1825 papers
This paper introduces a constrained Laplacian framework that utilizes Modified Nodal Analysis to translate node-level functional relationships into edge-level quantities, thereby identifying the specific anatomical pathways within the brain's structural network that best support observed functional coupling.
By extending the Kuramoto framework to include Hebbian and homeostatic plasticity, this study demonstrates that a robust excitation-inhibition balance ensures stable, desynchronized network dynamics, whereas weaker inhibition creates a bistable regime that selectively stabilizes strong connections while allowing weaker ones to reorganize, offering a potential mechanism for synaptic rewiring during sleep.
This study demonstrates that in the fly navigation center, disinhibition of a recurrent attractor circuit acts as a gating mechanism to rapidly switch persistent goal signals on and off, enabling the storage of heading information during upwind runs while allowing dynamic updates during turns.
This study identifies a specific population of local neurons in the Drosophila navigation center that integrates olfactory evidence and maintains working memory to sustain goal-directed heading during odor plume navigation, with their activity dynamics optimized for turbulent environments.
This study demonstrates that inhibiting lysine deacetylation with the HDAC inhibitor SAHA restores TDP-43 localization and function by modulating PPIA acetylation in ALS models, and that combining this approach with the heat shock protein co-inducer arimoclomol yields a synergistic, sustained therapeutic effect against neurodegeneration.
The paper introduces Long-Horizon Associative Learning (L-HAL), a unified, biologically grounded framework that uses a single neural mechanism with graded temporal overlap to explain diverse statistical learning phenomena across multiple temporal scales, from local transitions to complex network structures.
Contrary to the reconsolidation framework's prediction that reactivation destabilizes memories, this study demonstrates that reactivating a visuomotor memory actually protects it from interference by competing learning, thereby shaping which memory is expressed at retrieval.
This randomized crossover study demonstrates that Sensory Entrained iTBS (se-iTBS) significantly enhances and prolongs motor cortex plasticity compared to standard iTBS by leveraging sensorimotor oscillations to double motor-evoked potential responses in over 80% of participants.
A harmonized mega-analysis of fMRI data challenges the prevailing view of strict functional segregation within the extended amygdala, revealing that both the central nucleus and bed nucleus of the stria terminalis respond similarly to threat regardless of temporal certainty, while frontocortical regions specifically process uncertain threats.
This paper demonstrates that fine-tuning pre-trained chemical foundation models via a novel LoRA-based approach (LORAX) is necessary to generate superior, olfaction-specific odorant representations that outperform both classical hand-designed features and unadapted foundation model embeddings in predicting odorant-receptor binding.
This study demonstrates that using flexible temporal modeling to account for feature-specific hemodynamic delays, rather than relying on the canonical HRF, significantly improves the accuracy of mapping diverse naturalistic stimulus features to brain activity during fMRI.
By combining high-resolution videography with a dedicated computer-vision pipeline, this study reveals that cephalopod chromatophores are controlled by complex, overlapping motor units innervated by approximately four neurons per organ, enabling the formation of virtual chromatophores and diverse geometric patterns essential for dynamic camouflage.
This study reveals that odor-evoked neural sequences in the mammalian olfactory bulb propagate as wavefronts through a low-dimensional tuning space, where early concentration-invariant activity anchors odor identity while later sequential co-activation of similarly tuned neurons scaffolds unsupervised manifold learning in the piriform cortex to enable rapid generalization to novel odors.
This study demonstrates that deep learning models utilizing easily obtainable physiological signals, particularly electrodermal activity and heart rate, can objectively detect spontaneous decreases in fluctuating tonic pain with high accuracy and low latency, thereby enabling noninvasive, near real-time closed-loop interventions for chronic pain management.
Using a recurrent hyperthermia model in vivo, this study demonstrates that TDP-43 nuclear depletion and cytoplasmic aggregation leading to motor neuron loss occur independently of stress granules, challenging the prevailing hypothesis that stress granule persistence drives TDP-43 pathology in ALS and related diseases.
This study demonstrates that a hierarchical framework using shallow piecewise-linear recurrent neural networks can extract stable, subject-specific dynamical markers from resting-state fMRI data to reproduce functional connectivity patterns, while highlighting current limitations in generalizing to unseen individuals and predicting demographic or cognitive variables compared to direct empirical feature analysis.
This paper proposes a unified "Bayesian efficient coding" framework that generalizes the traditional efficient coding hypothesis by incorporating arbitrary loss functionals to define optimal neural codes, demonstrating through the introduction of "covtropy" and a reanalysis of blowfly data that minimizing reconstruction error can better explain sensory system design than maximizing mutual information.
This study establishes murine and zebrafish *Wac* deletion mutants as complementary vertebrate models that recapitulate key craniofacial, neurobehavioral, and molecular features of DeSanto-Shinawi Syndrome, thereby providing a foundation for understanding the disorder's underlying biological mechanisms.
This study demonstrates that human observers estimate the location of a collection of points by first identifying visual clusters within the sample, a strategy that allows them to approximate optimal statistical estimators across different probability distributions.
This study identifies distinct but interacting neurocomputational mechanisms in the human brain that support multisensory learning by dissociating structure-based statistical learning, reward-based reinforcement learning, and outcome-surprise processing into complementary, modality-general neural networks.