1829 papers
Neuroscience explores the intricate machinery of the brain and nervous system, seeking to understand how we think, feel, and move. From the microscopic dance of individual neurons to the complex networks that shape our memories and behaviors, this field peels back the layers of our biological selves to reveal the origins of consciousness and disease.
At Gist.Science, we bring these discoveries directly from bioRxiv, the leading preprint server for biological sciences, to a broader audience. We process every new neuroscience preprint as it is uploaded, transforming dense academic manuscripts into clear, plain-language explanations alongside detailed technical summaries. This ensures that both curious readers and specialists can stay current with the latest breakthroughs before they are formally published.
Below are the latest neuroscience papers we have processed from bioRxiv, offering fresh insights into the workings of the mind.
This study demonstrates that an Adaptive Gain model, which normalizes sound intensity based on recent auditory history and was originally derived from mouse thalamic data, significantly outperforms standard linear envelope models in predicting human cortical EEG responses to continuous speech across diverse experiments and languages.
This paper presents a normative computational framework that accurately infers environmental volatility and outcome stochasticity from binary feedback using a hidden Markov model with particle filtering, and validates through human experiments that people adaptively adjust their learning rates in accordance with these distinct noise sources.
This study demonstrates that humans can innately perceive limb damping from visual motion cues and that targeted coaching—specifically directing attention to elbow-angle velocity—significantly enhances this perceptual accuracy, revealing the malleability of action-perception coupling in estimating mechanical impedance.
This study demonstrates that a symmetric systemic challenge like water deprivation induces a directional right-biased hindlimb postural asymmetry in rats through a vasopressin-dependent mechanism involving both pituitary and spinal signaling pathways.
This study presents a stable, inducible human iPSC line engineered to efficiently generate functional human astrocytes that support the growth and synchronous activity of mature neuronal networks, thereby offering a standardized, translationally relevant alternative to mouse astrocytes or complex differentiation protocols.
This study demonstrates that suboptimal locomotor performance in novel environments stems from a fall-risk-aware adaptation strategy where individuals prioritize safety over efficiency by adjusting internal learning parameters, a mechanism quantified through a new inverse adaptation modeling framework.
Using a novel robotic maze task, this study demonstrates that humans adaptively integrate model-based and model-free reinforcement learning strategies to navigate reachable space, shifting toward more efficient model-free control as familiarity increases and revealing that spatial learning architectures are shared across scales but calibrated to the specific constraints of the effector system.
This study demonstrates that psilocybin's rapid and sustained antidepressant effects are mediated by the modulation of affective biases through the 5HT1A/5HT2A receptor-dependent suppression of excitatory inputs to prelimbic cortico-amygdala projection neurons, which subsequently shifts to enhanced inhibitory input and mimics the drug's behavioral effects when these neurons are chemogenetically inhibited.
Through multi-modality and multi-cohort replications, this study demonstrates that individuals with Corpus Callosum Dysgenesis exhibit normal perceptual accuracy but significantly impaired metacognitive efficiency due to a failure to align confidence judgments with task difficulty, highlighting the corpus callosum's crucial role in supporting metacognition.
This study reveals that cerebellar granule cells resolve the generalization-separation tradeoff by applying affine transformations that rotate low-dimensional cortical manifolds to distinguish contexts while preserving their intrinsic geometric structure, thereby enabling both smooth generalization and context-specific output.