Implementation of Bayesian Fly Tracking Model using Analog Neuromorphic Circuits | IEEE Conference Publication | IEEE Xplore
Scheduled Maintenance: On Monday, 30 June, IEEE Xplore will undergo scheduled maintenance from 1:00-2:00 PM ET (1800-1900 UTC).
On Tuesday, 1 July, IEEE Xplore will undergo scheduled maintenance from 1:00-5:00 PM ET (1800-2200 UTC).
During these times, there may be intermittent impact on performance. We apologize for any inconvenience.

Implementation of Bayesian Fly Tracking Model using Analog Neuromorphic Circuits


Abstract:

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...Show More

Abstract:

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.
Date of Conference: 12-14 October 2020
Date Added to IEEE Xplore: 28 September 2020
Print ISBN:978-1-7281-3320-1
Print ISSN: 2158-1525
Conference Location: Seville, Spain

Contact IEEE to Subscribe

References

References is not available for this document.