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 …

The ability to carry out signal processing, classification, recognition, and computation in
artificial spiking neural networks (SNNs) is mediated by their synapses. In particular, through …

A novel fully floating memristor circuit that accounts for the Spike Timing-Dependent
Plasticity (STDP) mechanism is presented in this paper. This proposed circuit does not need …

Neuromorphic hardware platforms have demonstrated significant promise in cognitive tasks
such as visual processing and classification. These platforms usually consist of several …

Abstract Triplet-based Spike Timing Dependent Plasticity (TSTDP) is a powerful synaptic
plasticity rule that acts beyond conventional pair-based STDP (PSTDP). Here, the TSTDP is …

Synapse plays an important role in learning in a neural network; the learning rules that
modify the synaptic strength based on the timing difference between the preand …

Cortical circuits in the brain have long been recognised for their information processing
capabilities and have been studied both experimentally and theoretically via spiking neural …

This paper presents low cost, accurate and configurable digital circuits for pair-based and
triplet-based STDP learning models, and the proposed architectures are compared with the …

Spike-Timing Dependent Plasticity (STDP) is believed to play an important role in learning
and the formation of computational function in the brain. The classical model of STDP which …

The Bienenstock-Cooper-Munro (BCM) and Spike Timing-Dependent Plasticity (STDP) rules
are two experimentally verified form of synaptic plasticity where the alteration of synaptic …