Chakraborty, Satrajit and Priyanka, P and Gupta, Sarthak and Afshar, Saeed and Hamilton, Tara and Thakur, Chetan Singh (2018) Neuromorphic Object Tracking Architecture, Based on Compound Eyes, and Implementation on FPGA. In: 61st IEEE International Midwest Symposium on Circuits and Systems (MWSCAS), AUG 05-08, 2018, Windsor, CANADA, pp. 668-671.
Full text not available from this repository. (Request a copy)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.
| Item Type: | Conference Proceedings |
|---|---|
| Series.: | Midwest Symposium on Circuits and Systems Conference Proceedings |
| Publisher: | IEEE |
| Additional Information: | 61st IEEE International Midwest Symposium on Circuits and Systems (MWSCAS), Windsor, CANADA, AUG 05-08, 2018 |
| Keywords: | Neuromorphic; Competitive Control; Object Tracking; LIF Neuron |
| Department/Centre: | Division of Electrical Sciences > Electronic Systems Engineering (Formerly Centre for Electronic Design & Technology) |
| Date Deposited: | 16 Mar 2019 06:13 |
| Last Modified: | 16 Mar 2019 06:13 |
| URI: | http://eprints.iisc.ac.in/id/eprint/61963 |
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