Neuromorphic object tracking architecture, based on compound eyes, and implementation on FPGA

Satrajit Chakraborty, P. Priyanka, Sarthak Gupta, Saeed Afshar, Tara Hamilton, Chetan Singh Thakur

Research output: Chapter in Book/Report/Conference proceedingConference proceeding contribution

Abstract

Recent findings in neuroscience, show that rapid changes in flight direction of a housefly/blowfly (mainly to track objects) are attributable to neural circuits distributed behind its photo-receptors. While tracking objects, using its compound eye structure, a fly is able to detect changes in the motion of the object quickly and changes its own motion accordingly. The working of these neural circuits may be modelled as a set of leaky integrate and fire neurons connected in a special manner to form a competitive feedback control. Based on this knowledge, we present a neuromorphic competitive control circuit utilizing an inference neuron model to control N actuators and analyze their outputs for tracking an object. This model was simulated in software first and then implemented on a Xilinx Artix-7 XC7A35T- ICPG236C FPGA board using Verilog. The results show an observable decoherence phenomenon between the neurons and support the working principle of the model.

Original languageEnglish
Title of host publication 2018 IEEE 61st International Midwest Symposium on Circuits and Systems (MWSCAS)
Place of PublicationPiscataway, NJ
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Pages668-671
Number of pages4
ISBN (Electronic)9781538673928, 9781538673911
ISBN (Print)9781538673935
DOIs
Publication statusPublished - 2018
Externally publishedYes
Event61st IEEE International Midwest Symposium on Circuits and Systems, MWSCAS 2018 - Windsor, Canada
Duration: 5 Aug 20188 Aug 2018

Publication series

Name
ISSN (Print)1548-3746
ISSN (Electronic)1558-3899

Conference

Conference61st IEEE International Midwest Symposium on Circuits and Systems, MWSCAS 2018
CountryCanada
CityWindsor
Period5/08/188/08/18

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  • Cite this

    Chakraborty, S., Priyanka, P., Gupta, S., Afshar, S., Hamilton, T., & Thakur, C. S. (2018). Neuromorphic object tracking architecture, based on compound eyes, and implementation on FPGA. In 2018 IEEE 61st International Midwest Symposium on Circuits and Systems (MWSCAS) (pp. 668-671). Piscataway, NJ: Institute of Electrical and Electronics Engineers (IEEE). https://doi.org/10.1109/MWSCAS.2018.8624115