The modelling of large systems of spiking neurons is computationally very demanding in
terms of processing power and communication. SpiNNaker-Spiking Neural Network …

This paper presents new theoretical results on the invariance and attractivity of memristor-
based cellular neural networks (MCNNs) with time-varying delays. First, sufficient conditions …

A compact neuromorphic nanodevice with inherent learning and memory properties
emulating those of biological synapses is the key to developing artificial neural networks …

In 1971, Leon O. Chua presented the formulation of a memristor, which was postulated as
the fourth circuit element in electrical circuit theory OCo one that could join the existing core …

Machine learning algorithms often rely on continuous updating of large matrices of “synaptic
weights” by local “Hebbian” rules. These rules generally involve a multiplication term, which …

Humans have used tools to transform raw resources into valued outputs ever since society
harnessed fire. The type of tool, amount of effort and form of energy required depends on the …

The computation goals of the digital computing world have been segmented into different
factions. The goals are no longer rooted in a purely speed/performance standpoint as added …

This paper focuses on connecting Human-Robot Interaction (HRI) studies and technology to
the field of Human Resource Development (HRD). Over centuries, with the industrial …

Cette thèse traite de mémoires associatives neuro-inspirées. Une extension des réseaux de
neurones récurrents et binaires introduits par Gripon et Berrou a été étudiée pour y accroître …

Field computation, an emerging computation technique, has inspired passion of intelligence
science research. A novel field computation model based on the magnetic field theory is …