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Dictionary Learning with Accumulator Neurons
Conference proceeding   Open access

Dictionary Learning with Accumulator Neurons

Gavin Parpart, Carlos Gonzalez Rivera, Terrence Stewart, Edward Kim, Jocelyn Rego, Andrew O'Brien, Steven Nesbit, Garrett Kenyon, Yijing Watkins and ACM
Proceedings of the International Conference on Neuromorphic Systems 2022, pp 1-9
27 Jul 2022
DOI: https://doi.org/10.1145/3546790.3546801
url
https://arxiv.org/abs/2205.15386View
Submitted Open arXiv.org - Non-exclusive license to distribute

Abstract

Computer systems organization -- Embedded and cyber-physical systems -- Sensors and actuators Computer systems organization -- Real-time systems Theory of computation -- Theory and algorithms for application domains -- Machine learning theory -- Online learning theory Theory of computation -- Theory and algorithms for application domains -- Machine learning theory -- Unsupervised learning and clustering
The Locally Competitive Algorithm (LCA) uses local competition between non-spiking leaky integrator neurons to infer sparse representations, allowing for potentially real-time execution on massively parallel neuromorphic architectures such as Intel’s Loihi processor. Here, we focus on the problem of inferring sparse representations from streaming video using dictionaries of spatiotemporal features optimized in an unsupervised manner for sparse reconstruction. Non-spiking LCA has previously been used to achieve unsupervised learning of spatiotemporal dictionaries composed of convolutional kernels from raw, unlabeled video. We demonstrate how unsupervised dictionary learning with spiking LCA (S-LCA) can be efficiently implemented using accumulator neurons, which combine a conventional leaky-integrate-and-fire (LIF) spike generator with an additional state variable that is used to minimize the difference between the integrated input and the spiking output. We demonstrate dictionary learning across a wide range of dynamical regimes, from graded to intermittent spiking, for inferring sparse representations of both static images drawn from the CIFAR database as well as video frames captured from a DVS camera. On a classification task that requires identification of the suite from a deck of cards being rapidly flipped through as viewed by a DVS camera, we find essentially no degradation in performance as the LCA model used to infer sparse spatiotemporal representations migrates from graded to spiking. We conclude that accumulator neurons are likely to provide a powerful enabling component of future neuromorphic hardware for implementing online unsupervised learning of spatiotemporal dictionaries optimized for sparse reconstruction of streaming video from event based DVS cameras.

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3 citations in Web of Science
1 citations in Scopus

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Computer Science, Theory & Methods
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