Search Results for author:
Found 14 papers, 3 papers with code
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.
Single-bit-per-weight deep convolutional neural networks without batch-normalization layers for embedded systems
We found, following experiments with wide residual networks applied to the ImageNet, CIFAR 10 and CIFAR 100 image classification datasets, that BN layers do not consistently offer a significant advantage.
Ranked #94 on
Image Classification
on CIFAR-100
(using extra training data)
Star Tracking using an Event Camera
Star trackers are primarily optical devices that are used to estimate the attitude of a spacecraft by recognising and tracking star patterns.
An FPGA-based Massively Parallel Neuromorphic Cortex Simulator
This paper presents a massively parallel and scalable neuromorphic cortex simulator designed for simulating large and structurally connected spiking neural networks, such as complex models of various areas of the cortex.
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.
A Reconfigurable Mixed-signal Implementation of a Neuromorphic ADC
The architecture consists of an analogue chip and a control module.
A compact aVLSI conductance-based silicon neuron
We present an analogue Very Large Scale Integration (aVLSI) implementation that uses first-order lowpass filters to implement a conductance-based silicon neuron for high-speed neuromorphic systems.
A neuromorphic hardware architecture using the Neural Engineering Framework for pattern recognition
The architecture is not limited to handwriting recognition, but is generally applicable as an extremely fast pattern recognition processor for various kinds of patterns such as speech and images.
A Trainable Neuromorphic Integrated Circuit that Exploits Device Mismatch
Additionally, we characterise each neuron and discuss the statistical variability of its tuning curve that arises due to random device mismatch, a desirable property for the learning capability of the TAB.
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.
