Mathematical Biosciences and Engineering (Apr 2024)

The switching and learning behavior of an octopus cell implemented on FPGA

  • Alexej Tschumak,
  • Frank Feldhoff ,
  • Frank Klefenz

DOI
https://doi.org/10.3934/mbe.2024254
Journal volume & issue
Vol. 21, no. 4
pp. 5762 – 5781

Abstract

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A dendrocentric backpropagation spike timing-dependent plasticity learning rule has been derived based on temporal logic for a single octopus neuron. It receives parallel spike trains and collectively adjusts its synaptic weights in the range [0, 1] during training. After the training phase, it spikes in reaction to event signaling input patterns in sensory streams. The learning and switching behavior of the octopus cell has been implemented in field-programmable gate array (FPGA) hardware. The application in an FPGA is described and the proof of concept for its application in hardware that was obtained by feeding it with spike cochleagrams is given; also, it is verified by performing a comparison with the pre-computed standard software simulation results.

Keywords