1795 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.
Inspired by the insect fan-shaped body, this paper presents a recurrent neural network model that uses memory of past movements to dynamically shape an agent's velocity setpoints, thereby enabling the generation of diverse exploration paths and the stabilization of lower-level motor control.
This paper introduces Switching Shared Latent Dynamics (SSLD), a unified framework that learns an interpretable shared latent space with nonlinear switching dynamics to simultaneously model the relationship between high-dimensional neural activity and behavior while isolating modality-specific variability across diverse experimental datasets.
This study demonstrates that archerfish, despite lacking a laminated cortex, exhibit conserved decision-making principles such as trial-history and spatial biases similar to mammals and birds, while also displaying unique adaptations to reward omission.
This paper presents a modular Gateway cloning platform for the streamlined production of recombinant AAV vectors that, when combined with AOD-based ULoVE two-photon microscopy, enables precise, cell-type-specific optical interrogation of neural circuits with high spatiotemporal resolution for both voltage/calcium imaging and optogenetic manipulation.
This machine-assisted systematic review and meta-analysis suggests that citrus flavonoids may alleviate pain in streptozotocin-induced diabetic neuropathy animal models, but the findings are limited by significant methodological biases and heterogeneity, highlighting the need for more rigorous preclinical studies before clinical translation.
This study maps 16 neurodegenerative disease proteins onto a proteostasis network to reveal that distinct proteinopathies share specific molecular components rather than just general processes, thereby identifying key network vulnerabilities like PRKN, HSPA8, and UBQLN2 as potential cross-disease therapeutic targets.
This paper introduces a dual-kernel deconvolution model that separates pupillary constriction and dilation to recover high-temporal-resolution latent attentional dynamics from pupil signals, overcoming the limitations of the sluggish pupillary response in rapid visual paradigms.
This study employs a biophysical neural model to demonstrate that excitatory/inhibitory imbalance and altered inter-areal connectivity in Alzheimer's disease mechanistically explain reduced visual motion illusion susceptibility, offering a potential framework for probing early AD neurobiology.
This study demonstrates that 3D-printed, hair-anchored EEG electrode attachments significantly improve signal quality, reduce impedance, and eliminate connection failures for individuals with Type 4 Afro-textured hair compared to conventional caps, offering a more inclusive and effective solution for neuroimaging research.
This study combines anatomical tracing and computational modeling in mice to reveal how segregated basal ganglia and cerebellar learning signals converge in the thalamus and project to specific cortical layers, establishing a modular credit-routing architecture that aligns subcortical feedback with cortical action representations to enhance learning.