Neuromorphic computing has emerged as a better alternative for developing next-
generation artificial intelligent systems. Resistive Random Access Memory (RRAM) have …

A fully-unsupervised learning algorithm for reaching self-organization in neuromorphic
architectures is provided in this work. We experimentally demonstrate spike-timing …

Brain-inspired learning mechanisms, eg, spike timing dependent plasticity (STDP), enable
agile, and fast on-the-fly adaptation capability in a spiking neural network. When …

The implementation of inference (ie, computing posterior probabilities) in Bayesian networks
using a conventional computing paradigm turns out to be inefficient in terms of energy, time …

Machine learning algorithms have proven unprecedented performance to solve many real-
world detection and classification tasks, for example, in image or speech recognition …

Resistive random access memories (RRAM) are one of the main constituents of the class of
memristive technologies that are today considered very promising in semiconductor industry …

This open access book offers a timely and comprehensive review of the field of neurotronics.
Gathering cutting-edge contributions from neuroscientists, biologists, psychologists, as well …

We present a novel integration technology for replacing damaged microcircuits in the rat
brain with electronic neuronal networks. This technology will allow simulating important …

As the demand for energy-efficient cognitive computing keeps increasing, the conventional
von Neumann architecture becomes power/energy prohibitive. Brain-inspired, or …

Identification of optimal transcription factor expression patterns to direct cellular
differentiation along a desired pathway presents significant challenges. We demonstrate …