464 citations · 502 across the 13 of their papers we have counts for
16 papers
A Neuromorphic Architecture for Reinforcement Learning from Real-Valued Observations
Sergio F. Chevtchenko, Yeshwanth Bethi, Teresa B. Ludermir +1
Reinforcement Learning (RL) provides a powerful framework for decision-making in complex environments. However, implementing RL in hardware-efficient and bio-inspired ways remains…
Efficient Implementation of a Multi-Layer Gradient-Free Online-Trainable Spiking Neural Network on FPGA
Ali Mehrabi, Yeshwanth Bethi, André van Schaik +2
This paper presents an efficient hardware implementation of the recently proposed Optimized Deep Event-driven Spiking Neural Network Architecture (ODESA). ODESA is the first networ…
Density Invariant Contrast Maximization for Neuromorphic Earth Observations
Sami Arja, Alexandre Marcireau, Richard L. Balthazor +3
Contrast maximization (CMax) techniques are widely used in event-based vision systems to estimate the motion parameters of the camera and generate high-contrast images. However, th…
Event-driven Spectrotemporal Feature Extraction and Classification using a Silicon Cochlea Model
Ying Xu, Samalika Perera, Yeshwanth Bethi +2
This paper presents a reconfigurable digital implementation of an event-based binaural cochlear system on a Field Programmable Gate Array (FPGA). It consists of a pair of the Casca…
Astrometric Calibration and Source Characterisation of the Latest Generation Neuromorphic Event-based Cameras for Space Imaging
Nicholas Owen Ralph, Alexandre Marcireau, Saeed Afshar +3
As an emerging approach to space situational awareness and space imaging, the practical use of an event-based camera in space imaging for precise source analysis is still in its in…
An optimised deep spiking neural network architecture without gradients
Yeshwanth Bethi, Ying Xu, Gregory Cohen +2
We present an end-to-end trainable modular event-driven neural architecture that uses local synaptic and threshold adaptation rules to perform transformations between arbitrary spa…