This paper proposes a neuromorphic platform for on-FPGA online spike timing dependant
plasticity (STDP) learning, based on the COordinate Rotation DIgital Computer (CORDIC) …

Modern machine learning models are beginning to rival human performance on some
realistic object recognition tasks, but we still lack a full understanding of how the human …

This report provides a wide-angle survey on computational paradigms which have a
possible bearing on the development of unconventional computational substrates and …

Hardware implementation of spiking neural networks (SNN) has been the focus of many
previous works due to its higher execution speed. A block-based SNN architecture with a …

At the early stages of their exploration, spiking neural networks were considered mainly as
plausible models of biological neuronal ensembles, but then it became clear that they can …

Since its inception, resistive random access memory (RRAM) has widely been regarded as
a promising technology, not only for its potential to revolutionize non-volatile data storage by …

In the field of neuromorphic systems, Spiking Neural Networks (SNNs) present notable
advantages in energy conservation and real-time learning. This paper proposes anefficient …

This paper proposes a scalable hardware architecture for block-based spiking neural
networks utilizing a multiplierless spiking neuron model. These blocks were implemented as …

Deep reinforcement learning which involved reinforcement learning with artificial neural
networks allows an agent to take the best possible actions in a virtual environment to …

Human and animal binaural hearing systems are able take advantage of a variety of cues to
localise sound-sources in a 3D space using only two sensors. This work presents a bionic …