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Biology subjects

Hengen, K. B.

Publications and source records attributed to Hengen, K. B..

At least 19 recordsLinked to original sources

Mice use predictive environmental cues to adapt predatory behavior in hunting tasks of variable difficulty

Prey capture has long served as a model of complex, multimodal behavior with ethological relevance. In this context, the ability of laboratory animals to capture fleeing prey has been investigated in terms of the sensory systems and neuroanatomical regions required for successful capture. While the ability to hunt is experience-dependent, prey capture is often assumed to be a reflexive behavior, governed by innate neural circuits and a basic drive to obtain food. Whether hunting performance depends on complex learning and cognitive strategy remains largely untested, in part because it is difficult to systematically manipulate the difficulty of predator-prey interactions. To address this, we varied ambient temperature across thousands of trials in which laboratory mice pursued cold-blooded cockroaches, whose movement speed was directly determined by body temperature. As expected, capture difficulty increased as a function of temperature. To test whether mice rely on external cues to adjust their predatory behavior, we then mismatched the ambient temperature and that of the prey, finding that mice learn to use relevant environmental information to inform their hunting strategy. Our results suggest that animals can extract and apply meaningful information from predictive environmental cues to adapt their learned behavior.

animal behavior and cognition↗

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↗

Environmental complexity reveals memory-guided search as the locus of learning in prey capture

Ethological behavior requires the integration of perception, memory, and decision-making, yet standard laboratory tasks are often too simplified to recruit these processes jointly. Here, mice that were expert hunters in a bare arena were challenged to capture live insect prey among objects that occlude vision and afford opportunities to hide. This revealed an axis of learning absent in an empty arena: mice improved not by refining pursuit but by reorganizing how they searched for concealed prey. An agent-based model showed that short-term memory and learning of object-specific values are sufficient to account for the behavior demonstrated by mice. A compact ethogram of object-directed actions revealed that animals selectively acquired the interactions most likely to expose prey. These results show that environmental complexity recruits memory- and value-guided search strategies. Reproducible with inexpensive materials, the paradigm and its open-source analyses offer a sensitive read-out of cognitive processes largely unobservable in conventional assays.

neuroscience↗

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↗

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.

neuroscience↗

Temperature-dependent predation predicts a more reptilian future

Vertebrate diversity increases toward the tropics, but the extent to which this pattern varies with thermoregulatory strategy is unknown. A strong divergence, if confirmed, would imply a global restructuring of vertebrate communities with temperature. Here we present evidence for a novel latitudinal and thermal gradient of comparative vertebrate diversity. Synthesizing over 34,000 terrestrial species distributions, we observe a two-orders-of-magnitude shift in comparative richness with temperature, from endothermic dominance in temperate habitats and high elevations, toward parity with ectotherms in the lowland tropics. Next, we provide experimental support for an underlying thermal gradient of predation. Using machine vision tracking in over 4,500 endotherm-ectotherm predation trials, we show that thermally-mediated differences in performance favor endotherm predators in colder conditions and yield theoretically predicted outcomes, including strike count, distance traveled, and time to capture ectotherm prey. Finally, we integrate theory and data to forecast future patterns of diversity, revealing that as the world gets warmer, it will become increasingly reptilian.

ecology↗

Is criticality a unified set-point of brain function?

Brains face selective pressure to optimize computation, broadly defined. This optimization is achieved by myriad mechanisms and processes that influence the brains computational state. These include development, plasticity, homeostasis, and more. Despite enormous variability over time and between individuals, do these diverse mechanisms converge on the same set-point? Is there a universal computational optimum around which the healthy brain tunes itself? The criticality hypothesis posits such a unified computational set-point. Criticality is a special dynamical brain state, defined by internally-generated multi-scale, marginally-stable dynamics which maximize many features of information processing. The first experimental support for this hypothesis emerged two decades ago, and evidence has accumulated at an accelerating pace, despite a contentious history. Here, we lay out the logic of criticality as a general computational end-point and systematically review experimental evidence for the hypothesis. We perform a meta-analysis of 143 datasets from manuscripts published between 2003 and 2024. To our surprise, we find that a long-standing controversy in the field is the product of a simple methodological choice that has no bearing on underlying dynamics. Our results suggest that a new generation of research can leverage the concept of criticality--as a unifying principle of brain function-to accelerate our understanding of behavior, cognition, and disease.

neuroscience↗

A conserved code for anatomy: Neurons throughout the brain embed robust signatures of their anatomical location into spike trains.

Neurons in the brain are known to encode diverse information through their spiking activity, primarily reflecting external stimuli and internal states. However, whether individual neurons also embed information about their own anatomical location within their spike patterns remains largely unexplored. Here, we show that machine learning models can predict a neurons anatomical location across multiple brain regions and structures based solely on its spiking activity. Analyzing high-density recordings from thousands of neurons in awake, behaving mice, we demonstrate that anatomical location can be reliably decoded from neuronal activity across various stimulus conditions, including drifting gratings, naturalistic movies, and spontaneous activity. Crucially, anatomical signatures generalize across animals and even across different research laboratories, suggesting a fundamental principle of neural organization. Examination of trained classifiers reveals that anatomical information is enriched in specific interspike intervals as well as responses to stimuli. Within the visual isocortex, anatomical embedding is robust at the level of layers and primary versus secondary but does not robustly separate individual secondary structures. In contrast, structures within the hippocampus and thalamus are robustly separable based on their spike patterns. Our findings reveal a generalizable dimension of the neural code, where anatomical information is multiplexed with the encoding of external stimuli and internal states. This discovery provides new insights into the relationship between brain structure and function, with broad implications for neurodevelopment, multimodal integration, and the interpretation of large-scale neuronal recordings. Computational approximations of anatomy have potential to support in-vivo electrode localization.

neuroscience↗

At the onset of active whisking, the input layer of barrel cortex exhibits a 24 h window of increased excitability that depends on prior experience.

The development of motor control over sensory organs is a critical milestone, enabling active explo-ration and shaping of the sensory environment. Whether the onset of sensory organ motor control directly influences the development of corresponding sensory cortices remains unknown. Here, we confirm and exploit the late onset of whisking behavior in mice to address this question in the somatosensory system. Using ex vivo electrophysiology, we describe a transient increase in the intrinsic excitability of excitatory neurons in layer IV of the barrel cortex, which processes whisker input, immediately following the onset of active whisking around on postnatal days 13 and 14. This increase in neuronal gain is specific to layer IV, independent of changes in synaptic strength, and requires prior sensory experience. Further, these effects are not expressed in inhibitory in-terneurons in barrel cortex. The transient increase in excitability is not evident in layer II/III of barrel cortex or in the visual cortex upon eye opening, suggesting a unique interaction between the development of active sensing and the thalamocortical input layer in the somatosensory iso-cortex. Predictive modeling indicates that, immediately following the onset of active whisking, changes in active membrane conductances alone can reliably distinguish neurons in control but not whisker-deprived hemispheres. Our findings demonstrate an experience-dependent, lamina-specific refinement of neuronal excitability tightly linked to the emergence of active whisking. This tran-sient increase in the gain of the thalamic input layer coincides with a critical period for synaptic plasticity in downstream layers, suggesting a role in cortical maturation and sensory processing.

neuroscience↗

Protosequences in human cortical organoids model intrinsic states in the developing cortex

Neuronal firing sequences are thought to be the basic building blocks of neural coding and information broadcasting within the brain. However, when sequences emerge during neurodevelopment remains unknown. We demonstrate that structured firing sequences are present in spontaneous activity of human and murine brain organoids and ex vivo neonatal brain slices from the murine somatosensory cortex. We observed a balance between temporally rigid and flexible firing patterns that are emergent phenomena in human and murine brain organoids and early postnatal murine somatosensory cortex, but not in primary dissociated cortical cultures. Our findings suggest that temporal sequences do not arise in an experience-dependent manner, but are rather constrained by an innate preconfigured architecture established during neurogenesis. These findings highlight the potential for brain organoids to further explore how exogenous inputs can be used to refine neuronal circuits and enable new studies into the genetic mechanisms that govern assembly of functional circuitry during early human brain development.

neuroscience↗

Tauopathy severely disrupts homeostatic set-points in emergent neural dynamics but not the activity of individual neurons.

The homeostatic regulation of neuronal activity is essential for robust computation; key set-points, such as firing rate, are actively stabilized to compensate for perturbations. From this perspective, the disruption of brain function central to neurodegenerative disease should reflect impairments of computationally essential set-points. Despite connecting neurodegeneration to functional outcomes, the impact of disease on set-points in neuronal activity is unknown. Here we present a comprehensive, theory-driven investigation of the effects of tau-mediated neurodegeneration on homeostatic set-points in neuronal activity. In a mouse model of tauopathy, we examine 27,000 hours of hippocampal recordings during free behavior throughout disease progression. Contrary to our initial hypothesis that tauopathy would impact set-points in spike rate and variance, we found that cell-level set-points are resilient to even the latest stages of disease. Instead, we find that tauopathy disrupts neuronal activity at the network-level, which we quantify using both pairwise measures of neuron interactions as well as measurement of the networks nearness to criticality, an ideal computational regime that is known to be a homeostatic set-point. We find that shifts in network criticality 1) track with symptoms, 2) predict underlying anatomical and molecular pathology, 3) occur in a sleep/wake dependent manner, and 4) can be used to reliably classify an animals genotype. Our data suggest that the critical set-point is intact, but that homeostatic machinery is progressively incapable of stabilizing hippocampal networks, particularly during waking. This work illustrates how neurodegenerative processes can impact the computational capacity of neurobiological systems, and suggest an important connection between molecular pathology, circuit function, and animal behavior.

neuroscience↗

A non-oscillatory, millisecond-scale embedding of brain state provides insight into behavior.

Sleep and wake are understood to be slow, long-lasting processes that span the entire brain. Brain states correlate with many neurophysiological changes, yet the most robust and reliable signature of state is enriched in rhythms between 0.1 and 20 Hz. The possibility that the fundamental unit of brain state could be a reliable structure at the scale of milliseconds and microns has not been addressed due to the physical limits associated with oscillation-based definitions. Here, by analyzing high resolution neural activity recorded in 10 anatomically and functionally diverse regions of the murine brain over 24 h, we reveal a mechanistically distinct embedding of state in the brain. Sleep and wake states can be accurately classified from on the order of 100 to 101 ms of neuronal activity sampled from 100 m of brain tissue. In contrast to canonical rhythms, this embedding persists above 1,000 Hz. This high frequency embedding is robust to substates and rapid events such as sharp wave ripples and cortical ON/OFF states. To ascertain whether such fast and local structure is meaningful, we leveraged our observation that individual circuits intermittently switch states independently of the rest of the brain. Brief state discontinuities in subsets of circuits correspond with brief behavioral discontinuities during both sleep and wake. Our results suggest that the fundamental unit of state in the brain is consistent with the spatial and temporal scale of neuronal computation, and that this resolution can contribute to an understanding of cognition and behavior.

neuroscience↗

Visual deprivation during mouse critical period reorganizes network-level functional connectivity

A classic example of experience-dependent plasticity is ocular dominance (OD) shift, in which the responsiveness of neurons in the visual cortex is profoundly altered following monocular deprivation (MD). It has been postulated that OD shifts also modify global neural networks, but such effects have never been demonstrated. Here, we used longitudinal wide-field optical calcium imaging to measure resting-state functional connectivity during acute (3-day) MD in mice. First, delta GCaMP6 power in the deprived visual cortex decreased, suggesting that excitatory activity was reduced in the region. In parallel, interhemispheric visual homotopic functional connectivity was rapidly reduced by the disruption of visual drive through MD and was sustained significantly below baseline state. This reduction of visual homotopic connectivity was accompanied by a reduction in parietal and motor homotopic connectivity. Finally, we observed enhanced internetwork connectivity between visual and parietal cortex that peaked at MD2. Together, these findings support the hypothesis that early MD induces dynamic reorganization of disparate functional networks including association cortices. Significance StatementMonocular deprivation during the visual critical period triggers several plasticity mechanisms that collaborate to shift the excitability of neurons in the visual cortex. However, little is known about the impacts of MD on cortex-wide functional networks. Here, we measured cortical functional connectivity during short-term critical period MD. We demonstrate that critical period MD has immediate effects on functional networks beyond the visual cortex, and identify regions of substantial functional connectivity reorganization in response to MD.

neuroscience↗

Sleep restores an optimal computational regime in cortical networks

SUMMARYSleep is vitally important for brain function, yet its core restorative process remains an open question. Sleep is assumed to subserve homeostatic processes in the brain because sleep restores functional capacity, and stable function requires compensatory tuning of circuits in the face of experience. However, the set-point around which sleep tunes circuit computation is unknown; for more than four decades, the homeostatic aspect of sleep has been approximated by a heuristic model whose strongest correlate is Slow-wave Activity (SWA). While SWA can indicate sleep pressure, it fails to explain why animals need sleep. In contrast, criticality is a computational regime that optimizes information processing capacity, and is a homeostatically regulated set-point in isocortical circuits. Consistent with the effects of waking, criticality is degraded by experience-dependent plasticity. Whether criticality is the computational set-point of sleep is unknown. To address this question, we evaluated the effects of sleep and wake on emergent dynamics in ensembles of cortical neurons recorded continuously for 10-14 d in freely behaving rats. We show that normal waking experience progressively disrupts criticality, and that sleep functions to restore critical dynamics. Criticality is perturbed in a context-dependent manner depending on behavior and environmental variables, and waking experience is causal in driving these effects. The degree of deviation from criticality is robustly predictive of future sleep/wake behavior, more accurate than SWA, behavioral history, and other neural measures. Our results demonstrate that perturbation and recovery of criticality is a network homeostatic mechanism consistent with the core, restorative function of sleep.

neuroscience↗

Circuit-specific selective vulnerability in the DMN persists in the face of widespread amyloid burden.

The relationship between brainwide functional decline and accumulation of pathological protein aggregates in Alzheimers disease (AD) is complex and not well understood. A set of highly interconnected cortical regions known as the default mode network (DMN) exhibits selective vulnerability to both functional decline and amyloid beta (A{beta}) plaques in early AD. One possibility is that early A{beta} accumulation in the DMN drives vulnerability. However, it is unknown whether there is something intrinsic to neuronal projections within the DMN that biases these circuits towards dysfunction. Here we directly test this hypothesis using long-term recordings of the spiking activity of ensembles of single units in freely behaving mice characterized by global cortical and hippocampal A{beta} burden (APP/PS1). Specifically, we track the interactions of a population of neurons within a DMN region and two additional populations that comprise monosynaptic targets, one within and the other outside the DMN. In addition, we record single neurons in hippocampus and examine interactions between the in-DMN and out-DMN cortical circuits triggered on hippocampal sharp-wave ripples, stereotyped hippocampal events that contribute to memory consolidation in the cortex. We examine the statistics of local activity as well as inter-regional communication in a region, genotype, and brain-state dependent manner. Our data reveal dysfunction restricted to the in-DMN projecting circuit. In contrast, communication along neuronal projections that originate in the DMN but target an out-DMN population is equivalent in APP/PS1 and WT mice. Circuit dysfunction is most evident throughout sleep, and particularly disrupted within sharp-wave ripples. Summarily, our results indicate that, even in the face of transgene overexpression and widespread A{beta}, there is distinct intrinsic and selective vulnerability. This vulnerability to amyloidosis is circuit-specific and conditioned on target, and neither source nor amyloid burden. These data raise the possibility that neuronal function in the DMN is not universally vulnerable; DMN subnetworks whose interactions involve targets outside the DMN may be resilient to A{beta}.

neuroscience↗

Transcriptomic cell type structures in vivo neuronal activity across multiple time scales.

SUMMARYCell type is hypothesized to be a key determinant of the role of a neuron within a circuit. However, it is unknown whether a neurons transcriptomic type influences the timing of its activity in the intact brain. In other words, can transcriptomic cell type be extracted from the time series of a neurons activity? To address this question, we developed a new deep learning architecture that learns features of interevent intervals across multiple timescales (milliseconds to >30 min). We show that transcriptomic cell class information is robustly embedded in the timing of single neuron activity recorded in the intact brain of behaving animals (calcium imaging and extracellular electrophysiology), as well as in a bio-realistic model of visual cortex. In contrast, we were unable to reliably extract cell identity from summary measures of rate, variance, and interevent interval statistics. We applied our analyses to the question of whether transcriptomic subtypes of excitatory neurons represent functionally distinct classes. In the calcium imaging dataset, which contains a diverse set of excitatory Cre lines, we found that a subset of excitatory cell types are computationally distinguishable based upon their Cre lines, and that excitatory types can be classified with higher accuracy when considering their cortical layer and projection class. Here we address the fundamental question of whether a neuron, within a complex cortical network, embeds a fingerprint of its transcriptomic identity into its activity. Our results reveal robust computational fingerprints for transcriptomic types and classes across diverse contexts, defined over multiple timescales.

neuroscience↗

Internet of Things Architecture for High Throughput Biology

The Internet of Things (IoT) provides a simple framework to easily control online devices. IoT is now a commonplace tool used by technology companies, but it is rarely used in biology experiments. IoT can benefit research through alarm notifications, automation, and the real-time monitoring of experiments. We developed and implemented an IoT architecture to control biological devices used in experiments. We developed our own electrophysiology, microscopy, and microfluidic devices so that may be controlled through a unified IoT architecture. The system allows each device to be monitored and controlled through an online web tool. We present our IoT architecture so other labs may replicate it for their own experiments.

bioengineering↗

Drop, Swap, and Generate: A Self-Supervised Approach for Generating Neural Activity

Meaningful and simplified representations of neural activity can yield insights into how and what information is being processed within a neural circuit. However, without labels, finding representations that reveal the link between the brain and behavior can be challenging. Here, we introduce a novel unsupervised approach for learning disentangled representations of neural activity called Swap-VAE. Our approach combines a generative modeling framework with an instance-specific alignment loss that tries to maximize the representational similarity between transformed views of the input (brain state). These transformed (or augmented) views are created by dropping out neurons and jittering samples in time, which intuitively should lead the network to a representation that maintains both temporal consistency and invariance to the specific neurons used to represent the neural state. Through evaluations on both synthetic data and neural recordings from hundreds of neurons in different primate brains, we show that it is possible to build representations that disentangle neural datasets along relevant latent dimensions linked tso behavior.

neuroscience↗