Toward computational neuroconstructivism: a framework for developmental systems neuroscience

Astle, Duncan E.; Johnson, Mark H.; Akarca, Danyal

doi:10.1016/j.tics.2023.04.009

Cited by 8 publications

(5 citation statements)

References 115 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Further, we generated a large and detailed experimental open-access database that could be instrumental in benchmarking future research on this topic. An approach that we believe might be of particular interest is that of generative models 93,94 . Illustrating the promise of this approach, generative models solely based on spatiotemporal gradients of neuronal development 95 or homophily principles 75 can recapitulate important features of the complex topology of adult brains.…”

Section: Discussionmentioning

confidence: 99%

Extreme distributions in the preconfigured developing brain

Chini,

Hnida,

Kostka

et al. 2023

Preprint

View full text Add to dashboard Cite

In the adult brain, structural and functional parameters, such as synaptic sizes and neuronal firing rates, follow right-skewed and heavy-tailed distributions. While this organization is thought of having significant implications, its development is still largely unknown. Here, we address this knowledge gap by investigating a large-scale dataset recorded from the prefrontal cortex (PFC) and the olfactory bulb of mice aged 4-60 postnatal days. We show that firing rates and pairwise correlations have a largely stable distribution shape over age, and that neural activity displays a small-world architecture. Moreover, early brain activity displays an oligarchical organization, i.e., neurons with high firing rates are likely to have hub-like properties. Leveraging neural network modeling, we show that analogously extremely distributed synaptic parameters are necessary to recapitulate the experimental data. Thus, functional and structural parameters in the developing brain are already extremely distributed, suggesting that this organization is preconfigured and not experience-dependent.

show abstract

Section: Discussionmentioning

confidence: 99%

Extreme distributions in the preconfigured developing brain

Chini,

Hnida,

Kostka

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

“…5). We anticipate this observation will lead to a number of new theoretical insights at the intersection of network neuroscience and neural network research 24,62 .…”

Section: In Context Of Prior Structural Generative Network Model Find...mentioning

confidence: 91%

“…For example, one emerging finding is that the preference for topological self-similarity, when modelled as a wiring rule (termed homophily), can approximate structural and functional connectome datasets across numerous species and scales (e.g., 22,23 ). This indicates that an important developmental principle may be that neural assemblies form connections with each other, based on how similar these assembles are to each other 24 .…”

Section: Introductionmentioning

confidence: 99%

A weighted generative model of the human connectome

Akarca

Schiavi

Achterberg

et al. 2023

Preprint

Self Cite

View full text Add to dashboard Cite

Probabilistic generative network models have offered an exciting window into the constraints governing the human connectome's organization. In particular, they have highlighted the economic context of network formation and the special roles that physical geometry and self-similarity likely play in determining the connectome's topology. However, a critical limitation of these models is that they do not consider the strength of anatomical connectivity between regions. This significantly limits their scope to answer neurobiological questions. The current work draws inspiration from the principle of redundancy reduction to develop a novel weighted generative network model. This weighted generative network model is a significant advance because it not only incorporates the theoretical advancements of previous models, but also has the ability to capture the dynamic strengthening or weakening of connections over time. Using a state-of-the-art Convex Optimization Modelling for Microstructure-Informed Tractography (COMMIT) approach, in a sample of children and adolescents (n = 88, aged 8 to 18 years), we show that this model can accurately approximate simultaneously the topology and edge-weights of the connectome (specifically, the MRI signal fraction attributed to axonal projections). We achieve this at both sparse and dense connectome densities. Generative model fits are comparable to, and in many cases better than, published findings simulating topology in the absence of weights. Our findings have implications for future research by providing new avenues for exploring normative developmental trends, models of neural computation and wider conceptual implications of the economics of connectomics supporting human functioning.

show abstract

“…The case for neurobiological constraints on models of cognition was made in the seminal work of Churchland and Sejnowski (1992) and has been reiterated by others since then (e.g., Astle et al, 2023 ; O’Reilly, 2006 ; Pulvermüller et al, 2021 ). One way to approach this issue is to constrain existing neurocognitive architectures in order to increase their biological plausibility ( Pulvermüller et al, 2021 ).…”

Section: Multiple Explanatory Strategiesmentioning

confidence: 92%

Neurobiological Causal Models of Language Processing

Fitz,

Hagoort,

Petersson

2024

Neurobiology of Language

View full text Add to dashboard Cite

The language faculty is physically realized in the neurobiological infrastructure of the human brain. Despite significant efforts, an integrated understanding of this system remains a formidable challenge. What is missing from most theoretical accounts is a specification of the neural mechanisms that implement language function. Computational models that have been put forward generally lack an explicit neurobiological foundation. We propose a neurobiologically informed causal modeling approach which offers a framework for how to bridge this gap. A neurobiological causal model is a mechanistic description of language processing that is grounded in, and constrained by, the characteristics of the neurobiological substrate. It intends to model the generators of language behavior at the level of implementational causality. We describe key features and neurobiological component parts from which causal models can be built and provide guidelines on how to implement them in model simulations. Then we outline how this approach can shed new light on the core computational machinery for language, the long-term storage of words in the mental lexicon and combinatorial processing in sentence comprehension. In contrast to cognitive theories of behavior, causal models are formulated in the ‘machine language’ of neurobiology which is universal to human cognition. We argue that neurobiological causal modeling should be pursued in addition to existing approaches. Eventually, this approach will allow us to develop an explicit computational neurobiology of language.

show abstract

Toward computational neuroconstructivism: a framework for developmental systems neuroscience

Cited by 8 publications

References 115 publications

Extreme distributions in the preconfigured developing brain

Extreme distributions in the preconfigured developing brain

A weighted generative model of the human connectome

Neurobiological Causal Models of Language Processing

Contact Info

Product

Resources

About