Hardware implementations of spiking neurons can be extremely useful for a large variety of
applications, ranging from high-speed modeling of large-scale neural systems to real-time …

Implementing compact, low-power artificial neural processing systems with real-time on-line
learning abilities is still an open challenge. In this paper we present a full-custom mixed …

In this paper we present a very exciting overlap between emergent nanotechnology and
neuroscience, which has been discovered by neuromorphic engineers. Specifically, we are …

This paper introduces an event-driven feedforward categorization system, which takes data
from a temporal contrast address event representation (AER) sensor. The proposed system …

The paper reviews the progress of neuromorphic vision chip research in decades. It focuses
on two kinds of the neuromorphic vision chips: frame-driven (FD) and event-driven (ED) …

This paper describes CAVIAR, a massively parallel hardware implementation of a spike-
based sensing-processing-learning-actuating system inspired by the physiology of the …

The theoretical, practical and technical development of neural associative memories during
the last 40 years is described. The importance of sparse coding of associative memory …

Increasingly large deep learning architectures, such as Deep Belief Networks (DBNs) are
the focus of current machine learning research and achieve state-of-the-art results in …

This paper presents a new architecture, design flow, and field-programmable gate array
(FPGA) implementation analysis of a neuromorphic binaural auditory sensor, designed …

We present and characterize an analog VLSI network of 4 spiking neurons and 12
conductance-based synapses, implementing a silicon model of biophysical membrane …