1527 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 paper introduces "SENSI-EEG-Preproc-ICA-EKG-Trigger," a semi-automated MATLAB module that accelerates and standardizes the identification of EKG and trigger artifacts in EEG data by leveraging spectral characteristics to prioritize candidate components for efficient, user-verified manual review.
Using data from the ABCD Study, this multistage analysis reveals that while externalizing polygenic liability robustly predicts earlier adolescent substance initiation through both direct genetic pathways and specific brain imaging phenotypes, the neurobiological mediation by baseline brain measures accounts for less than 2% of the total effect, indicating that direct genetic influences are the dominant mechanism.
This study utilizes experience sampling and brain imaging to demonstrate that the stability of human thought is context-dependent rather than intrinsic to specific tasks, revealing that coordinated activity within the multiple-demand network supports stable, goal-focused cognition when deliberate attention is high and distraction is low.
This paper proposes and validates a neural model demonstrating that self-directed learning in zebra finches can be bootstrapped by a local forebrain circuit that uses anti-Hebbian plasticity to predictively cancel memorized tutor songs, thereby generating internal error signals sufficient to guide reinforcement learning without external rewards.
This paper demonstrates that representational magnitude, a geometric property of distributed feature representations in both neural and artificial systems, serves as a general predictor of memorability across visual and lexical domains, suggesting that memory strength is inherent to the intensity and breadth of stimulus encoding.
Using multi-site electrophysiology in freely behaving rats, this study reveals that locus coeruleus activity exhibits state-dependent, inverse dynamics with hippocampal ripples, decreasing prior to ripple onset to suggest a critical role for the LC-NE system in coordinating cortical-subcortical networks for systems-level memory consolidation.
This study presents a biophysically detailed model demonstrating that sleep-modulated disinhibition drives both synaptic and systems memory consolidation by enabling spontaneous, time-compressed replay of spatial sequences, a process accelerated by ripples but capable of sustaining memory transfer even when ripples are absent.
This study proposes that selective vulnerability in neurodegenerative diseases arises from evolutionary trade-offs where high-computational brain regions accumulate thermodynamic entropy proportional to their workload, leading to structural failure and dynamic instability.
The authors developed and validated two novel genetically encoded voltage indicators, JEDI3sub and JEDI3hyp, which overcome previous sensitivity limitations to enable high-resolution, two-photon recording of millivolt-scale subthreshold voltage dynamics across diverse neuronal structures in awake, behaving mice.
This paper presents a scalable single-molecule fluorescence in situ hybridization workflow that achieves whole-organism spatial transcriptomics at single-cell resolution in *C. elegans*, enabling the multiplexed profiling of up to 40 genes to identify neuronal classes and reveal sex- and neuron-specific expression patterns.