Spike frequency adaptation: bridging neural models and neuromorphic applications
The human brain's unparalleled efficiency in executing complex cognitive tasks stems from
neurons communicating via short, intermittent bursts or spikes. This has inspired Spiking …
neurons communicating via short, intermittent bursts or spikes. This has inspired Spiking …
A super-efficient TinyML processor for the edge metaverse
Although the Metaverse is becoming a popular technology in many aspects of our lives,
there are some drawbacks to its implementation on clouds, including long latency, security …
there are some drawbacks to its implementation on clouds, including long latency, security …
Event-driven spectrotemporal feature extraction and classification using a silicon cochlea model
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 …
cochlear system on a Field Programmable Gate Array (FPGA). It consists of a pair of the …
Real-time anomaly detection using hardware-based unsupervised spiking neural network (TinySNN)
We present TinySNN, a novel unsupervised spiking neural network hardware designed for
real-time anomaly detection. TinySNN provides an energy-efficient edge computing solution …
real-time anomaly detection. TinySNN provides an energy-efficient edge computing solution …
Noise Filtering Benchmark for Neuromorphic Satellites Observations
Event cameras capture sparse, asynchronous brightness changes which offer high temporal
resolution, high dynamic range, low power consumption, and sparse data output. These …
resolution, high dynamic range, low power consumption, and sparse data output. These …
Efficient Hardware Implementation of a Multi-Layer Gradient-Free Online-Trainable Spiking Neural Network on FPGA
This paper presents an efficient hardware implementation of the recently proposed
Optimised Deep Event-driven Spiking Neural Network Architecture (ODESA). ODESA is the …
Optimised Deep Event-driven Spiking Neural Network Architecture (ODESA). ODESA is the …
An FPGA Implementation of An Event-Driven Unsupervised Feature Extraction Algorithm for Pattern Recognition
This paper presents the Field Programmable Gate Array (FPGA) implementation of an event-
driven unsupervised Feature Extraction using Adaptive Selection Thresholds (FEAST) …
driven unsupervised Feature Extraction using Adaptive Selection Thresholds (FEAST) …
Density Invariant Contrast Maximization for Neuromorphic Earth Observations
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 …
estimate the motion parameters of the camera and generate high-contrast images. However …
A neuromorphic architecture for reinforcement learning from real-valued observations
Reinforcement Learning (RL) provides a powerful framework for decision-making in
complex environments. However, implementing RL in hardware-efficient and bio-inspired …
complex environments. However, implementing RL in hardware-efficient and bio-inspired …
Robust spiking attractor networks with a hard winner-take-all neuron circuit
Attractor networks are widely understood to be a re-occurring primitive that underlies
cognitive function. Stabilising activity in spiking attractor networks however remains a difficult …
cognitive function. Stabilising activity in spiking attractor networks however remains a difficult …