1813 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 proposes and validates the Rhythmic-Item-Template hypothesis, demonstrating that multiple visual working memory items guide attention in alternating theta-rhythmic cycles through coordinated frontal theta and occipital alpha oscillations, thereby reconciling the debate between single- and multiple-item template theories.
This study reveals that while visual stimuli evoke dopamine release in the mouse dorsomedial striatum via a cholinergic-dependent mechanism, this signaling is driven exclusively by frontal cortical inputs rather than direct sensory cortical projections.
This study presents a novel biophysical model demonstrating that nonlinear junctional conductances in coupled astrocytes, particularly through N-shaped membrane voltage dependencies, significantly enhance membrane voltage bistability and front propagation, challenging the traditional view of glial cells as purely linear responders.
By combining behavioral modeling with neural recordings in humans, this study demonstrates that coordinated interactions between orbitofrontal value encoding and lateral prefrontal attention filtering dynamically sustain task-state representations to facilitate learning in complex environments.
This study demonstrates that blocking the opioid system with naltrexone specifically reduces exploratory behavior in response to novel, rewarding music during states of heightened curiosity, highlighting the critical role of the opioidergic system in regulating curiosity-driven exploration.
The Brain Encyclopedia Atlas Project (BEAP) is an expert-curated, publicly accessible neuroinformatics resource that synthesizes data from over 1,400 studies to create a unified, spatially grounded 3D atlas of human functional neuroanatomy, enabling systematic comparison of brain regions and facilitating research and education through an interactive, community-updatable platform.
This paper presents a biologically constrained computational model of zebra finch song learning demonstrating that a dual-pathway architecture, combining a volatile basal ganglia reinforcement learning loop with a consolidating cortical motor pathway, enables robust sensorimotor learning by effectively navigating non-convex performance landscapes and escaping local optima.
This study demonstrates that individuals with higher baseline vestibulomotor weighting are more susceptible to cybersickness in virtual reality, and while their vestibular contributions decrease during exposure as an adaptive response, this reweighting is insufficient to prevent postural instability and symptom progression.
This study demonstrates that covert attention can be directed toward afterimages, as evidenced by pupil size tracking the perceived brightness of attended negative afterimages even in the absence of physical stimuli, thereby suggesting that the distinction between external and internal attention is a continuum rather than a strict dichotomy.
This paper demonstrates that evolutionary optimization acts as a crucial inductive bias that pre-conditions artificial neural networks, enabling them to exhibit unique latent learning dynamics and achieve rapid fine-tuning to optimal performance with significantly less data compared to randomly initialized networks.