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Found 10 papers, 4 papers with code
Density Invariant Contrast Maximization for Neuromorphic Earth Observations
This is to ensure that the contrast is only high around the correct motion parameters.
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 Optimized 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 an error measure.
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).
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.
