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.

Integrating Biological and Artificial Neural Networks Processing on FPGAs

LEONE, GIANLUCA
2023-02-16

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.
16-feb-2023
File in questo prodotto:
File Dimensione Formato  
Tesi-di-dottorato_Gianluca-Leone.pdf

Open Access dal 17/02/2024

Descrizione: Integrating Biological and Artificial Neural Networks Processing on FPGAs
Tipologia: Tesi di dottorato
Dimensione 4.54 MB
Formato Adobe PDF
4.54 MB Adobe PDF Visualizza/Apri

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11584/357303
 Attenzione

Attenzione! I dati visualizzati non sono stati sottoposti a validazione da parte dell'ateneo

Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus ND
  • ???jsp.display-item.citation.isi??? ND
social impact