Real time on-chip implementation of dynamical systems with spiking neurons

Galluppi, Francesco; Davies, Sergio; Furber, Steve; Stewart, Terry; Eliasmith, Chris

doi:10.1109/ijcnn.2012.6252706

Cited by 24 publications

(15 citation statements)

References 30 publications

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“…Of course, the system also supports alternative approaches based on fixed synaptic weights. For example, the Neural Engineering Framework and Nengo (Galluppi, Davies, Furber, Stewart, & Eliasmith, 2012) are available on SpiNNaker. The actual challenge lies in an efficient use of the available system resources to achieve maximum performance.…”

Section: Implementing Stdp On Spinnakermentioning

confidence: 99%

Neuromorphic implementations of neurobiological learning algorithms for spiking neural networks

2015

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a b s t r a c tThe application of biologically inspired methods in design and control has a long tradition in robotics. Unlike previous approaches in this direction, the emerging field of neurorobotics not only mimics biological mechanisms at a relatively high level of abstraction but employs highly realistic simulations of actual biological nervous systems. Even today, carrying out these simulations efficiently at appropriate timescales is challenging. Neuromorphic chip designs specially tailored to this task therefore offer an interesting perspective for neurorobotics. Unlike Von Neumann CPUs, these chips cannot be simply programmed with a standard programming language. Like real brains, their functionality is determined by the structure of neural connectivity and synaptic efficacies. Enabling higher cognitive functions for neurorobotics consequently requires the application of neurobiological learning algorithms to adjust synaptic weights in a biologically plausible way. In this paper, we therefore investigate how to program neuromorphic chips by means of learning. First, we provide an overview over selected neuromorphic chip designs and analyze them in terms of neural computation, communication systems and software infrastructure. On the theoretical side, we review neurobiological learning techniques. Based on this overview, we then examine on-die implementations of these learning algorithms on the considered neuromorphic chips. A final discussion puts the findings of this work into context and highlights how neuromorphic hardware can potentially advance the field of autonomous robot systems. The paper thus gives an in-depth overview of neuromorphic implementations of basic mechanisms of synaptic plasticity which are required to realize advanced cognitive capabilities with spiking neural networks.

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Section: Implementing Stdp On Spinnakermentioning

confidence: 99%

Neuromorphic implementations of neurobiological learning algorithms for spiking neural networks

2015

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“…Known neurophysiological data acts as constraints on neural parameters, making comparisons with human neural and behavioral data possible. Regardless of the choice of the language, the model is mapped and distributed to the machine using the Partition and Configuration Manager (PACMAN) [10], which hides the complexity of configuring a massively-parallel machine to the final user, by exposing the system through interfaces with PyNN and Nengo [9][24], the software that implements the NEF principles, automatically translating the system in biologically plausible neural circuitry, and performing all computation in neurons, as presented in Section IV-B.…”

Section: The Robotic Platformmentioning

confidence: 99%

Event-based neural computing on an autonomous mobile platform

Galluppi

Denk

Meiner

et al. 2014

2014 IEEE International Conference on Robotics and Automation (ICRA)

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Abstract-Living organisms are capable of autonomously adapting to dynamically changing environments by receiving inputs from highly specialized sensory organs and elaborating them on the same parallel, power-efficient neural substrate. In this paper we present a prototype for a comprehensive integrated platform that allows replicating principles of neural information processing in real-time. Our system consists of (a) an autonomous mobile robotic platform, (b) on-board actuators and multiple (neuromorphic) sensors, and (c) the SpiNNaker computing system, a configurable neural architecture for exploration of parallel, brain-inspired models. The simulation of neurally inspired perception and reasoning algorithms is performed in real-time by distributed, low-power, low-latency event-driven computing nodes, which can be flexibly configured using C or specialized neural languages such as PyNN and Nengo. We conclude by demonstrating the platform in two experimental scenarios, exhibiting real-world closed loop behavior consisting of environmental perception, reasoning and execution of adequate motor actions.

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“…The fast asynchronous communication interface is designed to route neural action potentials from arbitrary neurons to a large number of other neurons. Various options for neural network implementations exist, from API library calls to interpreters of neural description languages such as PyNN [6] or NeNGO [7].…”

Section: The Spinnaker Neural Network Computing Systemmentioning

confidence: 99%

Real-Time Interface Board for Closed-Loop Robotic Tasks on the SpiNNaker Neural Computing System

Denk

Llobet-Blandino

Galluppi

et al. 2013

Artificial Neural Networks and Machine Learning – ICANN 2013

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Abstract. Various custom hardware solutions for simulation of neural circuitry have recently been developed, each focusing on particular aspects such as low power operation, high computation speed, or biologically detailed simulations. The SpiNNaker computing system has been developed to simulate large spiking neural circuits in real-time in a network of parallel operating microcontrollers, interconnected by a high-speed asynchronous interface. A potential application area is autonomous mobile robotics, which would tremendously benefit from on-board simulations of networks of tens of thousands of spiking neurons in real-time. Currently, the SpiNNaker hardware circuit boards provide a single Ethernet interface for booting, debug, and input and output of data, which results in a severe bottleneck for sensory perception and motor control signals. This paper describes a small and flexible real-time I/O-hardware interface to connect external devices such as robotic sensors and actuators directly to the fast asynchronous internal communication infrastructure of the SpiNNaker neural computing system. We evaluate performance in terms of package throughput and present a simple application demonstration of a closed loop mobile robot interpreting visual data to approach the most salient stimulus.

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Real time on-chip implementation of dynamical systems with spiking neurons

Cited by 24 publications

References 30 publications

Neuromorphic implementations of neurobiological learning algorithms for spiking neural networks

Neuromorphic implementations of neurobiological learning algorithms for spiking neural networks

Event-based neural computing on an autonomous mobile platform

Real-Time Interface Board for Closed-Loop Robotic Tasks on the SpiNNaker Neural Computing System

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