Integrating Biological and Artificial Neural Networks Processing on FPGAs

LEONE, GIANLUCA

2023-02-16

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Abstract

Neural interfaces are rapidly gaining momentum in the current landscape of neuroscience and bioengineering. This is due to a) unprecedented technology capable of sensing biological neural network electrical activity b) increasingly accurate analytical models usable to represent and understand dynamics and behavior in neural networks c) novel and improved artificial intelligence methods usable to extract information from recorded neural activity. Nevertheless, all these instruments pose significant requirements in terms of processing capabilities, especially when focusing on embedded implementations, respecting real-time constraints and exploiting resource-constrained computing platforms. Acquisition frequencies, as well as the complexity of neuron models and artificial intelligence methods based on neural networks, pose the need for high throughput processing of very high data rates and expose a significant level of intrinsic parallelism. Thus, a promising technology serving as a substrate for implementing efficient embedded neural interfaces is represented by APSoCs, that enable the use of configurable logic, organizable memory blocks and parallel DSP slices. In this thesis we assess the usability of APSoC in this domain by focusing on a) real-time processing and analysis of MEA-acquired signals featuring spike detection and spike sorting on 5,500 recording electrodes b) real-time emulation of a biologically-relevant spiking neural network counting 3,098 Izhikevich neurons and 9.6e6 synaptic interconnections c) real-time execution of spiking neural networks for neural activity decoding during a delayed reach-to-grasp task addressing low-power embedded applications.