1519 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 identifies a distinct supramammillary-to-lateral preoptic area (SuM-LPO) pathway that, through collateral projections and modulation of mesolimbic dopamine signaling, drives active behavioral responses to environmental challenges such as aversive stimuli and stress.
This study demonstrates that non-invasive temporal interference stimulation of the human hippocampus, when precisely timed to coincide with memory reactivation during sleep, enhances associative memory consolidation by boosting fast spindle amplitude and preserving slow oscillation-spindle coupling.
This study demonstrates that the widely projecting locus coeruleus can selectively modulate task-engaged hippocampal neurons and delay action timing through spatially confined dopamine release that synergizes with local neuronal activity, rather than relying on dedicated wiring.
This study demonstrates that dopamine release in the basolateral amygdala of freely moving mice encodes a state-dependent salience signal that is elevated during self-initiated exploration, adapts with experience, and tracks the behavioral relevance of predictive cues to prime circuits for plasticity.
This study identifies the adhesion GPCR FMIL-1, regulated by the transcription factor MEC-3 in *C. elegans* PVD neurons, as a critical molecular driver that directs specific synapse formation with PVC and AVA interneurons to establish the nociceptive circuit.
This study demonstrates that a transformer neural network can decode specific episodic memories from human intracranial neuronal spikes, revealing a dynamic shift in memory representation from the medial temporal lobe to the frontal cortex across wake-sleep cycles, which suggests new avenues for treating memory disorders via brain-computer interfaces.
By integrating whole-brain -synuclein pathology mapping with network diffusion modeling and a spatial gene expression atlas, this study identifies group II p21-activated kinases (PAKs) as key molecular regulators of regional vulnerability in Parkinson's disease and demonstrates that their inhibition effectively reduces aggregation and neuron loss both in vitro and in vivo.
This study presents the first systematic benchmark of four structural MRI foundation models, demonstrating that while performance varies across tasks and datasets, models like 3D-Neuro-SimCLR and AnatCL generally outperform traditional FreeSurfer-based baselines in predicting age, sex, and body-mass index, thereby establishing a new standard for evaluating learned representations in neuroimaging via the BrainFMBench platform.
This study demonstrates that the α2δ-2 subunit is essential for coupling presynaptic calcium entry to vesicle release in hippocampal parvalbumin-expressing interneurons, and its absence impairs synaptic inhibition, thereby contributing to the spontaneous seizures observed in knockout mice.
This paper demonstrates that recurrent excitatory-inhibitory networks with purely local plasticity, particularly BCM-like rules, can achieve predictive inference and learn sparse spatiotemporal features by maintaining weights on a consistency manifold, thereby linking predictive coding to biologically plausible circuit mechanisms without requiring explicit error representations.