytoskeleton as major players in molecular-level processing of information required for …

Monoclinic HfO 2− x N x has been incorporated into two-terminal devices exhibiting either
memristor or selector operation depending on the controlled inclusion/suppression of mobile …

We discuss in this short survey article some current mathematical models from
neurophysiology for the computational units of biological neural systems: neurons and …