Keith B. Hengen

The paper develops a method to measure proximity to criticality in complex systems using a temporal renormalization group and information theory, applying it to brain activity data to show shifts in criticality during wakefulness and sleep.

This study shows mice adjust their hunting strategies based on environmental cues by manipulating temperature in predator-prey interactions.

This study shows that mice improve prey capture by reorganizing search strategies in complex environments, using memory and learned object values.

The paper shows that proximity to criticality in neural systems predicts learning rates and cognitive abilities across species.

The paper defines criticality in low-dimensional dynamical systems and shows it enhances information processing in neural models.

This study shows that structured firing sequences emerge in human and murine brain organoids and neonatal brain slices, suggesting innate rather than experience-dependent neural coding.

The paper reviews evidence for criticality as a computational set-point in brain function and finds a methodological source of controversy.

The paper shows that vertebrate diversity shifts with temperature, favoring endotherms in cooler areas and ectotherms in warmer regions, predicting a more reptilian future as global temperatures rise.

The paper identifies a high-frequency, millisecond-scale brain state embedding that persists across sleep and wake states and correlates with behavior.

This study shows neurons encode their anatomical location in spike trains, which can be decoded across brain regions and conditions.

The paper shows that after active whisking starts in mice, layer IV neurons in the barrel cortex temporarily become more excitable, which depends on prior experience.

This study shows that monocular deprivation during the visual critical period reduces visual and homotopic connectivity while increasing internetwork connectivity between visual and parietal cortex in mice.

This study shows that sleep restores criticality in cortical networks, a computational regime that optimizes information processing, more accurately than traditional measures like SWA.

This study shows that tauopathy disrupts network-level homeostatic set-points in hippocampal activity but not individual neuron activity, with implications for computational capacity and behavior.

The paper investigates whether transcriptomic cell type can be inferred from neuronal activity timing across multiple time scales using deep learning.

The paper investigates circuit-specific vulnerability in the default mode network (DMN) in Alzheimer's disease, finding that dysfunction is restricted to in-DMN projecting circuits despite widespread amyloid burden.

This paper develops a MYT1L syndrome mouse model that recapitulates human phenotypes, revealing disrupted brain development and neuronal maturation as underlying mechanisms.

This paper introduces Swap-VAE, an unsupervised method for learning disentangled neural activity representations using self-supervised learning on neural recordings.

This study identifies how homeostatic mechanisms regulate higher-order network properties in rodents during visual deprivation, revealing distinct roles for synaptic scaling and intrinsic plasticity.

This study maps human microglia development and identifies cytokine-associated subtypes and functional roles in neural network maturation.

This study shows that V1 neurons maintain stable firing rates during expected light-dark transitions but respond to unexpected changes, with correlations during natural vision being more informative than individual rates.

The paper shows cortical networks maintain critical dynamics independently of excitatory firing rate homeostasis, with inhibitory neurons playing a key role in stabilizing this state.

This study shows that Shank3 loss disrupts homeostatic plasticity and circuit stability, which can be rescued by lithium, suggesting a therapeutic target for Shankopathies.