Computational Principles of Brain Network Development
type: CreativeWork
description: Brain development can be viewed through many lenses and studied at many scales. However, multiple theoretical perspectives have argued that brain organisation develops via competitive interactions between its constituent units, dynamically over time. In this thesis, I focus on modelling these interactions. In Chapter 1, after providing an historical backdrop to the field of developmental systems neuroscience, I introduce generative network models. These relatively new family of models are capable of simulating probabilistic network development. The basis for these models includes simple sets of wiring rules, existing within various imposed biophysical constraints, which steer the developmental trajectory of the network. In Chapter 2, I show how the applications of these models can reveal simple principles that may contribute to our understanding of neurodiversity. In particular, small iterative updates in networks can lead to constrained variability in a child’s macroscopic structural brain organisation inferred via in vivo diffusion imaging. I highlight how decompositions of networks into the generative components used to construct them in this way can be useful lowerdimensional representations of developmental ingredients. This is particularly relevant when aiming to bridge associations between genomics, cognition and the brain for answering developmental questions. Generative network models emphasise the evolving economic context of dynamic interactive negotiations between brain regions. These regions can be defined at any scale. In Chapter 3 I pivot from studying cross-sectional macroscopic connectomes, to modelling the microstructural longitudinal development of in vitro neuronal networks at the cellular scale. I show that current instantiations of a homophily generative model are an effective growth model of in vitro neuronal network development. This simple model can recapitulate observable local topological organisation of functional networks across species,…
abstract: Brain development can be viewed through many lenses and studied at many scales. However, multiple theoretical perspectives have argued that brain organisation develops via competitive interactions between its constituent units, dynamically over time. In this thesis, I focus on modelling these interactions. In Chapter 1, after providing an historical backdrop to the field of developmental systems neuroscience, I introduce generative network models. These relatively new family of models are capable of simulating probabilistic network development. The basis for these models includes simple sets of wiring rules, existing within various imposed biophysical constraints, which steer the developmental trajectory of the network. In Chapter 2, I show how the applications of these models can reveal simple principles that may contribute to our understanding of neurodiversity. In particular, small iterative updates in networks can lead to constrained variability in a child’s macroscopic structural brain organisation inferred via in vivo diffusion imaging. I highlight how decompositions of networks into the generative components used to construct them in this way can be useful lowerdimensional representations of developmental ingredients. This is particularly relevant when aiming to bridge associations between genomics, cognition and the brain for answering developmental questions. Generative network models emphasise the evolving economic context of dynamic interactive negotiations between brain regions. These regions can be defined at any scale. In Chapter 3 I pivot from studying cross-sectional macroscopic connectomes, to modelling the microstructural longitudinal development of in vitro neuronal networks at the cellular scale. I show that current instantiations of a homophily generative model are an effective growth model of in vitro neuronal network development. This simple model can recapitulate observable local topological organisation of functional networks across species,…
author: Akarca, Danyal
language: eng
keywords: Brain development, Computational neuroscience, Developmental systems neuroscience, Generative models, Multi-scale connectomics, Network neuroscience, Spatially-embedded RNNs
license: https://creativecommons.org/licenses/by/4.0/
date published: 2022-11-05
date modified: 2026-07-22T22:24:23Z
provider: Cambridge University
publisher: Networked Digital Library of Theses and Dissertations
type: WebPage
source organization: University of Cambridge

PropertyValue
property ID: DOI
value: 10.17863/CAM.93782