The nervous systems converts the physical quantities sensed by its primary receptors into
trains of events that are then processed in the brain. The unmatched efficiency in information …

This paper presents a brief review of some analog hardware implementations of neural
networks. Several criteria for the classification of general neural networks implementations …

Most hardware neural networks have a basic competitive learning rule on top of a more
involved processing algorithm. This work highlights two basic learning rules/behavior …

This paper analyzes some aspects of the computational power of neural networks using
integer weights in a very restricted range. Using limited range integer values opens the road …

Neuromorphic computing mimics the neural activity of the brain through emulating spiking
neural networks. In numerous machine learning tasks, neuromorphic chips are expected to …

Neuromorphic chips embody computational principles operating in the nervous system, into
microelectronic devices. In this domain it is important to identify computational primitives that …

This paper focuses on the circuit aspects required for an on-chip, on-line system on chip
large-scale field-programmable analog array learning for vector-matrix multiplier (VMM)+ …

This paper presents a learning algorithm for a vector-matrix multiplier (VMM)+ k-winner-take-
all (WTA) classifier one-layer architecture on a large-scale field programmable analog array …

Neuromorphic systems are inspired by the structure, function and plasticity of biological
nervous systems. This field is evolving a new era in computing with a great promise for …

Memristive devices have been shown to exhibit slow and stochastic resistive switching
behavior under low-voltage, low-current operating conditions. Here we explore such …