TY - GEN
T1 - Neuromorphic object tracking architecture, based on compound eyes, and implementation on FPGA
AU - Chakraborty, Satrajit
AU - Priyanka, P.
AU - Gupta, Sarthak
AU - Afshar, Saeed
AU - Hamilton, Tara
AU - Thakur, Chetan Singh
PY - 2018
Y1 - 2018
N2 - 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.
AB - 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.
KW - computer vision
KW - field programmable gate arrays
KW - housefly
KW - neuromorphics
KW - object tracking
UR - http://handle.westernsydney.edu.au:8081/1959.7/uws:51415
U2 - 10.1109/MWSCAS.2018.8624115
DO - 10.1109/MWSCAS.2018.8624115
M3 - Conference Paper
SN - 9781538673928
SP - 668
EP - 671
BT - Proceedings of the 61st IEEE International Midwest Symposium on Circuits and Systems (MWSCAS), 5-8 Aug. 2018, Windsor, Ontario, Canada
PB - IEEE
T2 - Midwest Symposium on Circuits and Systems
Y2 - 5 August 2018
ER -