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Programming Neuromorphics Using the Neural Engineering Framework

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Handbook of Neuroengineering

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Abstract

As neuromorphic hardware begins to emerge as a viable target platform for artificial intelligence (AI) applications, there is a need for tools and software that can effectively compile a variety of AI models onto such hardware. Nengo (http://nengo.ai) is an ecosystem of software designed to fill this need with a suite of tools for creating, training, deploying, and visualizing neural networks for various hardware backends, including CPUs, GPUs, FPGAs, microcontrollers, and neuromorphic hardware. While backpropagation-based methods are powerful and fully supported in Nengo, there is also a need for frameworks that are capable of efficiently mapping dynamical systems onto such hardware while best utilizing its computational resources. The neural engineering framework (NEF) is one such method that is supported by Nengo. Most prominently, Nengo and the NEF have been used to engineer the world’s largest functional model of the human brain. In addition, as a particularly efficient approach to training neural networks for neuromorphics, the NEF has been ported to several neuromorphic platforms. In this chapter, we discuss the mathematical foundations of the NEF and a number of its extensions and review several recent applications that use Nengo to build models for neuromorphic hardware. We focus in-depth on a particular class of dynamic neural networks, Legendre Memory Units (LMUs), which have demonstrated advantages over state-of-the-art approaches in deep learning with respect to energy efficiency, training time, and accuracy.

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Notes

  1. 1.

    There is a similar theorem for continuous time [38].

  2. 2.

    By linearity of convolution, it does not matter whether the filter is applied before or after the decoding or any subsequent encoding. For efficiency reasons, it is often applied in the lower-dimensional (i.e., decoded) space. What is most efficient depends on the hardware and the sparsity of neural activity relative to the integration timescale.

  3. 3.

    The optimization problem from Eq. 6 need only be solved once to decode x(t) from the neural activity. The same decoders may then be transformed by each C without loss in optimality (by linearity).

  4. 4.

    Also assuming the use of a dense state-space realization such as from zero-order hold discretization of the LMU dynamics.

  5. 5.

    Goldman [43] has shown that repeated low-pass filtering can be usefully exploited to implement an integrator, by summing across all of the filters.

  6. 6.

    Determined empirically using the NengoLoihi = 0.5.0 emulator

  7. 7.

    Hyperopt was used to the benefit of LSMs and ESNs. All hyperparameters (apart from q) had minimal effect on the LMU’s performance compared to the usual defaults in Nengo.

  8. 8.

    As additional validation, lower input frequencies or shorter delay lengths were possible with the LSM.

  9. 9.

    The postsynaptic filters are leveraged to participate in the required computation (see Principle 3; Sect. 0.2.1). There is no unwanted phase shift.

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  1. Centre for Theoretical Neuroscience, University of Waterloo, Applied Brain Research Inc., Waterloo, ON, Canada

    Aaron R. Voelker & Chris Eliasmith

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  1. Aaron R. Voelker
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  1. Neuroengineering and Biomedical Instrumentation Laboratory Department of Biomedical Engineering and Department of Electrical and Computer Engineering, Neurology, Johns Hopkins University, Baltimore, MD, USA

    Nitish V. Thakor

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Voelker, A.R., Eliasmith, C. (2023). Programming Neuromorphics Using the Neural Engineering Framework. In: Thakor, N.V. (eds) Handbook of Neuroengineering. Springer, Singapore. https://doi.org/10.1007/978-981-16-5540-1_115

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