16,279 research outputs found
Adaptive motor control and learning in a spiking neural network realised on a mixed-signal neuromorphic processor
Neuromorphic computing is a new paradigm for design of both the computing
hardware and algorithms inspired by biological neural networks. The event-based
nature and the inherent parallelism make neuromorphic computing a promising
paradigm for building efficient neural network based architectures for control
of fast and agile robots. In this paper, we present a spiking neural network
architecture that uses sensory feedback to control rotational velocity of a
robotic vehicle. When the velocity reaches the target value, the mapping from
the target velocity of the vehicle to the correct motor command, both
represented in the spiking neural network on the neuromorphic device, is
autonomously stored on the device using on-chip plastic synaptic weights. We
validate the controller using a wheel motor of a miniature mobile vehicle and
inertia measurement unit as the sensory feedback and demonstrate online
learning of a simple 'inverse model' in a two-layer spiking neural network on
the neuromorphic chip. The prototype neuromorphic device that features 256
spiking neurons allows us to realise a simple proof of concept architecture for
the purely neuromorphic motor control and learning. The architecture can be
easily scaled-up if a larger neuromorphic device is available.Comment: 6+1 pages, 4 figures, will appear in one of the Robotics conference
Live Demonstration: Neuromorphic Row-by-Row Multi-convolution FPGA Processor-SpiNNaker architecture for Dynamic-Vision Feature Extraction
In this demonstration a spiking neural network
architecture for vision recognition using an FPGA spiking
convolution processor, based on leaky integrate and fire neurons
(LIF) and a SpiNNaker board is presented. The network has
been trained with Poker-DVS dataset in order to classify the
four different card symbols. The spiking convolution processor
extracts features from images in form of spikes, computes by
one layer of 64 convolutions. These features are sent to an
OKAERtool board that converts from AER to 2-7 protocol
to be classified by a spiking neural network deployed on a
SpiNNaker platform
Whetstone Trained Spiking Deep Neural Networks to Spiking Neural Networks
A deep neural network is a non-spiking artificial neural network which uses multiple structured layers to extract features from the input. Spiking neural networks are another type of artificial neural network which closely mimic biology with time dependent pulses to transmit information. Whetstone is a training algorithm for spiking deep neural networks. It modifies the back propagation algorithm, typically used in deep learning, to train a spiking deep neural network, by converting the activation function found in deep neural networks into a threshold used by a spiking neural network. This work converts a spiking deep neural network trained from Whetstone to a traditional spiking neural network in the TENNLab framework. This conversion decomposes the dot product operation found in the convolutional layer of spiking deep neural networks to synapse connections between neurons in traditional spiking neural networks. The conversion also redesigns the neuron and synapse structure in the convolutional layer to trade time for space. A new architecture is created in the TENNLab framework using traditional spiking neural networks, which behave the same as the spiking deep neural network trained by Whetstone before conversion. This new architecture verifies the converted spiking neural network behaves the same as the original spiking deep neural network. This work can convert networks to run on other architectures from TENNLab, and this allows networks from those architectures to be trained with back propagation from Whetstone. This expands the variety of training techniques available to the TENNLab architectures
Self-Learning Fuzzy Spiking Neural Network as a Nonlinear Pulse-Position Threshold Detection Dynamic System Based on Second-Order Critically Damped Response Units
Architecture and learning algorithm of self-learning spiking neural network in fuzzy clustering task are
outlined. Fuzzy receptive neurons for pulse-position transformation of input data are considered. It is proposed to
treat a spiking neural network in terms of classical automatic control theory apparatus based on the Laplace
transform. It is shown that synapse functioning can be easily modeled by a second order damped response unit.
Spiking neuron soma is presented as a threshold detection unit. Thus, the proposed fuzzy spiking neural network
is an analog-digital nonlinear pulse-position dynamic system. It is demonstrated how fuzzy probabilistic and
possibilistic clustering approaches can be implemented on the base of the presented spiking neural network
A Digital Neuromorphic Architecture Efficiently Facilitating Complex Synaptic Response Functions Applied to Liquid State Machines
Information in neural networks is represented as weighted connections, or
synapses, between neurons. This poses a problem as the primary computational
bottleneck for neural networks is the vector-matrix multiply when inputs are
multiplied by the neural network weights. Conventional processing architectures
are not well suited for simulating neural networks, often requiring large
amounts of energy and time. Additionally, synapses in biological neural
networks are not binary connections, but exhibit a nonlinear response function
as neurotransmitters are emitted and diffuse between neurons. Inspired by
neuroscience principles, we present a digital neuromorphic architecture, the
Spiking Temporal Processing Unit (STPU), capable of modeling arbitrary complex
synaptic response functions without requiring additional hardware components.
We consider the paradigm of spiking neurons with temporally coded information
as opposed to non-spiking rate coded neurons used in most neural networks. In
this paradigm we examine liquid state machines applied to speech recognition
and show how a liquid state machine with temporal dynamics maps onto the
STPU-demonstrating the flexibility and efficiency of the STPU for instantiating
neural algorithms.Comment: 8 pages, 4 Figures, Preprint of 2017 IJCN
SpiNNaker - A Spiking Neural Network Architecture
20 years in conception and 15 in construction, the SpiNNaker project has delivered the world’s largest neuromorphic computing platform incorporating over a million ARM mobile phone processors and capable of modelling spiking neural networks of the scale of a mouse brain in biological real time. This machine, hosted at the University of Manchester in the UK, is freely available under the auspices of the EU Flagship Human Brain Project. This book tells the story of the origins of the machine, its development and its deployment, and the immense software development effort that has gone into making it openly available and accessible to researchers and students the world over. It also presents exemplar applications from ‘Talk’, a SpiNNaker-controlled robotic exhibit at the Manchester Art Gallery as part of ‘The Imitation Game’, a set of works commissioned in 2016 in honour of Alan Turing, through to a way to solve hard computing problems using stochastic neural networks. The book concludes with a look to the future, and the SpiNNaker-2 machine which is yet to come
A parallel supercomputer implementation of a biological inspired neural network and its use for pattern recognition
Abstract : A parallel implementation of a large spiking neural network is proposed and evaluated. The
neural network implements the binding by synchrony process using the Oscillatory Dynamic Link Matcher
(ODLM). Scalability, speed and performance are compared for 2 implementations: Message Passing
Interface (MPI) and Compute Unified Device Architecture (CUDA) running on clusters of multicore
supercomputers and NVIDIA graphical processing units respectively. A global spiking list that represents
at each instant the state of the neural network is described. This list indexes each neuron that fires during the
current simulation time so that the influence of their spikes are simultaneously processed on all computing
units. Our implementation shows a good scalability for very large networks. A complex and large spiking
neural network has been implemented in parallel with success, thus paving the road towards real-life
applications based on networks of spiking neurons. MPI offers a better scalability than CUDA, while
the CUDA implementation on a GeForce GTX 285 gives the best cost to performance ratio. When running
the neural network on the GTX 285, the processing speed is comparable to the MPI implementation on
RQCHP’s Mammouth parallel with 64 notes (128 cores)
SpiNNaker - A Spiking Neural Network Architecture
20 years in conception and 15 in construction, the SpiNNaker project has delivered the world’s largest neuromorphic computing platform incorporating over a million ARM mobile phone processors and capable of modelling spiking neural networks of the scale of a mouse brain in biological real time. This machine, hosted at the University of Manchester in the UK, is freely available under the auspices of the EU Flagship Human Brain Project. This book tells the story of the origins of the machine, its development and its deployment, and the immense software development effort that has gone into making it openly available and accessible to researchers and students the world over. It also presents exemplar applications from ‘Talk’, a SpiNNaker-controlled robotic exhibit at the Manchester Art Gallery as part of ‘The Imitation Game’, a set of works commissioned in 2016 in honour of Alan Turing, through to a way to solve hard computing problems using stochastic neural networks. The book concludes with a look to the future, and the SpiNNaker-2 machine which is yet to come
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