Artificial neural networks have been used as a powerful processing tool in various areas
such as pattern recognition, control, robotics, and bioinformatics. Their wide applicability has …

Dense crossbar arrays of non-volatile memory (NVM) devices represent one possible path
for implementing massively-parallel and highly energy-efficient neuromorphic computing …

Spiking neural networks (SNNs) aim to realize brain-inspired intelligence on neuromorphic
chips with high energy efficiency by introducing neural dynamics and spike properties. As …

What happens in our brain when we make a decision? What triggers a neuron to send out a
signal? What is the neural code? This textbook for advanced undergraduate and beginning …

Ca2+/calmodulin-dependent protein kinase II (CaMKII) and long-term potentiation (LTP)
were discovered within a decade of each other and have been inextricably intertwined ever …

Due to the increasing importance of artificial intelligence (AI), significant recent effort has
been devoted to the development of neuromorphic circuits that seek to emulate the energy …

This work is aimed to study experimental and theoretical approaches for searching effective
local training rules for unsupervised pattern recognition by high-performance memristor …

In spike-timing-dependent plasticity (STDP), the order and precise temporal interval
between presynaptic and postsynaptic spikes determine the sign and magnitude of long …

Classical Hebbian learning puts the emphasis on joint pre-and postsynaptic activity, but
neglects the potential role of neuromodulators. Since neuromodulators convey information …

On-line learning and recognition of spatio-and spectro-temporal data (SSTD) is a very
challenging task and an important one for the future development of autonomous machine …