1290 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.
Using a computational model of the pyramidal-interneuron circuit, this study demonstrates that persistent gamma oscillations in the prefrontal cortex are intrinsically more vulnerable to schizophrenia-related synaptic alterations than stimulus-locked oscillations in the visual cortex due to a narrower margin of oscillatory stability.
This paper proposes a minimal mean-field neural circuit model featuring nonlinear gating and recurrent excitation that unifies the mechanisms underlying flexible perceptual, memory-based, and abstract decision-making by replicating parietal cortical dynamics and predicting specific behavioral interference patterns.
This study demonstrates that flexible evidence accumulation during perceptual decision-making is supported by two independent mechanisms: stimulus-specific sensory adaptation in the MT area that shapes evidence encoding, and pupil-linked arousal that likely modulates the accumulation process itself.
This study reveals that visuomotor flexibility in the marmoset frontal cortex arises from a mosaic organization of visual and motor maps with distinct spatial scales, where their superposition creates a moiré pattern that enables flexible transformations between visual inputs and saccade outputs.
This study demonstrates that volumetric reductions in specific thalamic nuclei, detectable via standard T1-weighted MRI, serve as practical biomarkers for identifying preclinical and symptomatic Alzheimer's disease and improving clinical classification accuracy.
The paper introduces the Decisions From Experience Database (DfE-DB), a comprehensive open-access resource harmonizing 3.8 million trial-level decisions from 11,921 participants across 168 studies to enable systematic comparisons of how design features shape human decision-making tendencies and foster reproducible research.
This study demonstrates that intranasal delivery of hypoxia-primed Wharton's jelly mesenchymal stem cell-derived small extracellular vesicles (WJ-H-sEVs) effectively ameliorates behavioral deficits and neuroinflammation in a valproic acid-induced mouse model of autism spectrum disorder by reprogramming neuroimmune signaling through the activation of the Nrf2 pathway and suppression of NF-κB and JAK-STAT signaling.
This study constructs a single-cell atlas of dorsal root ganglion remodeling following neuroma-forming nerve injury to compare the effects of proximal crush and nerve resection interventions, revealing distinct glial and immune transcriptional programs that differentiate permissive regeneration from persistent pain states and offering new targets for mechanism-guided therapies.
Using in vivo calcium imaging in rats, this study reveals that distinct neural ensembles in the prelimbic cortex independently track different facets of motivation for both cocaine and water rewards, rather than relying on a shared neural population to drive behavior across various tasks.
This study presents the first large-scale comparative analysis of 26 hybrid deep learning architectures for reconstructing speech envelopes from EEG signals, demonstrating that combining CNNs with LSTMs and GCNs effectively captures complex spatio-temporal patterns and offers practical guidelines for advancing robust non-invasive brain-computer interfaces.