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.
This study demonstrates that rhythmic sensory stimulation in the delta-band enhances temporal prediction performance by aligning external sensory input with intrinsic neural oscillations, thereby validating the role of delta-band dynamics in predictive behavior across sensory modalities.
This study demonstrates that mannose-capped gold nanoparticles protect against ischemic stroke damage by scavenging mannose-binding lectin (MBL) from the hypoxia-exposed glycocalyx, thereby inhibiting the complement lectin pathway and reducing neuronal loss and anxiety in humanized mice.
This study demonstrates that the choice of visual tagging frequency (8.6 Hz vs. 12 Hz) is not a neutral methodological parameter but significantly shapes neural attention indices (SSVEPs and ERPs) and influences their relationship with cognitive performance in working memory tasks.
This study demonstrates that in non-human primates, the preferential vulnerability of ventral-tier substantia nigra dopaminergic neurons to Lewy body pathology is driven by a synergistic combination of their specific anatomical location and high neuromelanin levels, which trigger alpha-synuclein aggregation beyond a critical threshold.
This study demonstrates that robust electrophysiological function in early-birth rat CA1 pyramidal neurons arises from a "degenerate" relationship where diverse combinations of ion channel parameters and varying dendritic morphologies converge to produce similar firing behaviors, highlighting the critical role of population-based modeling in capturing biological variability.
This study demonstrates that tau filaments from Alzheimer's disease and corticobasal degeneration brains can be transmitted to mice, where they seed the assembly of mouse tau filaments that retain the original structural conformations, thereby confirming the prion-like nature of tau strains and validating the mouse as a model for studying tauopathies.
This study analyzes data from nearly 39,000 individuals to demonstrate that general cognitive function is supported by a widespread, distributed white matter network whose structural integrity and relevance to cognition increase with age, particularly through long-range connections in frontal regions.
This study challenges the prevailing view that mice rely solely on reflexive eye movements by demonstrating that they actively coordinate saccadic eye movements with head motions during goal-directed orienting, revealing an evolutionarily conserved mechanism for voluntary gaze redirection similar to that found in foveate animals.
This study investigates the loudness reduction of narrowband sounds compared to pure tones across various frequencies and levels in listeners with normal to moderate hearing loss, using both categorical loudness scaling experiments and a neural ensemble averaging model to suggest that this phenomenon arises from early central auditory processing.
By analyzing Echo State Networks based on the Drosophila connectome, this study demonstrates that non-random wiring features, particularly excess neuronal self-recurrency, enable sparse biological networks to achieve robust and efficient computation that outperforms random sparse networks in stability and task engagement.