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Found 14 papers, 3 papers with code
A Neuromorphic Architecture for Reinforcement Learning from Real-Valued Observations
Reinforcement Learning (RL) provides a powerful framework for decision-making in complex environments.
Efficient 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 Optimized Deep Event-driven Spiking Neural Network Architecture (ODESA).
Density Invariant Contrast Maximization for Neuromorphic Earth Observations
This is to ensure that the contrast is only high around the correct motion parameters.
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).
Astrometric Calibration and Source Characterisation of the Latest Generation Neuromorphic Event-based Cameras for Space Imaging
These techniques are vital in building event-based space imaging systems capable of real-world space situational awareness tasks.
An optimised deep spiking neural network architecture without gradients
Through the use of simple local adaptive selection thresholds at each node, the network rapidly learns to appropriately allocate its neuronal resources at each layer for any given problem without using a real-valued error measure.
Event-based Object Detection and Tracking for Space Situational Awareness
In this work, we present optical space imaging using an unconventional yet promising class of imaging devices known as neuromorphic event-based sensors.
Event-based Feature Extraction Using Adaptive Selection Thresholds
A novel heuristic method for network size selection is proposed which makes use of noise events and their feature representations.
EMNIST: an extension of MNIST to handwritten letters
The MNIST dataset has become a standard benchmark for learning, classification and computer vision systems.
Ranked #6 on
Image Classification
on EMNIST-Digits
Investigation of event-based memory surfaces for high-speed tracking, unsupervised feature extraction and object recognition
This variance motivated the investigation of event-based decaying memory surfaces in comparison to time-based decaying memory surfaces to capture the temporal aspect of the event-based data.
An Online Learning Algorithm for Neuromorphic Hardware Implementation
We propose a sign-based online learning (SOL) algorithm for a neuromorphic hardware framework called Trainable Analogue Block (TAB).
Turn Down that Noise: Synaptic Encoding of Afferent SNR in a Single Spiking Neuron
We have added a simplified neuromorphic model of Spike Time Dependent Plasticity (STDP) to the Synapto-dendritic Kernel Adapting Neuron (SKAN).
Racing to Learn: Statistical Inference and Learning in a Single Spiking Neuron with Adaptive Kernels
This paper describes the Synapto-dendritic Kernel Adapting Neuron (SKAN), a simple spiking neuron model that performs statistical inference and unsupervised learning of spatiotemporal spike patterns.
The Ripple Pond: Enabling Spiking Networks to See
In this paper we present the biologically inspired Ripple Pond Network (RPN), a simply connected spiking neural network that, operating together with recently proposed PolyChronous Networks (PCN), enables rapid, unsupervised, scale and rotation invariant object recognition using efficient spatio-temporal spike coding.
