1313 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 describes the development and preclinical validation of a potent divalent siRNA drug candidate, 2439-s4, which significantly lowers human prion protein levels and extends survival in mouse models of prion disease, leading to FDA clearance for clinical trials.
This paper introduces the Sparse Cognitive Graph (SCG) framework, which reconciles gradual predictive learning with efficient goal-directed control by demonstrating how reward-dependent, nonlinear selection of transition statistics drives discrete graph reorganization to explain diverse behavioral regimes and neural activity patterns across humans and mice.
This study reveals that maternal age in *C. elegans* adaptively modulates offspring social behavior by altering an ERI-1/mir-8207/flp-26 small RNA-neuropeptide axis in AVH interneurons, which subsequently tunes ADL sensory circuit activity to adjust pheromone avoidance strategies.
This study demonstrates that while PARK2-knockout mice exhibit age-dependent behavioral and biochemical changes, they fail to fully recapitulate the specific pathological manifestations of human Parkinson's disease.
By analyzing standardized datasets from approximately 100 mice, this study reveals that individual differences in decision-making strategies, ranging from anticipatory to inattentive behaviors, are driven by distinct, low-dimensional cortical dynamics characterized by specific population activity timescales.
By combining fMRI studies in healthy adults with behavioral assessments of individuals with severe aphasia, this research demonstrates that logical reasoning operates independently of natural language, engaging distinct neural networks rather than linguistic representations.
This study demonstrates that sustained alpha oscillations in working memory specifically reflect the attentional prioritization of task-relevant information rather than serving as a general mechanism for maintaining all stored items, which may instead rely on activity-silent synaptic traces.
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.