Hengen Lab Logo
Papers etc

Publications

Neural criticality, homeostatic plasticity, neurodegeneration, and computational neuroscience.

Peer-Reviewed Articles
Hengen & Shew Neuron Cover - Click to view full size
Is criticality a unified set-point of brain function?
Hengen KB, Shew WL (2025).
Description: The most fundamental question in neuroscience is "how does the brain work?" Despite decades of research on individual neurons, behaviors, and molecular mechanisms, we lack what physicists call a unifying theory. Here, with Woody Shew, we present evidence that criticality is precisely that unifying principle. We revisit data from 140 papers and resolve a long-standing controversy. Finally, we present eight predictions that emerge when criticality is understood as an optimization principle that maximizes the brain's computational power. These predictions directly inform a new understanding of brain disease, development, homeostasis, and learning.
Neuron
IF: 18.7
PDF DOI
Tolossa & Schneider eLife 2025
Neurons throughout the brain embed robust signatures of their anatomical location into spike trains
Tolossa GB, Schneider AM, Dyer EL, Hengen KB (2025).
Description: Gemechu and Aidan asked a fundamental question: is information about a neuron's anatomical location embedded in its spike train? The null must be no: there is no prior evidence that we can look at a timeseries (a list of times when a neuron fires) and say anything about that neuron's identity. Further, we've long modeled neurons as rate varying Poisson (random) processes whose outputs (spike trains) transmit information about their inputs (stimuli). Against this backdrop, G & A worked with Allen Institute datasets to train machine learning models on single neuron time series. Lo and behold, they were able to identify the anatomical home of individual cells based on spike timing. Further, these models (trained on Allen Institute recordings) tested above chance on data from an independent group. Combined with the LOLCAT paper (see Scheider, Azabou 2023, below), this suggests that, in addition to stimulus information, spike trains carry robust latent information about identity.
eLife
IF: 7.25
PDF DOI
Molly main finding
Experience-Dependent Intrinsic Plasticity in Layer IV of Barrel Cortex at Whisking Onset
Shallow MC, Tian L, Higashikubo BT, Lin H, Lefton KB, Chen S, Dougherty JD, Culver JP, Lambo ME, Hengen KB.
Description: At two weeks old, mice abruptly begin to actively use their whiskers to explore the world. Molly and Lucy discovered that this behavioral switch comes alongside a transient increase in the excitability of excitatory neurons in layer 4 barrel cortex. These are the neurons that process whisker input --- this brief increase in excitability is poised to drive plasticity and requires prior experience.
eNeuro
IF: 2.7
PDF DOI
organoids are weird.
Protosequences in human cortical organoids model intrinsic states in the developing cortex
van der Molen T, Spaeth A, Chini M, Bartram J, Dendukuri A, Zhang Z, Bhaskaran-Nair K, Blauvelt LJ, Petzold LR, Hansma PK, Teodorescu M, Hierlemann A, Hengen KB, Hanganu-Opatz IL, Kosik KS, Sharf T.
Description: Study of early neural network development using human cortical organoids, revealing fundamental patterns of spontaneous activity that model intrinsic brain states.
Nature Neuroscience
IF: 27.7
PDF DOI
Argyle Neurons
What if what matters is emergent?
Chopra R, Hengen KB (2025).
Description: Invited review of Ruggiero... Slutsky (2024, Neuron). Ravi makes a strong argument that homeostasis is an emergent process that is concerned with emergent end-points. Since reliability of behavior and cognition are of utmost importance, it must be the case that there are cellular mechanisms that result in its (the reliability's) maintenance.
Neuron (Invited Review)
IF: 18.7
PDF DOI
James' Neuron Cover - Click to view full size
Failure in a population: tauopathy disrupts homeostatic set-points in emergent dynamics despite stability in the constituent neurons
McGregor JN, Farris CA, Ensley S, Schneider A, Wang C, Liu Y, Tu J, Elmore H, Ronayne KD, Wessel R, Dyer EL, Bhaskaran-Nair K, Holtzman DM, Hengen KB (2024).
Description: What is a brain disease? While it may be caused by a protein, it's reasonable to suggest that the disease is only problematic because of failures in computation. Otherwise, the loss of neurons (e.g.), would be irrelevant. Thus, in this work, we reason that disease can only occur when homeostatic mechanisms either fail or are overwhelmed. Prior to that point, perturbations are trivially accounted for by compensatory plasticity --- axiomatically, that's the job of a homeostatic mechanism. James set out to record for the entire lifetime of animals destined to die of neurodegeneration, and track every known homeostatic set-point in neuronal activity. At some point along the way, at least one of these features must be derailed. To our great surprise, James found that, even at the end of life, individual neurons maintain set points in firing rates and other basic properties. In sharp contrast, network criticality was severely disrupted. This movement away from criticality began early in life, and progressively worsened until death. This suggests that criticality might be the dynamical locus of neurodegenerative diseases --- in other words, impaired brain function is not a result of killing neurons outright, but a disruption of the delicate network interactions that account for optimal information processing.
Neuron
IF: 18.7
PDF DOI
Parks & Schneider Wave-Particle Brain State Visualization
A non-oscillatory, millisecond-scale embedding of brain state provides insight into behavior
Parks DF*, Schneider AM*, Xu Y, Brunwasser SJ, Funderburk S, Thurber D, Blanche T, Dyer EL, Haussler D, Hengen KB. *equal contribution
Description: David and Aidan developed a revolutionary approach to understanding brain states that doesn't rely on traditional oscillations or rhythms. Instead, they rigorously and exhautively demonstrated that the brain's sleep/wake state is most accurately captured by millisecond-scale patterns in neural activity. Mathematically, this precludes the canonical waves that have been used to describe/identify sleep and wake for a century. As a result, it raises the possibility of local control of states. Aidan and David elegantly showed that local states of all flavors (sleep in a waking brain, wake in sleeping brain, etc.) occur regularly throughout the brain as brief "flickers". Flickers predict macroscopic features of animal behavior. A plausible interpretation is that the minimal unit of a state (e.g., sleep) is not a slow, global wave, but a millisecond-scale structuring of neural activity that plays out in micrometers of the brain. A global state, as we experience them, then requires a massive coordinating signal. Perhaps this is the role of the slower processes?
Nature Neuroscience
IF: 27.7
PDF DOI
pirate mouse
Visual deprivation during mouse critical period reorganizes network-level functional connectivity
Chen S, Rahn R, Bice A, Gaines S, Hengen KB, Dougherty J, Culver J.
Description: Investigation of how sensory experience shapes neural connectivity during critical developmental periods using advanced imaging techniques.
Journal of Neuroscience
IF: 5.3
PDF DOI
Rip Van Winkle
Sleep restores an optimal computational regime in cortical networks
Xu Y, Schneider A, Wessel R, Hengen KB (2024).
Description: Why all animals sleep is one of the great questions in biology. Because sleep improves everything a brain does (and conversely, sleep deprivation undermines everything from reasoning to breathing), it is reasonable to hypothesize that the core, restorative function of sleep is to resurrect a universally necessary feature of computation. In this paper, Yifan demonstrates that sleep restores criticality, an optimal computational regime, in cortical networks. More than any other tested measure, distance from criticality predicts future sleep/wake. This provides a direct explanation of how sleep supports brain function.
Nature Neuroscience
IF: 27.7
PDF DOI
LOLCAT
Transcriptomic cell type structures in vivo neuronal activity across multiple time scales
Schneider A, Azabou M, McDougall-Vigier L, Parks DF, Ensley S, Bhaskaran-Nair K, Nowakowski TJ, Dyer EL*, Hengen KB* (2023). *corresponding author
Description: Aidan and Mehdi used Allen Institute data (dense microelectrode and calcium imaging) to as if information about a neuron's transcriptomic cell is embedded in its spike timeseries. While there's some prior indication that certain cell types tend to fire at higher rates than others, this isn't sufficient to identify a single cell --- it's a population statistic. Put simply, if you were given a raster plot of one neuron's activity (recorded in an awake, behaving animal) with no other information, could you reliably identify the cell type? With this as the challenge, A & M show that cell type can be robustly recovered from the activity timeseries. Taken with Aidan and Gemechu's 2025 eLife paper (anatomy is embedded in the spike timeseries), it seems clear that neuronal spiking carries much more information than stimuli alone. Such models are fundamentally data-limited; future work will increase the accuracy and universalizability of these insights.
Cell Reports
IF: 10.0
PDF DOI
Image placeholder
Internet of Things Architecture for Cellular Biology
Parks DF, Voitiuk K, Geng J, Elliott MAT, Keefe MG, Jung EA, Robbins A, Baudin PV, Ly VT, Hawthorne N, Yong D, Sanso SE, Rezaee N, Sevetson J, Seiler ST, Currie R, Pollen AA, Hengen KB, Nowakowski TJ, Mostajo-Radji MA, Salama SR, Teodorescu M, Haussler D (2022).
Description: Development of Internet of Things infrastructure for high-throughput cellular biology research, enabling large-scale automated experiments.
ScienceDirect|Internet of Things
IF: 5.9
PDF DOI
Image placeholder
Drop, Swap, and Generate: A Self-Supervised Approach for Generating Neural Activity
Liu R, Azabou M, Dabagia M, Lin CH, Azar MG, Hengen KB, Valko M, Dyer EL (2021).
Description: Novel machine learning approach for generating synthetic neural activity data using self-supervised learning methods.
Advances in Neural Information Processing Systems
IF: 9.71
PDF DOI
Image placeholder
A MYT1L syndrome mouse model recapitulates patient phenotypes and reveals altered brain development due to disrupted neuronal maturation
Chen J, Lambo ME, Ge X, Dearborn JT, Liu Y, McCullough KB, Swift RG, Tabachnick DR, Tian L, Noguchi K, Garbow JR, Constantino JN, Gabel HW, Hengen KB, Maloney SE, Dougherty JD (2021).
Description: Development and characterization of a mouse model of MYT1L syndrome, revealing mechanisms of altered neuronal maturation in neurodevelopmental disorders.
Neuron
IF: 18.7
PDF DOI
Image placeholder
Human microglia states are conserved across experimental models and regulate neural stem cell responses in chimeric organoids
Popova G, Soliman SS, Kim CN, Keefe MG, Hennick KM, Jain S, Li T, Tejera D, Shin D, Chhun BB, McGinnis CS, Speir M, Gartner ZJ, Mehta SB, Haeussler M, Hengen KB, Ransohoff RR, Piao X, Nowakowski TJ (2021).
Description: Investigation of human microglia in brain organoid models, revealing conserved states and regulatory functions in neural development.
Cell Stem Cell
IF: 24.9
PDF DOI
Carbon SEM
Construction and implementation of carbon fiber microelectrodes for acute and chronic in vivo recordings
Reikersdorfer KN*, Stacy AK*, Bressler DA, Hayashi LS, Hengen KB, Van Hooser SD (2021). *equal contribution
Description: Methodological paper detailing construction and use of carbon fiber microelectrodes for neural recording applications.
JOVE
IF: 1.2
PDF DOI
Image placeholder
Homeostatic Mechanisms Regulate Distinct Aspects of Cortical Circuit Dynamics
Wu YK, Hengen KB, Turrigiano GG, Gjorgjieva J (2020).
Description: Theoretical and experimental investigation of how homeostatic mechanisms regulate different aspects of cortical circuit function.
Proceedings of the National Academy of Sciences
IF: 12.8
PDF DOI
Image placeholder
Autism-Associated Shank3 Is Essential for Homeostatic Compensation in Rodent V1
Tatavarty V, Torrado Pacheco A, Groves Kuhnle C, Lin H, Koundinya P, Miska NJ, Hengen KB, Wagner FF, Van Hooser SD, Turrigiano GG (2020).
Description: Investigation of how Shank3, an autism-associated protein, regulates homeostatic plasticity in visual cortex.
Neuron
IF: 18.7
PDF DOI
Image placeholder
Currently unstable: daily ups and downs in E-I balance
Brunwasser SJ, Hengen KB (2020).
Description: Invited review discussing daily fluctuations in excitatory-inhibitory balance and their functional significance.
Neuron (Invited Review)
IF: 18.7
PDF DOI
Ma & Hengen 2019 Neuron Cover - Click to view full size
Cortical Circuit Dynamics Are Homeostatically Tuned to Criticality In Vivo
Ma Z, Turrigiano GG, Wessel R, Hengen KB* (2019). *corresponding author
Description: The first major paper out of the Hengen Lab, this is a seminal work by Zhengyu Ma. Zhengyu demonstrated that cortical circuits are homeostatically maintained near criticality in freely behaving animals. Essentially, she tested the core prediction that criticality is the setting on the thermostat --- this is what all the set-points throughout the brain are ultimately trying to acheive. Using chronic, continuous recordings in rats, her study revealed that the brain actively tunes itself to a state that maximizes computational capacity, information transmission, and dynamic range. When Zhengyu pushed the brain away from criticality, it pushed back. Her work bridged theoretical physics and neuroscience by showing that the concept of criticality is not just a mathematical abstraction but a fundamental organizing principle that the brain actively maintains through homeostatic processes.
Neuron
IF: 18.7
PDF DOI
Image placeholder
Rapid and Active Stabilization of Visual Cortical Firing Rates Across Light-Dark Transitions
Torrado Pacheco A, Tilden EI, Gruztner SM, Lane B, Wu Y, Hengen KB, Gjorgjieva J, Turrigiano GG (2019).
Description: Investigation of how visual cortex rapidly adapts firing rates during light-dark transitions through homeostatic mechanisms.
Proceedings of the National Academy of Sciences
IF: 12.8
PDF DOI
tracking
Neuronal firing rate homeostasis is inhibited by sleep and promoted by wake
Hengen KB, Torrado Pacheco A, McGregor JN, Van Hooser SD, Turrigiano GG (2016).
Description: For brain activity to be stable/reliable (on a long timescale), there is likely to be homeostatic control over neuronal firing rates. This is fundamental, but quite hard to test. We developed one of the first methods for following ensembles of single neurons continuously for 9 days. In response to sustained perturbation, neuronal activity slowly recovered --- but the homeostatic recovery only occurred when animals were awake. This paper established three important contributions: 1) Individual neurons have a thermostat-like set-point for their own firing rate that is independent of stimuli, 2) homeostatic plasticity interacts with sleep/wake states, and 3) methods of chronic, continuous recording from single neurons in freely behaving animals.
Cell
IF: 57.5
PDF DOI
Image placeholder
Daily isoflurane exposure increases barbiturate insensitivity in medullary respiratory and cortical neurons via expression of ε-subunit containing GABAARs
Hengen KB, Nelson NR, Stang KM, Johnson SM, Smith SM, Watters JJ, Mitchell GS, Behan M (2015).
Description: Investigation of anesthetic tolerance mechanisms involving GABA receptor subunit changes in respiratory control circuits.
PLoS ONE
IF: 3.8
PDF DOI
Hengen 2013 Neuron Cover - Click to view full size
Firing rate homeostasis in visual cortex of freely behaving rodents
Hengen KB, Lambo ME, Van Hooser SD, Katz DB, Turrigiano GG (2013).
Description: This was a groundbreaking study --- the aim was to test core principles of homeostatic control where it counts: in the brain outside of experimental constraint. In the end, this manuscript was the first to demonstrate firing rate homeostasis in freely behaving animals, establishing the physiological relevance of homeostatic plasticity mechanisms that had previously only been observed in cultured neurons or anesthetized preparations. By chronically recording from ensembles of single neurons in visual cortex in freely behaving rats, we showed that the brain actively maintains stable activity levels even in the face of sustained challenge (e.g., chronic visual blockade). In retrospect, this paper laid a bit of foundation for the field of in vivo homeostatic plasticity.
Neuron
IF: 18.7
PDF DOI
Image placeholder
Increased GABAA Receptor ε-Subunit Expression on Ventral Respiratory Column Neurons Protects Breathing during Pregnancy
Hengen KB, Nelson NR, Stang KM, Johnson SM, Crader SM, Watters JJ, Behan M (2012).
Description: Investigation of respiratory adaptations during pregnancy involving changes in GABA receptor expression. Selected by Faculty of 1000 as top 2% of published articles.
PLoS ONE
IF: 3.8
PDF DOI
Image placeholder
Changes in ventral respiratory column GABAA ε and δ subunits during hibernation mediate resistance to depression by ETOH and pentobarbital
Hengen KB, Gomez TM, Stang KM, Johnson SM, Behan M (2011).
Description: Study of GABA receptor plasticity during hibernation and its effects on drug sensitivity in respiratory control circuits.
American Journal of Physiology - Regulatory, Integrative and Comparative Physiology
IF: 3.2
PDF DOI
Image placeholder
An examination of orthographic and phonological processing using the task-choice procedure
Kahan TA, Hengen KB, Mathis KM (2010).
Description: Cognitive psychology study examining reading processes using experimental paradigms from decision-making research.
Language and Cognitive Processes
IF: 2.4
PDF DOI
Image placeholder
Hibernation induces pentobarbital insensitivity in medulla but not cortex
Hengen KB, Behan M, Carey HV, Jones MV, Johnson SM (2009).
Description: Investigation of regional differences in drug sensitivity that develop during hibernation, focusing on brainstem versus cortical responses.
American Journal of Physiology - Regulatory, Integrative and Comparative Physiology
IF: 3.2
PDF DOI
Conference Proceedings
Image placeholder
Structural localization is embedded in the spike trains of neurons
Bekele Tolossa, G., Schneider, A., Hengen, K. (2024).
Description: Conference presentation exploring how neuronal location information is encoded in spike train patterns at COSYNE 2024.
Computational and Systems Neuroscience (COSYNE)
Abstract
Image placeholder
Detecting change points in neural population activity with contrastive metric learning
C. Urzay, N. Ahad, M. Azabou, A. Schneider, G. Atamkuri, K.B. Hengen, E.L. Dyer (2023).
Description: Machine learning approach for detecting changes in neural population dynamics using contrastive learning methods.
IEEE Conference on Neural Engineering (NER)
PDF DOI
Image placeholder
Drop, Swap, and Generate: A Self-Supervised Approach for Generating Neural Activity
R. Liu, M. Azabou, M. Dabagia, C-H. Lin, M. Gheshlaghi Azar, K.B. Hengen, M. Valko, E.L. Dyer.
Description: Oral presentation at NeurIPS (top 1% of submitted papers) on self-supervised neural activity generation.
Neural Information Processing Systems (NeurIPS) - Oral
PDF Link
Other Refereed Material
Image placeholder
Mine Your Own vieW: Self-supervised learning through across-sample prediction
M. Azabou, M. Gheshlaghi Azar, R. Liu, C-H. Lin, E.C. Johnson, K. Bhaskharan-Nair, M. Dabagia, K.B. Hengen, W. Gray-Roncal, M. Valko, E.L. Dyer.
Description: Workshop oral presentation on self-supervised learning methods for neural data analysis.
NeurIPS Workshop on Self-supervised Learning - Oral
PDF
Image placeholder
Using self-supervision and augmentations to build insights into neural coding
M. Azabou, M. Dabagia, R. Liu, C-H Lin, K.B. Hengen, E.L. Dyer.
Description: Workshop paper exploring self-supervised learning applications to understanding neural coding principles.
NeurIPS Workshop on Self-supervised Learning
PDF
Preprints
Image placeholder
Defining and measuring proximity to criticality
Sooter JS, Fontenele AJ, Barreiro AK, Ly C, Hengen KB, Shew WL (2025).
Description: How can we precisely define and measure how close neural systems are to the critical point? Sam Sooter (next gen genius...) developed a rigorous mathematical framework based on renormalization group theory (1982 Nobel Prize to Kenneth Wilson) to precise measure distance to temporal criticality. At criticality, a signal will carry information at all time scales --- the millisecond and the hour both have meaning. As you move away from criticality, this range decreases. To directly measure that range, Sam created a computational tool, d2, providing what will be a field standard method. Sam's work is essential for understanding criticality in neural systems. One of the big deliverables is that distance to criticality can be measured in seconds, rather than hours.
bioRxiv Preprint
PDF DOI
Image placeholder
Temperature-dependent predation predicts a more reptilian future
Grady JM, Amme JL, Bhaskaran-Nair K, Sinha V, Brunwasser SJ, Record S, Dell AI, Hengen KB.
Description: Interdisciplinary work examining how temperature affects predation dynamics and its implications for future ecosystem structure and climate change impacts.
Preprint
PDF Preprint
Julie's Circuit
Circuit-specific selective vulnerability in the DMN persists in the face of widespread amyloid burden
Brunwasser SJ*, McGregor JN*, Farris C, Elmore H, Dyer EL, Bhaskaran-Nair K, Whitesell JD, Harris JA, Hengen KB. *equal contribution
Description: Investigation of selective vulnerability patterns in the default mode network in the context of Alzheimer's disease pathology, revealing how certain brain circuits remain vulnerable despite widespread amyloid accumulation.
Preprint
PDF DOI
Image placeholder
Learning Sinkhorn divergences for supervised change point detection
N. Ahad, E.L. Dyer, K.B. Hengen, Y. Xie, M.A. Davenport.
Description: Machine learning methodology for detecting changes in time series data using optimal transport theory and Sinkhorn divergences.
arXiv Preprint
PDF arXiv
0
Skip to Content
hengenlab
Resources
hengenlab
Resources
Resources