Biology subjects

Lynn, C. W.

Publications and source records attributed to Lynn, C. W..

7 recordsLinked to original sources

bioRxiv2026

Heavy-tailed connections support robust neural communication

In the synaptic wiring between neurons, some rare connections are orders of magnitude stronger than most. This heavy-tailed connectivity is ubiquitous across different species, brain regions, and cell types, suggesting that it serves a general purpose for neural function. Here, we show, both analytically and in simulations, that heavy-tailed connections reduce communication errors in the presence of synaptic noise. In minimal models of individual neurons, noisy connections induce errors at a rate that depends only on a simple network statistic, which we refer to as robustness. For a fixed number of incoming synapses, neurons with broader distributions of connection strength are more robust and, therefore, admit fewer errors. Across several connectomes, we find that real neurons are consistently more robust than comparable null networks. In the whole brain of the fruit fly Drosophila, robustness increases with distance from the sensory periphery, such that central neurons are more robust than those involved in sensory processing. Together, these results indicate that heavy-tailed connections may arise universally to fortify the brain against noise in communication.

neuroscience↗

bioRxiv2025

Hierarchical Community Structure of the Adult Drosophila Connectome Reveals Conserved Circuit Archetypes

The community structure of connectomes supports functional specialization, adaptability, and cost-efficient wiring. However, little is known about communities in connectomes mapped at the level of individual neurons and synapses. Here, we analyze a whole-brain Drosophila adult connectome using a nested stochastic blockmodel to uncover its hierarchical community structure. Most of the roughly 1500 fine-scale communities-the smallest, and best resolved level of the hierarchy- are spatially compact, mostly assortative, and aligned with biological features. Nonetheless, we find evidence of nonassortative communities, spatially co-localized within the optic lobe and vision-processing pathways. Seeking "functional primitives"-small circuits with functionally narrow feature profiles-we use data-driven clustering to group communities into 45 archetypical meta-clusters based on their spatial, functional, and molecular properties, revealing modular building blocks from which larger, functionally diverse communities are composed. This work advances our understanding of how structure and function are organized in the fruit fly brain and highlights the value of statistical network models in interpreting nanoscale connectomes.

neuroscience↗

bioRxiv2025

Decay in transcriptional information flow is a hallmark of cellular aging

Aging is marked by the progressive loss of cellular function, yet the organizing principles underlying this decline remain unclear. Although molecular fingerprints of aging are diverse, many converge on disruption of the interrelated and overlapping communication networks that coordinate molecular activity. Here, we apply information theory to quantify age-related corruption in gene regulation by modeling regulatory interactions between transcription factors (TFs) and their target genes (TGs) as a multi-input multi-output communication channel. Using an analytically tractable probabilistic model and single-cell RNA-sequencing data from multiple tissues, we find that the mutual information (a measure of information transfer) between TFs and TGs declines with age across all ten tissues analyzed, establishing loss of regulatory information transmission as a hallmark of aging. Structural analysis of the regulatory network reveals that aging degrades communication primarily through input distribution mismatch, reflecting a loss of coordinated TF activity, rather than channel corruption, or the inability of TFs to reliably activate or inhibit their targets. This mismatch is caused by increased network centralization and loss of stabilizing feedback motifs, leading to reduced robustness to random perturbations. Notably, in silico upregulation of a small set of TFs restores youthful information transfer and gene expression levels, suggesting that targeted reinforcement of key regulatory nodes may rejuvenate aged networks.

systems biology↗

bioRxiv2022

Heavy-tailed neuronal connectivity arises from Hebbian self-organization

In networks of neurons, the connections are heavy-tailed, with a small number of neurons connected much more strongly than the vast majority of pairs.1-6 Yet it remains unclear whether, and how, such heavy-tailed connectivity emerges from simple underlying mechanisms. Here we propose a minimal model of synaptic self-organization: connections are pruned at random, and the synaptic strength rearranges under a mixture of Hebbian and random dynamics. Under these generic rules, networks evolve to produce scale-free distributions of connectivity strength, with a power-law exponent [Formula] that depends only on the probability p of Hebbian (rather than random) growth. By extending our model to include correlations in neuronal activity, we find that clustering--another ubiquitous feature of neuronal networks6-9--also emerges naturally. We confirm these predictions in the connectomes of several animals, suggesting that heavy-tailed and clustered connectivity may arise from general principles of self-organization, rather than the biophysical particulars of individual neural systems.

neuroscience↗

bioRxiv2021

Neurophysiological evidence for cognitive map formation during sequence learning

Humans deftly parse statistics from sequences. Some theories posit that humans learn these statistics by forming cognitive maps, or underlying representations of the latent space which links items in the sequence. Here, an item in the sequence is a node, and the probability of transitioning between two items is an edge. Sequences can then be generated from walks through the latent space, with different spaces giving rise to different sequence statistics. Individual or group differences in sequence learning can be modeled by changing the time scale over which estimates of transition probabilities are built, or in other words, by changing the amount of temporal discounting. Latent space models with temporal discounting bear a resemblance to models of navigation through Euclidean spaces. However, few explicit links have been made between predictions from Euclidean spatial navigation and neural activity during human sequence learning. Here, we use a combination of behavioral modeling and intracranial encephalography (iEEG) recordings to investigate how neural activity might support the formation of space-like cognitive maps through temporal discounting during sequence learning. Specifically, we acquire human reaction times from a sequential reaction time task, to which we fit a model that formulates the amount of temporal discounting as a single free parameter. From the parameter, we calculate each individuals estimate of the latent space. We find that neural activity reflects these estimates mostly in the temporal lobe, including areas involved in spatial navigation. Similar to spatial navigation, we find that low dimensional representations of neural activity allow for easy separation of important features, such as modules, in the latent space. Lastly, we take advantage of the high temporal resolution of iEEG data to determine the time scale on which latent spaces are learned. We find that learning typically happens within the first 500 trials, and is modulated by the underlying latent space and the amount of temporal discounting characteristic of each participant. Ultimately, this work provides important links between behavioral models of sequence learning and neural activity during the same behavior, and contextualizes these results within a broader framework of domain general cognitive maps.

neuroscience↗

bioRxiv2020

Efficient Coding in the Economics of Human Brain Connectomics

In systems neuroscience, most models posit that brain regions communicate information under constraints of efficiency. Yet, evidence for efficient communication in structural brain networks characterized by hierarchical organization and highly connected hubs remains sparse. The principle of efficient coding proposes that the brain transmits maximal information in a metabolically economical or compressed form to improve future behavior. To determine how structural connectivity supports efficient coding, we develop a theory specifying minimum rates of message transmission between brain regions to achieve an expected fidelity, and we test five predictions from the theory based on random walk communication dynamics. In doing so, we introduce the metric of compression efficiency, which quantifies the trade-off between lossy compression and transmission fidelity in structural networks. In a large sample of youth (n = 1,042; age 8-23 years), we analyze structural networks derived from diffusion weighted imaging and metabolic expenditure operationalized using cerebral blood flow. We show that structural networks strike compression efficiency trade-offs consistent with theoretical predictions. We find that compression efficiency prioritizes fidelity with development, heightens when metabolic resources and myelination guide communication, explains advantages of hierarchical organization, links higher input fidelity to disproportionate areal expansion, and shows that hubs integrate information by lossy compression. Lastly, compression efficiency is predictive of behavior--beyond the conventional network efficiency metric--for cognitive domains including executive function, memory, complex reasoning, and social cognition. Our findings elucidate how macroscale connectivity supports efficient coding, and serve to foreground communication processes that utilize random walk dynamics constrained by network connectivity. Author SummaryMacroscale communication between interconnected brain regions underpins most aspects of brain function and incurs substantial metabolic cost. Understanding efficient and behaviorally meaningful information transmission dependent on structural connectivity has remained challenging. We validate a model of communication dynamics atop the macroscale human structural connectome, finding that structural networks support dynamics that strike a balance between information transmission fidelity and lossy compression. Notably, this balance is predictive of behavior and explanatory of biology. In addition to challenging and reformulating the currently held view that communication occurs by routing dynamics along metabolically efficient direct anatomical pathways, our results suggest that connectome architecture and behavioral demands yield communication dynamics that accord to neurobiological and information theoretical principles of efficient coding and lossy compression.

neuroscience↗

bioRxiv2018

A pairwise maximum entropy model uncovers the white matter scaffold underlying emergent dynamics in intracranial EEG

A major challenge in systems neuroscience is to understand how the brains structural architecture gives rise to its complex functional dynamics. Here, we address this challenge by examining the inter-ictal activity of five patients with medically refractory epilepsy during [~] 15 hours of multi-channel intracranial recording. By constructing a pairwise maximum entropy model (MEM) of the observed neural dynamics, we seek to uncover the fundamental relationship between functional activity and its underlying structural substrate. Despite only incorporating the pairwise correlations in the observed neural activity, we find that the pairwise MEM robustly fits large-scale patterns of inter-ictal power dynamics across a wide range of frequency bands, notably displaying time-invariance and cross-frequency similarity. Furthermore, across all frequency bands, we demonstrate that the pairwise MEM accurately identifies the structural white matter connections between brain regions, outperforming other common model-free measures of functional connectivity. Together, our findings show that a simple pairwise MEM, which is explicitly ignorant of higher-order correlations between three or more brain regions, not only captures complex spatiotemporal patterns of neural activity across the frequency spectrum, but also suggests that the network of structural connections in the human brain is a plausible scaffold capable of supporting observed wide-band neural dynamics.

neuroscience↗