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Implementation of Bayesian fly tracking model using analog neuromorphic circuits

Tharakan, AT and Bhaskar, D and Thakur, CS (2020) Implementation of Bayesian fly tracking model using analog neuromorphic circuits. In: UNSPECIFIED, 10-21 Oct 2021, pp. 143-164.

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Official URL: https://doi.org/10.7169/FACM/1748


There is a growing body of evidence that suggests that the neurons in the brain calculate the posterior probability of states and events based on observations provided by the sensory neurons. Based on this hypothesis, a neuromorphic framework is proposed, where the sensory neurons of the dragonfly make noisy observations of the fruit fly and uses the underlying Hidden Markov Model (HMM) to track the fruit fly in two dimensional space. The dragonfly estimates the target position by solving the Bayesian recursive equations online. This work presents a novel approach for implementing probabilistic networks using sub-threshold analog neuromorphic circuits, with the ability to perform the computation in real-time. This framework will pave the way to build complex probabilistic algorithms based on HMMs for low power real-time applications. © 2020 IEEE

Item Type: Conference Paper
Publication: Proceedings - IEEE International Symposium on Circuits and Systems
Publisher: Institute of Electrical and Electronics Engineers Inc.
Additional Information: The copyright for this article belongs to Institute of Electrical and Electronics Engineers Inc.
Keywords: Fruits; Hidden Markov models; Timing circuits, Neuromorphic circuits; Noisy observations; Posterior probability; Probabilistic algorithm; Probabilistic network; Real-time application; Recursive equations; Two dimensional spaces, Neurons
Department/Centre: Division of Electrical Sciences > Computer Science & Automation
Date Deposited: 24 Sep 2021 07:53
Last Modified: 24 Sep 2021 07:53
URI: http://eprints.iisc.ac.in/id/eprint/69639

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