Learning from instructions or demonstrations is a fundamental property of our brain
necessary to acquire new knowledge and develop novel skills or behavioral patterns. This …

Electrophysiological connectivity patterns in cortex often have a few strong connections,
which are sometimes bidirectional, among a lot of weak connections. To explain these …

Recent results about spike-timing-dependent plasticity (STDP) in recurrently connected
neurons are reviewed, with a focus on the relationship between the weight dynamics and …

This paper presents a synaptic weight association training (SWAT) algorithm for spiking
neural networks (SNNs). SWAT merges the Bienenstock–Cooper—Munro (BCM) learning …

Based on the authors' groundbreaking research, Automated EEG-Based Diagnosis of
Neurological Disorders: Inventing the Future of Neurology presents a research ideology, a …

Spiking Neural Networks (SNNs) are considered to be the third generation of neural
networks, and have proved more powerful than classical artificial neural networks from the …

It is widely believed that sensory and motor processing in the brain is based on simple
computational primitives rooted in cellular and synaptic physiology. However, many gaps …

Accurate perception of the order of occurrence of sensory information is critical for the
building up of coherent representations of the external world from ongoing flows of sensory …

In many cases, neurons process information carried by the precise timing of spikes. Here we
show how neurons can learn to generate specific temporally-precise output spikes in …

Recently, spiking neural networks (SNNs) have been shown capable of approximating the
dynamics of biological neuronal networks, and of being trainable by biologically-plausible …