On the computational power and complexity of Spiking Neural Networks

• URL: http://arxiv.org/abs/2001.08439v1
• Date: Thu, 23 Jan 2020 10:40:16 GMT
• Title: On the computational power and complexity of Spiking Neural Networks
• Authors: Johan Kwisthout, Nils Donselaar
• Abstract summary: We introduce spiking neural networks as a machine model where---in contrast to the familiar Turing machine---information and the manipulation thereof are co-located in the machine. We introduce canonical problems, define hierarchies of complexity classes and provide some first completeness results.
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• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The last decade has seen the rise of neuromorphic architectures based on artificial spiking neural networks, such as the SpiNNaker, TrueNorth, and Loihi systems. The massive parallelism and co-locating of computation and memory in these architectures potentially allows for an energy usage that is orders of magnitude lower compared to traditional Von Neumann architectures. However, to date a comparison with more traditional computational architectures (particularly with respect to energy usage) is hampered by the lack of a formal machine model and a computational complexity theory for neuromorphic computation. In this paper we take the first steps towards such a theory. We introduce spiking neural networks as a machine model where---in contrast to the familiar Turing machine---information and the manipulation thereof are co-located in the machine. We introduce canonical problems, define hierarchies of complexity classes and provide some first completeness results.

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