963 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 utilizes high-resolution EEG markers to reveal that sleep-onset insomnia is characterized by persistent, continuous hypervigilance that is paradoxically more pronounced in cases without psychiatric comorbidity than in those with depressive or anxiety symptoms, suggesting distinct underlying pathophysiological mechanisms.
This study demonstrates that in the macaque prefrontal cortex, noise correlations between neurons can exhibit condition-dependent selectivity during a spatial delayed response task, even when the individual neurons' spike counts lack such selectivity, indicating that correlated variability serves as an independent source of information.
This study reveals that in the mouse escape circuit, the functional weights of diverse inputs onto dorsal periaqueductal grey neurons are determined not by anatomical location but by the temporal statistics of presynaptic activity, enabling rapid, context-dependent reweighting of signals to support flexible survival decisions.
Using intracranial EEG during a retinotopic mapping task, this study provides electrophysiological evidence that the human hippocampus exhibits visuospatial coding properties, specifically stimulus-size sensitivity and contralateral field biases mediated by slow theta oscillations, thereby supporting its role as a high-level component of the visual hierarchy.
This study demonstrates that human hippocampal and amygdala activity during scene encoding is temporally structured by eye fixations, with enhanced theta phase locking and evoked potentials specifically for initial fixations on people, thereby linking discrete visual sampling events to the formation of semantic and temporal memory.
This study reveals that an ancient midbrain pathway connecting the superior colliculus to the lateral visual cortex, rather than the primary visual cortex, relays visual information to the hippocampus to shape distinct spatial maps in mice, offering a potential explanation for residual visual navigation in cortically blind humans.
NUGRAPH is a self-supervised, graph-based framework that corrects large-scale 3D nucleus segmentation errors by performing global relational reasoning over decomposed 3D primitives and refining shapes via signed distance fields, achieving state-of-the-art performance on the newly curated NucEMFix brain-wide benchmark.
By analyzing longitudinal data from over 10,000 adolescents, this study identifies age 14 as a critical inflection point where brain-behavior relationships dynamically reorganize from predicting externalising symptoms via diffuse cortical and basal ganglia mechanisms to predicting internalising symptoms through late-maturing prefrontal networks and thalamic structures, challenging static biomarker models in favor of developmentally contingent precision mental health strategies.
This study demonstrates that nonhuman primate hippocampal synapses exhibit a lower threshold for associative plasticity and enhanced protein synthesis-dependent stabilization compared to rodents, highlighting critical species-specific differences that limit the translational value of rodent models for human memory processes.
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.