Jonathan Tapson
54 papers 1K citations h-index 10 2006–2026
2022 Roadmap on Neuromorphic Computing and Engineering
Modern computation based on the von Neumann architecture is today a mature cutting-edge science. In the Von Neumann architecture, processing and memory units are implemented as separate blocks...
Biologically-inspired Salience Affected Artificial Neural Network (SANN)
In this paper we introduce a novel Salience Affected Artificial Neural Network (SANN) that models the way neuromodulators such as dopamine and noradrenaline affect neural dynamics in the human...
A neuromorphic hardware architecture using the Neural Engineering Framework for pattern recognition
We present a hardware architecture that uses the Neural Engineering Framework (NEF) to implement large-scale neural networks on Field Programmable Gate Arrays (FPGAs) for performing pattern...
Fast, simple and accurate handwritten digit classification by training shallow neural network classifiers with the 'extreme learning machine' algorithm
Recent advances in training deep (multi-layer) architectures have inspired a renaissance in neural network use. For example, deep convolutional networks are becoming the default option for...
A Stochastic Approach to STDP
We present a digital implementation of the Spike Timing Dependent Plasticity (STDP) learning rule. The proposed digital implementation consists of an exponential decay generator array and a STDP...
FPGA Implementation of the CAR Model of the Cochlea
The front end of the human auditory system, the cochlea, converts sound signals from the outside world into neural impulses transmitted along the auditory pathway for further processing. The...
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...
A neuromorphic hardware framework based on population coding
In the biological nervous system, large neuronal populations work collaboratively to encode sensory stimuli. These neuronal populations are characterised by a diverse distribution of tuning...
Explicit Computation of Input Weights in Extreme Learning Machines
We present a closed form expression for initializing the input weights in a multi-layer perceptron, which can be used as the first step in synthesis of an Extreme Learning Ma-chine. The expression...
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 TAB framework utilises the principles of neural population...
Learning the Pseudoinverse Solution to Network Weights
The last decade has seen the parallel emergence in computational neuroscience and machine learning of neural network structures which spread the input signal randomly to a higher dimensional...
A Reconfigurable Mixed-signal Implementation of a Neuromorphic ADC
We present a neuromorphic Analogue-to-Digital Converter (ADC), which uses integrate-and-fire (I&F) neurons as the encoders of the analogue signal, with modulated inhibitions to decohere the...
Learning ELM network weights using linear discriminant analysis
We present an alternative to the pseudo-inverse method for determining the hidden to output weight values for Extreme Learning Machines performing classification tasks. The method is based on...
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),...
Salience-Affected Neural Networks
We present a simple neural network model which combines a locally-connected feedforward structure, as is traditionally used to model inter-neuron connectivity, with a layer of undifferentiated...