1306 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 combines data analysis and computational modeling to demonstrate that phasic dopamine not only drives learning through reward prediction errors but also directly modulates conditioned responding, challenging the traditional view that its effects are solely mediated by value learning.
The paper introduces DELSSOME, a deep learning framework that bypasses computationally expensive numerical integration to accelerate the optimization of biophysical brain circuit models by 50–8000 times, enabling the first population-scale analysis of individual cortical E/I ratio trajectories across the lifespan.
This study establishes a highly reliable and versatile AAV-TH-SNCA mouse model of Parkinson's disease by systematically optimizing viral serotypes, promoters, and titers to induce early, progressive alpha-synuclein pathology and motor deficits prior to neuronal loss, thereby providing a powerful platform for investigating early disease mechanisms and evaluating therapeutic interventions.
This paper proposes a method to derive computationally efficient point-neuron models from morphologically detailed neurons by matching their transfer functions under *in vivo* conditions, thereby enabling the functional characterization of diverse neuronal morphologies.
This study demonstrates that fear learning induces specific reorganizations in spontaneous brain-state dynamics, which serve as interpretable biomarkers capable of predicting individual differences in extinction memory expression following transcranial magnetic stimulation (TMS) during extinction learning.
Practice-dependent refinement of motor execution yields durable and broadly transferable improvements in speed and efficiency, yet these gains are constrained by inherent movement direction biases that impair performance when the direction of movement is reversed.
This study identifies Rabphilin (Rph) in *Drosophila* DN1p clock neurons as a critical regulator that modulates synaptic plasticity thresholds in a time-of-day-dependent manner, thereby establishing a mechanistic link between microscale circadian neural dynamics and the hierarchical metaplastic regulation of sleep.
This paper addresses the challenge of measuring true correlations between fMRI activation patterns by deriving a maximum-likelihood estimate that corrects for measurement noise bias and demonstrating that a subject-wise bootstrap approach provides the most reliable method for testing hypotheses about representational geometry.
The paper introduces ViSoND, an open-source tool that synchronizes video with sonified neural and behavioral data to enhance qualitative observation, improve data interpretation, and broaden the accessibility of neuroscientific findings.
Using preregistered movie-fMRI encoding models, this study reveals that autistic children and adolescents exhibit reduced high-level visual feature representation and a relative shift toward low-level processing in social brain regions like the pSTS, supporting weak central coherence theories over early sensory enhancement hypotheses.