Sergio Davies

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This paper presents an efficient approach for implementing spike-timing-dependent plasticity (STDP) on the SpiNNaker neuromorphic hardware. The event-address mapping and the distributed synaptic weight storage schemes used in parallel neuromorphic hardware such as SpiNNaker make the conventional pre-post-sensitive scheme of STDP implementation inefficient,(More)
SpiNNaker is a massively parallel architecture with more than a million processing cores that can model up to 1 billion spiking neurons in biological real time. Here, we offer an overview of our research project and describe the first experiments with these test chips running spiking neurons based on Eugene Izhikevich's model. Note that we're not targeting(More)
Dedicated hardware is becoming increasingly essential to simulate emerging very-large-scale neural models. Equally, however, it needs to be able to support multiple models of the neural dynamics, possibly operating simultaneously within the same system. This may be necessary either to simulate large models with heterogeneous neural types, or to simplify(More)
This paper describes a closed-loop robotic system which calculates its position by means of a silicon retina sensor. The system uses an artificial neural network to determine the direction in which to move the robot in order to maintain a line-following trajectory. We introduce a pure “end to end” neural system in substitution of typical algorithms executed(More)
Artificial neural networks increasingly involve spiking dynamics to permit greater computational efficiency. This becomes especially attractive for on-chip implementation using dedicated neuromorphic hardware. However, both spiking neural networks and neuromorphic hardware have historically found difficulties in implementing efficient, effective learning(More)
Simulation of large networks of neurons is a powerful and increasingly prominent methodology for investigate brain functions and structures. Dedicated parallel hardware is a natural candidate for simulating the dynamic activity of many non-linear units communicating asynchronously. It is only scientifically useful, however, if the simulation tools can be(More)
This paper presents the algorithm and software developed for parallel simulation of spiking neural networks on multiple SpiNNaker universal neuromorphic chips. It not only describes approaches to simulating neural network models, such as dynamics, neural representations, and synaptic delays, but also presents the software design of loading a neural(More)
Simulation of large-scale networks of spiking neurons has become appealing for understanding the computational principles of the nervous system by producing models based on biological evidence. In particular, networks that can assume a variety of (dynamically) stable states have been proposed as the basis for different behavioural and cognitive functions.(More)
SpiNNaker is a hardware-based massively-parallel real-time universal neural network simulator designed to simulate large-scale spiking neural networks. Spikes are distributed across the system using a multicast packet router. Each packet represents an event (spike) generated by a neuron. On the basis of the source of the spike (chip, core and neuron), the(More)
Recent papers have shown the possibility to implement large scale neural network models that perform complex algorithms in a biologically realistic way. However, such models have been simulated on architectures unable to perform real-time simulations. In previous work we presented the possibility to simulate simple models in real-time on the SpiNNaker(More)