Biology subjects

Barabasi, D. L.

Publications and source records attributed to Barabasi, D. L..

5 recordsLinked to original sources

bioRxiv2025

Physical Network Constraints Define the Lognormal Architecture of the Brain's Connectome

The brain has long been conceptualized as a network of neurons connected by synapses. However, attempts to describe the connectome using established network science models have yielded conflicting outcomes, leaving the architecture of neural networks unresolved. Here, by performing a comparative analysis of eight experimentally mapped connectomes, we find that their degree distributions cannot be captured by the well-established random or scale-free models. Instead, the node degrees and strengths are well approximated by lognormal distributions, although these lack a mechanistic explanation in the context of the brain. By acknowledging the physical network nature of the brain, we show that neuron size is governed by a stochastic multiplicative process, which allows us to analytically derive the lognormal nature of the neuron length distribution. Our framework not only predicts the degree and strength distributions across each of the eight connectomes, but also yields a series of empirically falsifiable relationships between different neuron characteristics. The resulting multiplicative network represents a novel architecture for network science, whose distinctive quantitative features bridge critical gaps between neural structure and function, with implications for brain dynamics, robustness, and synchronization.

neuroscience↗

bioRxiv2022

Nature over Nurture: Functional neuronal circuits emerge in the absence of developmental activity

During development, the complex neuronal circuitry of the brain arises from limited information contained in the genome. After the genetic code instructs the birth of neurons, the emergence of brain regions, and the formation of axon tracts, it is believed that neuronal activity plays a critical role in shaping circuits for behavior. Current AI technologies are modeled after the same principle: connections in an initial weight matrix are pruned and strengthened by activity-dependent signals until the network can sufficiently generalize a set of inputs into outputs. Here, we challenge these learning-dominated assumptions by quantifying the contribution of neuronal activity to the development of visually guided swimming behavior in larval zebrafish. Intriguingly, dark-rearing zebrafish revealed that visual experience has no effect on the emergence of the optomotor response (OMR). We then raised animals under conditions where neuronal activity was pharmacologically silenced from organogenesis onward using the sodium-channel blocker tricaine. Strikingly, after washout of the anesthetic, animals performed swim bouts and responded to visual stimuli with 75% accuracy in the OMR paradigm. After shorter periods of silenced activity OMR performance stayed above 90% accuracy, calling into question the importance and impact of classical critical periods for visual development. Detailed quantification of the emergence of functional circuit properties by brain-wide imaging experiments confirmed that neuronal circuits came online fully tuned and without the requirement for activity-dependent plasticity. Thus, we find that complex sensory guided behaviors can be wired up by activity-independent developmental mechanisms.

neuroscience↗

bioRxiv2021

Complex Computation from Developmental Priors

Artificial Intelligence (AI) research has provided key insights into the mechanics of learning complex tasks. However, AI models have long overlooked innateness: how strong pressures for survival lead to the encoding of complex behaviors in the nascent wiring of a brain. Although innate neural solutions have inspired AI approaches from layered architectures to ConvNets, the underlying neuroevolutionary search for novel heuristics has not been successfully systematized. In this manuscript, we examine how neurodevelopmental principles can inform the discovery of computational heuristics. We begin by considering the weight matrix of a neural network to be emergent from well-studied rules of neuronal compatibility. Rather than updating the networks weights directly, we improve task fitness by updating the neurons wiring rules, thereby mirroring evolutionary selection on brain development. We find that the resulting framework can not only achieve high performance on standard machine learning tasks, but does so with a fraction of the full networks parameters. Further, when we condition neuronal identity on biologically-plausible spatial constraints, we discover representations that resemble visual filters and are capable of learning transfer. Finally, we show that developmentally-inspired techniques have higher and more stable performance on metalearning tasks than the standard models they encode. In summary, by introducing realistic developmental considerations into machine learning frameworks, we not only capture the emergence of innate behaviors, but also define a discovery process for structures that promote complex computations.

neuroscience↗

bioRxiv2020

Constructing Graphs from Genetic Encodings

Our understanding of real-world connected systems has benefited from studying their evolution, from random wirings and rewirings to growth-dependent topologies. Long overlooked in this search has been the role of the innate: networks that connect based on identity-dependent compatibility rules. Inspired by the genetic principles that guide brain connectivity, we derive a network encoding process that can utilize wiring rules to reproducibly generate specific topologies. To illustrate the representational power of this approach, we propose stochastic and deterministic processes for generating a wide range of network topologies. Specifically, we detail network heuristics that generate structured graphs, such as feed-forward and hierarchical networks. In addition, we characterize a Random Genetic (RG) family of networks, which, like Erd[o]s-Renyi graphs, display critical phase transitions, however their modular underpinnings lead to markedly different behaviors under targeted attacks. The proposed framework provides a relevant null-model for social and biological systems, where diverse metrics of identity underpin a nodes preferred connectivity.

neuroscience↗

bioRxiv2020

Uncovering the genetic blueprint of the C. elegans nervous system

Despite rapid advances in connectome mapping and neuronal genetics, we lack theoretical and computational tools to unveil, in an experimentally testable fashion, the genetic mechanisms that govern neuronal wiring. Here we introduce a computational framework to link the adjacency matrix of a connectome to the expression patterns of its neurons, helping us uncover a set of genetic rules that govern the interactions between adjacent neurons. The method incorporates the biological realities of the system, accounting for noise from data collection limitations, as well as spatial restrictions. The resulting methodology allows us to infer a network of 19 innexin interactions that govern the formation of gap junctions in C. elegans, five of which are already supported by experimental data. As advances in single-cell gene expression profiling increase the accuracy and the coverage of the data, the developed framework will allow researchers to systematically infer experimentally testable connection rules, offering mechanistic predictions for synapse and gap junction formation.

biophysics↗