Neuromorphic computing has come to refer to a variety of brain-inspired computers, devices,
and models that contrast the pervasive von Neumann computer architecture. This …

We propose an analog integrated circuit that implements a resonate-and-fire neuron (RFN)
model based on the Volterra system. The RFN model is a simple spiking neuron model that …

Synchrony detection between burst and non-burst spikes is known to be one functional
example of depressing synapses. Kanazawa et al. demonstrated synchrony detection with …

We propose an analog integrated circuit that implements a resonate-and-fire neuron (RFN)
model based on the Lotka-Volterra (LV) system. The RFN model is a spiking neuron model …

This paper describes a thermal-noise-exploiting single-electron majority logic circuit. The
circuit is based on a single-electron majority logic circuit using an irreversible single-electron …

This work proposes a Spiking Neural Network, SNN, based on a nanoelectronic spiking
neuron–as building block–and a 2D-mesh network-on-chip, NoC—as interconnect …

I briefly review a neuromorphic complementary metal-oxide semiconductor (CMOS) family—
a set of elementary neuromorphic circuits that has recently been developed—and discuss its …

A continuous-valued discrete-time Hopfield neural network with synaptic depression
(CDHSD) is constructed. We prove that the fixed point of CDHSD is the same as that of a …

Associative memory (AM) is a very important part of the theory of neural networks. Although
the Hebbian learning rule is always used to model the associative memory, it easily leads to …

In this paper, a cellular neural network with depressing synapses for contrast-invariant
pattern classification and synchrony detection is presented, starting from the impulse model …