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Multi-Scale Spatiotemporal Neural Computation : = On the Relationship Between Dynamical Attractors Spiking Neural Networks and Convolutional Neural Circuits.

Record Type:	Language materials, manuscript : Monograph/item
Title/Author:	Multi-Scale Spatiotemporal Neural Computation :/
Reminder of title:	On the Relationship Between Dynamical Attractors Spiking Neural Networks and Convolutional Neural Circuits.
Author:	Diniz, Eduardo.
Description:	1 online resource (181 pages)
Notes:	Source: Dissertations Abstracts International, Volume: 85-12, Section: B.
Contained By:	Dissertations Abstracts International85-12B.
Subject:	Behavior. -
Online resource:	click for full text (PQDT)
ISBN:	9798383009550

Multi-Scale Spatiotemporal Neural Computation : = On the Relationship Between Dynamical Attractors Spiking Neural Networks and Convolutional Neural Circuits.
Diniz, Eduardo.

Multi-Scale Spatiotemporal Neural Computation :On the Relationship Between Dynamical Attractors Spiking Neural Networks and Convolutional Neural Circuits. - 1 online resource (181 pages)

Source: Dissertations Abstracts International, Volume: 85-12, Section: B.

Thesis (Ph.D.)--University of Pittsburgh, 2023.

Includes bibliographical references

Understanding spatiotemporal neural dynamics and developing biologically-inspired artificial neural networks remain open challenges in computational neuroscience. Critical gaps persist in elucidating cortical rhythms, memory consolidation, and biological networks' remarkable spatiotemporal processing capabilities. This dissertation hypothesizes that asymmetric connectivity and dedicated fast-slow processing pathways in neural systems enhance depth, robustness, and versatility in handling complex spatiotemporal patterns. Our first contribution is elucidating how neurons communicate and synchronize activity via temporally precise spikes by examining the dynamics of spike-coding networks. Developing models of cortical neural oscillators reveal the origins of spontaneous transitions between active and silent states underlying slow-wave sleep rhythms, demonstrating how the intricate balance of excitation and inhibition orchestrates these oscillations. Our second is to establish a mathematical equivalence between Hopfield networks' associative memory models and spike-coding networks by showing that fast and slow asymmetric connectivity weights induce equivalent cyclic attractor dynamics in both systems. Introducing asymmetric weights in slow connections enables both models to learn and generate complex temporal firing sequences, transitioning between quasi-attractor states representing stored memories. Simulations demonstrate the efficacy of spike-coding networks for encoding and retrieving temporal sequences while performing the n-back working memory task. Our third contribution is to harness the potential of generative adversarial networks for unpaired cross-modality translation from 3 Tesla to 7 Tesla magnetic resonance imaging. We propose a fast-slow convolutional network architecture to enhance translation performance by balancing local and global information processing. This dissertation makes significant contributions by elucidating brain mechanisms underlying rhythms and memory, and unifying foundational computational frameworks while extracting principles to improve artificial neural network design.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2024

Mode of access: World Wide Web

ISBN: 9798383009550Subjects--Topical Terms:

582559
Behavior.
Index Terms--Genre/Form:

554714
Electronic books.

Multi-Scale Spatiotemporal Neural Computation : = On the Relationship Between Dynamical Attractors Spiking Neural Networks and Convolutional Neural Circuits.
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