Information Representation on a Universal Neural Chip

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Abstract

How can science possibly understand the organ through which the Universe knows itself? The scientific method can be used to study how electro-chemical signals represent information in the brain. However, modelling it by simulating its structures and functions is a computation- and communication-intensive task. Whilst supercomputers offer great computational power, brain-scale models are challenging in terms of communication overheads and power consumption. Dedicated neural hardware can be used to enhance simulation performance, but it is often optimised for specific models. While performance and flexibility are desirable simulation features, there is no perfect modelling platform, and the choice is subordinate to the specific research question being investigated.In this context SpiNNaker constitutes a novel parallel architecture, with communication and memory accesses optimised for spike-based computation, permitting simulation of large spiking neural networks in real time.To exploit SpiNNaker's performance and reconfigurability fully, a neural network model must be translated from its conceptual form into data structures for a parallel system. This thesis presents a flexible approach to distributing and mapping neural models onto SpiNNaker, within the constraints introduced by its specialised architecture. The conceptual map underlying this approach characterizes the interaction between the model and the system: during the build phase the model is placed on SpiNNaker; at runtime, placement information mediates communication with devices and instrumentation for data analysis. Integration within the computational neuroscience community is achieved by interfaces to two domain-specific languages: PyNN and Nengo. The real-time, event-driven nature of the SpiNNaker platform is explored using address-event representation sensors and robots, performing visual processing using a silicon retina, and navigation on a robotic platform based on a cortical, basal ganglia and hippocampal place cells model. The approach has been successfully exploited to run models on all iterations of SpiNNaker chips and development boards to date, and demonstrated live in workshops and conferences.

Layman's Abstract

How can science possibly understand the organ through which the Universe knows itself? The scientific method can be used to study how electro-chemical signals represent information in the brain. However, modelling it by simulating its structures and functions is a computation- and communication-intensive task. Whilst supercomputers offer great computational power, brain-scale models are challenging in terms of communication overheads and power consumption. Dedicated neural hardware can be used to enhance simulation performance, but it is often optimised for specific models. While performance and flexibility are desirable simulation features, there is no perfect modelling platform, and the choice is subordinate to the specific research question being investigated.In this context SpiNNaker constitutes a novel parallel architecture, with communication and memory accesses optimised for spike-based computation, permitting simulation of large spiking neural networks in real time.To exploit SpiNNaker's performance and reconfigurability fully, a neural network model must be translated from its conceptual form into data structures for a parallel system. This thesis presents a flexible approach to distributing and mapping neural models onto SpiNNaker, within the constraints introduced by its specialised architecture. The conceptual map underlying this approach characterizes the interaction between the model and the system: during the build phase the model is placed on SpiNNaker; at runtime, placement information mediates communication with devices and instrumentation for data analysis. Integration within the computational neuroscience community is achieved by interfaces to two domain-specific languages: PyNN and Nengo. The real-time, event-driven nature of the SpiNNaker platform is explored using address-event representation sensors and robots, performing visual processing using a silicon retina, and navigation on a robotic platform based on a cortical, basal ganglia and hippocampal place cells model. The approach has been successfully exploited to run models on all iterations of SpiNNaker chips and development boards to date, and demonstrated live in workshops and conferences.

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CD-ROM attachment (Appendix C) enclosed with the hard copies

Keyword(s)

Biologically-inspired vision; Brain-inspired computing; Neural computation; Neurorobotics; Parallel architectures

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