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Sensing-to-Learn and Learning-to-Sense: Principles for Designing Neuromorphic Sensors

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Abstract

Neurobiological systems have evolved over a billion years and serve as a good template for some text engineers to mimic when designing intelligent sensors and systems. For instance, neurobiological systems exploit noise and system non-linearity as a computational aid to push the limits of performance and energy efficiency. In contrast, in man-made technologies, these artifacts are generally considered to be a nuisance. This chapter’s focus is on the neuromorphic concept of “sensing-to-learn” and “learning-to-sense,” which are grounded in key neuromorphic adaptation principles based on noise exploitation and non-linear sensory processing techniques. “Noise shaping” and “jump-resonance” are two techniques that can extract salient sensing cues by exploring the synergy between noise and system non-linearity. We illustrate these concepts in the context of auditory and olfaction pathways, and we argue how these principles can be used to design the next generation of neuromorphic sensory interfaces.

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Acknowledgements

The authors would like to thank the Scheme for Promotion of Academic and Research Collaboration (SPARC), MHRD, Govt. of India, for funding (SPARC/2018-2019/P606/SL) this work. The authors acknowledge the Brain, Computation and Learning workshop at the Indian Institute of Science, India, where much of this work has been done.

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Authors and Affiliations

  1. Washington University in St. Louis, St. Louis, MO, USA

    Shantanu Chakrabartty & Baranidharan Raman

  2. Indian Institute of Science, Bengaluru, India

    Chetan Singh Thakur

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  1. Shantanu Chakrabartty
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  2. Baranidharan Raman
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  3. Chetan Singh Thakur
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Correspondence to Chetan Singh Thakur .

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  1. Singapore Institute for Neurotechnology, National University of Singapore, Singapore, Singapore

    Nitish V. Thakor

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  1. Johns Hopkins University, Baltimore, United States

    Adam Khalifa

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Chakrabartty, S., Raman, B., Thakur, C.S. (2022). Sensing-to-Learn and Learning-to-Sense: Principles for Designing Neuromorphic Sensors. In: Thakor, N.V. (eds) Handbook of Neuroengineering. Springer, Singapore. https://doi.org/10.1007/978-981-15-2848-4_122-1

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