Biology subjects

Shew, W.

Publications and source records attributed to Shew, W..

15 recordsLinked to original sources

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.

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bioRxiv2026

From criticality to cognitive effort: scale-invariant EEG dynamics supporting cognitive flexibility are suppressed by effort

The critical brain hypothesis contends that brains operate near a phase transition where excitation and inhibition are balanced, enabling neural dynamics to rapidly adapt and reorganize for cognitive demands. Allocating control resources to maintain stable task representations likely shifts brains away from criticality. Here, we test whether proximity to criticality indexes the balance between flexible adaptation and effortful task engagement. To do so, we adapt a time-resolved measure of scale invariance in EEG amplitude fluctuations (d2), capable of quantifying distance-to-criticality under non-stationary conditions - as during cognitive tasks. We benchmark our measure using ground-truth simulations of a neural mass model and show that d2 is lowest when excitation and inhibition are balanced. Next, we apply d2 to data collected during a task-switching paradigm and find that more demanding trials increased deviation from criticality, whereas greater flexibility, faster responses, and higher accuracy occurred closer to criticality. These effects were region-specific: deviation at posterior electrodes predicted worse performance, while deviations at frontal midline electrodes predicted better performance. Together, these results suggest that deviations from criticality reflect both cognitive load and effort exertion, highlighting EEG amplitude scale-invariance as a sensitive marker of adaptive neural dynamics under cognitive demand.

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bioRxiv2026

Emergent critical oscillations in motor cortex of Parkinson's patients

The dynamical state of cortical neural activity constrains the complexity of functions it can perform. A marginally stable dynamical state - called criticality - is thought to be beneficial for brain functions that require multiple time scales, broad dynamic range, and large information storage and transmission. A growing body of evidence suggests that criticality is a feature of healthy brain dynamics, but breaks down in certain brain disorders. Here we ask whether Parkinsons disease incurs deviation from criticality compared to healthy controls. We analyze human resting state EEG activity in primary motor cortex. Parkinsons patients exhibit prominent oscillatory brain activity in multiple frequency bands (low delta and high theta) that is not present in controls. Surprisingly, we find that these emergent oscillations are close to criticality, i.e., amplitude fluctuations with approximate temporal scale invariance. We compare traditional signatures of criticality and more principled measurements of proximity to criticality using our new approach based on information theory and temporal renormalization group theory. Our new approach and traditional methods agree, demonstrating that critical dynamics are not always associated with healthy states; Parkinsons disease is associated with the emergence of near-critical oscillations in motor cortex. Author SummaryBrain function is thought to be optimal when its activity is near the border of order and chaos -- a state called criticality. This state is thought to help the brain stay flexible and process information efficiently. We investigate whether Parkinsons disease disrupts this balance like in other diseases and pathologies. Using resting EEG brain activity, we found that people with Parkinsons show strong rhythmic signals not seen in healthy brains, and surprisingly these rhythms are also near the critical state. Using both established and new theoretical tools, we show that critical dynamics can accompany disease, suggesting that being closer to criticality is not always a sign of healthy brain function.

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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.

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bioRxiv2025

Is critical brain dynamics more prevalent than previously thought?

The hypothesis that the brain operates near criticality has far-reaching implications for brain function and is supported by growing experimental evidence. Observations of scale-invariant brain activity agree with this hypothesis, but what about when brain activity is not scale-invariant? Should we reject the criticality hypothesis When power-laws poorly fit the data or when strong oscillations occur (dominated by a specific time scale)? Here we show several ways that criticality can be hidden from traditional data analytic approaches, leading to false negative conclusions. We use a parsimonious high-dimensional model to demonstrate how neural systems may separate different dynamical modes into different subspaces, simultaneously generating non-critical dynamics, critical oscillations, and scale-invariant avalanches. Our results point to a need for new methods capable of revealing hidden criticality and suggest that criticality could be more prevalent than previously thought, hidden in subspaces not readily revealed by standard data analyses. Does the brain operate in a dynamical regime close to a critical phase transition? This question is fundamental; the nature of neural computation is strongly impacted by whether a system is close to or far from criticality [1-5]. Evidence suggesting that the brain operates close to criticality has accumulated at an accelerating pace over the past two decades (a recent meta analysis found 31 reports in 2024 alone [2]). The two most common types of experimental evidence have been based on neuronal avalanche analysis [1, 6, 7] and long-range temporal correlation analysis [8-12]. Both these approaches examine a one-dimensional time series of collective neural activity, averaged over large populations of neurons, seeking temporally-correlated, scale-invariant fluctuations that obey specific scaling laws predicted by theory. In many cases, measurements agree with these predictions; the success and persistence of the criticality hypothesis rests on these cases. However, it is not difficult to find measurements that do not agree, with fluctuations poorly described by power-laws and scaling laws. Another common observation that, naively, seems to contradict criticality is oscillatory brain activity with a particular dominant frequency. At first glance, either the lack of power-laws or the prominence of a dominant oscillatory time scale appear to contradict the criticality hypothesis. What should we conclude from these apparently contradictory observations? Assuming that all the experimental observations are sound and valid, there are two possible explanations, not necessarily mutually exclusive. First, it could be that there are biophysical control parameters constantly in flux in the brain, causing shifts in proximity to criticality. In this scenario, non-scale-invariant data reflect true deviations from criticality and we should conclude that the brain is, at times, closer to and, at other times, further from criticality. This view has been proposed in multiple previous studies and is consistent with substantial experimental evidence [6, 13-19]. However, here we argue that there is an important, but rarely considered, second plausible explanation. It could be that traditional methods for assessing criticality can be fooled. In this case, the system dynamics could be truly critical and scale-invariant, but not visible to traditional methods. We first show why such hidden criticality is plausible considering general mathematical arguments and relevant experimental evidence. Then, we use a parsimonious computational model to demonstrate concrete examples of ground truth scale-invariant critical dynamics that are missed by traditional analyses of population average activity. Moreover, our model shows how critical oscillations, avalanches, and other non-critical modes of dynamics can coexist, by separating each different mode into a distinct low-dimensional subspace. All together, our results suggest that the combination of these various subspaces make up the high-dimensional dynamical system commonly observed in the brain. Our results reconcile multiple, seemingly discrepant, experimental observations and describe prospects for new methods that can reveal hidden criticality.

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bioRxiv2025

Defining and measuring proximity to criticality

Over the half century since the renormalization group (RG) brought about deep understanding of critical phenomena in condensed matter physics, it has been claimed that diverse social, engineered, astrophysical, and biological systems operate close to criticality. However, these systems do not afford the neat phase diagrams and exquisite control available in condensed matter physics. How can one assess proximity to criticality when control parameters are unknown, difficult to manipulate experimentally, and fluctuating in response to changing environmental or internal conditions? Here we meet this challenge with a rigorous theoretical framework and data-analytic strategy for measuring proximity to criticality from observed system dynamics. We developed a temporal RG, well-suited to commonly measured time series, and an information theoretic quantification of proximity to criticality that is independent of model parameterization. After benchmarking our approach on diverse ground-truth cases, we apply it to recordings of spiking activity in the mammalian brain, addressing a long-standing controversy. We show that brain dynamics shift closer to criticality during wakefulness and shift away during deep sleep.

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bioRxiv2024

Coding odor modality in piriform cortex efficiently with low-dimensional subspaces: a Shared Covariance Decoding approach

A fundamental question in neuroscience is how sensory signals are decoded from noisy cortical activity. We address this question in the olfactory system, decoding the route by which odorants arrive into the nasal cavity: through the nostrils (orthonasal), or through the back of the throat (retronasal). We recently showed with modeling and novel experiments on anesthetized rats that orthonasal versus retronasal modality information is encoded in the olfactory bulb (OB, a pre-cortical region). However, key questions remain: is modality information transmitted from OB to anterior piriform cortex (aPC)? How can this information be extracted from a much noisier cortical population with overall less firing? With simultaneous spike recordings of populations of neurons in OB and aPC, we show that an unsupervised and biologically plausible algorithm, Shared Covariance Decoding (SCD), can indeed linearly encode modality in low dimensional subspaces. Specifically, SCD improves encoding of ortho/retro in aPC compared to Fishers linear discriminant analysis (LDA). Consistent with our theoretical analysis, when noise correlations between OB and aPC are low and OB well-encodes modality, modality in aPC tends to be encoded optimally with SCD. We observe that with several algorithms (LDA, SCD, optimal) that the decoding accuracy distributions are invariant when GABAA (ant-)agonists (bicuculline and muscimol) are applied to OB, consistent invariance in population firing in aPC. Overall, we show modality information can be encoded efficiently in piriform cortex.

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bioRxiv2024

Cortex deviates from criticality during action and deep sleep: a temporal renormalization group approach

The hypothesis that the brain operates near criticality explains observations of complex, often scale-invariant, neural activity. However, the brain is not static, its dynamical state varies depending on what an organism is doing. Neurons often become more synchronized (ordered) during unconsciousness and more desynchronized (disordered) in highly active awake conditions. Are all these states equidistant from criticality; if not, which is closest? The fundamental physics of how systems behave near criticality came from renormalization group (RG) theory, but RG for neural systems remains largely undeveloped. Here we developed a temporal RG (tRG) theory for analysis of typical neuroscience data. We mathematically identified multiple types of criticality (tRG fixed points) and developed tRG-driven data analytic methods to assess proximity to each fixed point based on relatively short time series. Unlike traditional methods for studying criticality in neural systems, our tRG approach allows time-resolved measurements of distance from criticality in experiments at behaviorally relevant timescales. We apply our approach to recordings of spike activity in mouse visual cortex, showing that the relaxed, awake state is closest to criticality. When arousal shifts away from this state - either increasing in more active awake states or decreasing in deep sleep - cortical dynamics deviate from criticality.

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bioRxiv2023

When random variation results in functional significance

Many functional properties vary dramatically across neurons in cerebral cortex. Two fundamental goals of systems neuroscience are to determine which neurons execute which functions and how the different functional properties of a neuron are related. Often, it is assumed that if two properties are uncorrelated, then there is no important relationship to report. Here we show that this assumption can lead to wrong conclusions; functional segregation can emerge, by chance, due to random variation when that variation is distributed according to skewed, heavy-tailed distributions. We reinterpret the results we previously reported in Kells et al 2019 [1], showing that they are a prime example of functional segregation due to random variation.

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bioRxiv2023

Modality of odor perception is transmitted to cortical brain regions from the olfactory bulb

Odor perception is the impetus for important animal behaviors with two predominate modes of processing: odors pass through the front of the nose (orthonasal) while inhaling and sniffing, or through the rear (retronasal) during exhalation and while eating. Despite the importance of olfaction for an animals well-being and that ortho and retro naturally occur, it is unknown how the modality (ortho versus retro) is even transmitted to cortical brain regions, which could significantly affect how odors are processed and perceived. Using multi-electrode array recordings in tracheotomized anesthetized rats, which decouples ortho-retro modality from breathing, we show that mitral cells in rat olfactory bulb can reliably and directly transmit ortho versus retronasal modality with ethyl butyrate, a common food odor. Drug manipulations affecting synaptic inhibition via GABAA lead to worse decoding of ortho versus retro, independent of whether overall inhibition increases or decreases, suggesting that the olfactory bulb circuit may naturally favor encoding this important aspect of odors. Detailed data analysis paired with a firing rate model that captures population trends in spiking statistics shows how this circuit can encode odor modality. We have not only demonstrated that ortho/retro information is encoded to downstream brain regions, but also use modeling to demonstrate a plausible mechanism for this encoding: due to synaptic adaptation, it is the slower time course of the retronasal stimulation that causes retronasal responses to be stronger and less sensitive to inhibitory drug manipulations than orthonasal responses. New and NoteworthyWhether ortho (sniffing odors) versus retro (exhalation and eating) is encoded from the olfactory bulb to other brain areas is not completely known. Using multi-electrode array recordings in anesthetized rats, we show that the olfactory bulb transmits this information downstream via spikes. Altering inhibition degrades ortho/retro information on average. We use theory and computation to explain our results, which should have implications on cortical processing considering that only food odors occur retronasally.

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bioRxiv2023

Low dimensional criticality embedded in high dimensional awake brain dynamics

Whether cortical neurons operate in a strongly or weakly correlated dynamical regime determines fundamental information processing capabilities and has fueled decades of debate. Here we offer a resolution of this debate; we show that two important dynamical regimes, typically considered incompatible, can coexist in the same local cortical circuit by separating them into two different subspaces. In awake mouse motor cortex, we find a low-dimensional subspace with large fluctuations consistent with criticality - a dynamical regime with moderate correlations and multi-scale information capacity and transmission. Orthogonal to this critical subspace, we find a high-dimensional subspace containing a desynchronized dynamical regime, which may optimize input discrimination. The critical subspace is apparent only at long timescales, which explains discrepancies among some previous studies. Using a computational model, we show that the emergence of a low-dimensional critical subspace at large timescale agrees with established theory of critical dynamics. Our results suggest that cortex leverages its high dimensionality to multiplex dynamical regimes across different subspaces. TeaserTemporal coarse-graining reveals a low-dimensional critical subspace coexistent with a desynchronized subspace in awake cortex.

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bioRxiv2022

Sensory input to cortex encoded on low-dimensional periphery-correlated subspaces

As information about the world is conveyed from the sensory periphery to central neural circuits, it mixes with complex ongoing cortical activity. How do neural populations keep track of sensory signals, separating them from noisy ongoing activity? Here we show that sensory signals are encoded more reliably in certain low-dimensional subspaces. These coding subspaces are defined by correlations between neural activity in primary sensory cortex and upstream sensory brain regions; the most correlated dimensions were best for decoding. We analytically show that these correlation-based coding subspaces improve, reaching optimal limits (without an ideal observer) as noise correlations between cortex and upstream regions are reduced. We show that this principle generalizes across diverse sensory stimuli in the olfactory system and the visual system of awake mice. Our results demonstrate an algorithm the cortex may use to multiplex different functions, processing sensory input in low dimensional subspaces separate from other ongoing functions. Significance statementTraditionally, primary sensory cortex was thought to have one job - processing sensory signals. As technical advances allow more holistic measurements of the brain and body in action, it has become clear that primary sensory cortex is involved with many other aspects of brain function, not just dealing with sensory input. How can a single neural circuit juggle multiple jobs simultaneously? Here we use numerical, analytical, and experimental methods to demonstrate an algorithm the brain may use to solve this problem by separating different jobs into different subspaces defined by correlations between primary sensory cortex and the brain regions that source the sensory input signals.

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bioRxiv2020

Selective participation of single cortical neurons in neuronal avalanches

Neuronal avalanches are scale-invariant neuronal population activity patterns in cortex that emerge in vivo in the awake state and in vitro during balanced excitation and inhibition. Theory and experiments suggest that avalanches indicate a state of cortex that improves numerous aspects of information processing by allowing for the transient and selective formation of local as well as system-wide spanning neuronal groups. If avalanches are indeed involved with information processing, one might expect that particular single neurons would participate in particular avalanche patterns selectively. Alternatively, all neurons could participate with equal likelihood in each avalanche as would be expected for a population rate code. Distinguishing these hypotheses, however, has been difficult as robust avalanche analysis requires technically challenging measures of their intricate organization in space and time at the population level, while also recording sub- or suprathreshold activity from individual neurons with high temporal resolution. Here we identify repeated avalanches in the ongoing local field potential (LFP) measured with high-density microelectrode arrays in the cortex of awake nonhuman primates and in acute cortex slices from rats. We studied extracellular unit firing in vivo and intracellular responses of pyramidal neurons in vitro. We found that single neurons participate selectively in specific LFP-based avalanche patterns. Furthermore, we show in vitro that manipulating the balance of excitation and inhibition abolishes this selectivity. Our results support the view that avalanches represent the selective, scale-invariant formation of neuronal groups in line with the idea of Hebbian cell assemblies underlying cortical information processing.

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bioRxiv2019

Tuning network dynamics from criticality to the chaotic balanced state

According to many experimental observations, neurons in cerebral cortex tend to operate in an asynchronous regime, firing independently of each other. In contrast, many other experimental observations reveal cortical population firing dynamics that are relatively coordinated and occasionally synchronous. These discrepant observations have naturally led to a lively debate surrounding discrepant hypotheses. A commonly hypothesized explanation of asynchronous firing is that excitatory and inhibitory neurons are precisely correlated, nearly canceling each other, resulting in the so-called chaotic balanced state. On the other hand, the criticality hypothesis posits an explanation of the more coordinated state that also requires a certain balance of excitatory and inhibitory interactions. Both hypotheses claim the same qualitative mechanism - properly balanced excitation and inhibition. Thus, a natural question arises: how are the chaotic balanced state and criticality related, how do they differ? Here we propose an answer to this question based on investigation of a simple, network-level computational model. We show that the strength of inhibitory synapses relative to excitatory synapses can be tuned from weak to strong to generate a family of models that spans a continuum from criticality to the chaotic balanced state. Our results bridge two long-standing competing hypotheses and offer a possible explanation of discrepant experimental observations: neuromodulatory mechanisms that tune the strength of excitatory and inhibitory synapses may tune cortical state from criticality to the balanced state, and to intermediate states between these extremes. Author summaryWhat is the dynamical state of cerebral cortex? Are neurons mostly uncorrelated, firing asynchronously with each other? Are synchronous oscillations important? The answers to these questions have fundamental implications for how the cortical neural population encodes and processes information. Here we show that two possible scenarios - criticality and the chaotic balanced state - that are typically considered incompatible can be attained in the same network by maintaining a certain kind of balance, while tuning the strength of inhibition relative to excitation.

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bioRxiv2017

Visual Response Properties In The Three Layer Turtle Visual Cortex

Turtle dorsal cortex provides us with unique insights into cortical processing. It is known to share many features with the mammalian hippocampus and olfactory cortex as well as geniculo-cortical areas in stem amniotes from which mammals evolved. To this end, we have used data from extracellular recordings from microelectrode arrays to study spatial and temporal patterns of responses to visual stimuli as seen in both local field potential and action potentials. We discovered surprisingly large receptive fields, responsiveness to a broad range of stimuli, and high correlation between distant neural ensembles across recording array. Moreover, we found significant response variability regarding latency and strength in the presence of adaptation to both ongoing and visually evoked activity.

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