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bioRxiv · 10.1101/2021.07.18.452831

Towards on-chip real-time classification of extra-cellular neural recordings

Ozdas, M.·Gronskaya, E.·von der Behrens, W.·Indiveri, G.
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Abstract

On-line classification of neural recordings can be extremely useful in brain-machine interface, prosthetic applications or therapeutic intervention. In this work we present a feasibility study for developing compact low-power VLSI systems able to classify neural recordings in real-time, using spike-based neuromorphic circuits. We developed a framework for classifying extra-cellular recordings made in rat auditory cortex in response to different auditory stimuli and porting the classification algorithm onto a spiking multi-neuron VLSI chip with programmable synaptic weights. We present recording methods and software classification algorithms; we demonstrate real-time classification in hardware and quantify the system performance; finally, we identify the potential sources of problems in developing such types of systems and propose strategies for overcoming them.

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Ozdas, M., Gronskaya, E., von der Behrens, W., Indiveri, G.. 2021-07-19. Towards on-chip real-time classification of extra-cellular neural recordings. https://doi.org/10.1101/2021.07.18.452831

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