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— SpiNNaker is a novel chip – based on the ARM processor – which is designed to support large scale spiking neural networks simulations. In this paper we describe some of the features that permit SpiNNaker chips to be connected together to form scalable massively-parallel systems. Our eventual goal is to be able to simulate neural networks consisting of 10(More)
Computer simulation of neural matter is a promising methodology for understanding the function of the brain. Recent anatomical studies have mapped the intricate structure of cortex, and these data have been exploited in numerous simulations attempting to explain its function. However, the largest of these models run inconveniently slowly and require vast(More)
— 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(More)
— Given the limited current understanding of the neural model of computation, hardware neural network archi-tectures that impose a specific relationship between physical connectivity and model topology are likely to be overly restrictive. Here we introduce, in the SpiNNaker chip, an alternative approach: a mappable virtual topology using an asynchronous(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)
Neural networks present a fundamentally different model of computation from the conventional sequential digital model. Modelling large networks on conventional hardware thus tends to be inefficient if not impossible. Neither dedicated neural chips, with model limitations, nor FPGA implementations, with scalability limitations, offer a satisfactory solution(More)
— Large-scale neural hardware systems are trend-ing increasingly towards the " neuromimetic " architecture: a general-purpose platform that specialises the hardware for neural networks but allows flexibility in model choice. Since the model is not hard-wired into the chip, exploration of different neural and synaptic models is not merely possible but(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)
Real-time modelling of large neural systems places critical demands on the processing system's dynamic model. With spiking neu-ral networks it is convenient to abstract each spike to a point event. In addition to the representational simplification, the event model confers the ability to defer state updates, if the model does not propagate the effects of(More)