Biology subjects

Fosque, L. J.

Publications and source records attributed to Fosque, L. J..

3 recordsLinked to original sources

bioRxiv2026

Amyloidosis and tauopathy progressively disrupt near-critical dynamics in selectively vulnerable circuits

Mechanistically distinct neurodegenerative diseases result in similar cognitive deficits, and pathology is often concentrated in a set of circuits known to be selectively vulnerable. Selective vulnerability is an intrinsic property of a circuit: in APP/PS1 mice, where plaques are global, functional disruptions are confined to circuits within the default mode network. What characterizes the dynamics of vulnerable circuits from those of spared circuits is unknown. We asked whether the distinguishing property is the proximity of population dynamics to criticality, a homeostatic set-point of network activity that determines computational capacity. We recorded single units simultaneously in three selectively vulnerable regions (retrosplenial cortex, anterior cingulate cortex, and CA1 hippocampus) and in primary visual cortex of freely behaving young and old APP/PS1 and wild-type mice. Firing rate and spike-time variability differed by genotype inconsistently across regions and ages. In contrast, three measures from criticality theory each revealed deficits that emerged only with age and only in the vulnerable regions. No measure detected a progressive change in V1. Avalanche scaling is not fixed in neural activity, but varies along a scaling line as the network state changes. As a result, the lines generated by each genotype revealed a constitutive effect of the transgene in young animals, which inverted with age in amyloid-bearing animals, but only in vulnerable circuits. If the destabilization of near-critical dynamics directly reflects a circuit's functional capacity, the same signature should identify a mechanistically distinct degenerative disease. In a model of tauopathy (P301S/E4), CA1 dynamics were displaced from the control scaling line with the same age-dependent reversal observed in APP/PS1. Our results suggest a network-level principle of neurodegenerative diseases: molecularly unrelated pathologies converge on the loss of near-critical dynamics in selectively vulnerable circuits.

neuroscience↗

bioRxiv2026

A single dynamical property can account for the capacity to learn, from artificial networks to the mammalian brain.

SUMMARY STATEMENTEvery brain must adapt to an unpredictable world, yet individuals differ in how readily they learn. Theoretical work suggests that learning is fastest when a system, whether biological or synthetic, is initialized in a state close to instability - i.e., near criticality - because critical dynamics are imbued with a diverse repertoire of patterns and multi-scale correlations. Here, we empirically estimate distance to criticality in the brain and show that it predicts the rate of adaptability underlying learning, neuronal tuning, and general intelligence. In mouse motor cortex, proximity to criticality forecasts the learning rate of two future, complex tasks: prey-capture hunting and ladder crossing. In contrast, distance to criticality predicted neither an animals naive ability nor its asymptotic skill -- isolating the rate of learning itself. In visual cortex of young ferrets, proximity to criticality predicts how strongly subsequent experience reshapes neural tuning. In human frontal cortex, it correlates with general cognitive ability. A minimal recurrent network model reproduced these results and offers a mechanism: proximity to criticality defines the timescale over which a system can learn from its past experiences, directly setting the rate of learning.

neuroscience↗

bioRxiv2025

Two views of the brain are reconciled by a unifying principle of maximal information processing

There is selective pressure on brains to maximize computational capacity and adaptability in an unpredictable world. Prior work suggests that this demand is satisfied by a regime called criticality, which has emerged as a powerful, unifying framework for understanding how computation can arise in biological systems. However, this framework has been confined to high-dimensional network models. At first glance, this appears irreconcilable with many of the foundational, low dimensional dynamical models that have driven progress in theoretical and computational neuroscience for a century. If criticality is a universal principle, then all models that accurately capture significant aspects of brain function should be constrained by the same fact. Lacking a definition of criticality in low-dimensional dynamical systems, this has been impossible to evaluate. Here, we develop a mathematical definition of criticality that transcends dimensionality by recognizing temporal scale invariance as analogous to spatial scale invariance that defines criticality in large systems. We demonstrate that there are two mechanistically distinct sources of criticality at bifurcations, one deterministic and one that emerges from noise fluctuations. Further, we show that some but not all canonical bifurcations in neural models exhibit criticality, and only a subset of these are biologically plausible. We conduct numerical analyses demonstrating that information processing capacity peaks at critical bifurcations, and evaluate which historically influential neural models contain these bifurcations. Our results establish criticality as a universal neurobiological principle that is accessible to systems of any dimensionality. This unifies disparate modeling approaches under a single computational framework and suggests that optimal information processing emerges not from model-specific mechanisms but from fundamental properties of critical dynamics themselves.

neuroscience↗