1 code implementation • 16 Jul 2019 • Mark D. McDonnell, Hesham Mostafa, Runchun Wang, Andre van Schaik
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)
no code implementations • 23 May 2018 • Chetan Singh Thakur, Jamal Molin, Gert Cauwenberghs, Giacomo Indiveri, Kundan Kumar, Ning Qiao, Johannes Schemmel, Runchun Wang, Elisabetta Chicca, Jennifer Olson Hasler, Jae-sun Seo, Shimeng Yu, Yu Cao, André van Schaik, Ralph Etienne-Cummings
Neuromorphic engineering (NE) encompasses a diverse range of approaches to information processing that are inspired by neurobiological systems, and this feature distinguishes neuromorphic systems from conventional computing systems.
no code implementations • 8 Mar 2018 • Runchun Wang, Chetan Singh Thakur, Andre van Schaik
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.
no code implementations • 13 Mar 2016 • Runchun Wang, Chetan Singh Thakur, Tara Julia Hamilton, Jonathan Tapson, André van Schaik
The decay generator will then generate an exponential decay, which will be used by the STDP adaptor to perform the weight adaption.
no code implementations • 3 Sep 2015 • Ying Xu, Chetan Singh Thakur, Tara Julia Hamilton, Jonathan Tapson, Runchun Wang, Andre van Schaik
The architecture consists of an analogue chip and a control module.
no code implementations • 3 Sep 2015 • Runchun Wang, Chetan Singh Thakur, Tara Julia Hamilton, Jonathan Tapson, Andre van Schaik
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.
1 code implementation • 21 Jul 2015 • Runchun Wang, Chetan Singh Thakur, Tara Julia Hamilton, Jonathan Tapson, Andre van Schaik
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.
no code implementations • 10 Jul 2015 • Chetan Singh Thakur, Runchun Wang, Tara Julia Hamilton, Jonathan Tapson, Andre van Schaik
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.
no code implementations • 11 May 2015 • Chetan Singh Thakur, Runchun Wang, Saeed Afshar, Gregory Cohen, Tara Julia Hamilton, Jonathan Tapson, Andre van Schaik
We propose a sign-based online learning (SOL) algorithm for a neuromorphic hardware framework called Trainable Analogue Block (TAB).
no code implementations • 2 Mar 2015 • Chetan Singh Thakur, Tara Julia Hamilton, Runchun Wang, Jonathan Tapson, André van Schaik
These neuronal populations are characterised by a diverse distribution of tuning curves, ensuring that the entire range of input stimuli is encoded.
no code implementations • 13 Jun 2013 • Saeed Afshar, Gregory Cohen, Runchun Wang, Andre van Schaik, Jonathan Tapson, Torsten Lehmann, Tara Julia Hamilton
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.