1296 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.
By analyzing simultaneous recordings of striatal projection neurons and dopamine neurons, this study demonstrates that the mesolimbic system employs error-driven representation learning to update state features, revealing a convergence of learning principles between biological and artificial systems.
This study demonstrates that brain-specific deletion of the RNA helicase MOV10 in mice leads to enhanced fear learning driven by increased GABRA2 expression and a shift toward non-canonical, corticalized fear circuits rather than traditional fear pathways, offering new molecular insights into the persistence of fear memories relevant to PTSD and substance dependence.
This study demonstrates that reduced VPS26B levels in the primary motor cortex of a Parkinsonian mouse model lead to decreased surface GluA1 and synaptic protein loss, resulting in motor deficits that can be partially rescued by VPS26B overexpression.
By combining eye tracking with feature-based encoding models, this study demonstrates that prior scene context facilitates rapid face detection by shifting the perceptual strategy from reliance on sensory-driven features to expectation-based spatial guidance, a change evident even in the first eye movement.
This study provides a computational account demonstrating that a positive bias in self-evaluation, where individuals overestimate their performance especially under high cognitive demands, helps attenuate the subjective cost of effort and sustain engagement in effortful tasks.
SUITPy is a comprehensive Python toolbox that enhances cerebellar imaging analysis across the human lifespan by utilizing a U-Net-based model for robust automatic isolation, improved normalization to a cerebellum-specific template, and integrated visualization and atlas resources.
This study demonstrates that individual differences in other-directed emotion regulation, particularly the tendency to worsen others' emotions, significantly influence both the behavioral rejection of unfair offers and the neural connectivity between the amygdala and prefrontal regions during social fairness decisions.
This study presents a computational model demonstrating that astrocyte-mediated PGE2 signaling, particularly via endfeet and PIP2-derived diacylglycerol, drives the late phase of neurovascular coupling and arteriole dilation in response to neuronal activity.
This study establishes a versatile platform using adeno-associated virus (AAV) transduction to enable longitudinal, 3D single-cell morphometric analysis and dynamic connectivity tracking of neurons and astrocytes within living human midbrain organoids, overcoming the limitations of static imaging in dense neural networks.
By manipulating linguistic features in predictability-matched sentences, this study demonstrates that hierarchical linguistic composition, particularly syntactic structure and associative semantics, significantly drives the alignment between LLM representations and human brain activity, while compositional semantics appears to be more uniquely encoded in the human brain.