1825 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 reveals that the ventrolateral prefrontal cortex (vlPFC) processes visual information through two successive stages: an early, rapid encoding of low spatial frequency features to form coarse priors, followed by a later, richer representation of detailed visual scenes and sub-categories.
This study identifies and functionally characterizes two TRPV channels in *Octopus vulgaris* as polymodal nociceptors that rescue aversive responses in *C. elegans* mutants and form active heteromeric channels in *Xenopus* oocytes, providing molecular evidence for nociceptive mechanisms in cephalopods.
By analyzing 7T fMRI data from humans viewing natural images, this study reveals that the visual cortex implements distinct prediction regimes across the visual field, where central vision exhibits a hierarchical sensitivity to predictability from low to high levels, while peripheral vision shows amplified effects where even early areas respond to high-level unpredictability.
This study demonstrates that knocking down TTLL1 in human iPSC-derived cortical neurons mitigates Aβ-induced TAU missorting, tubulin polyglutamylation, and synaptic dysfunction without compromising neuronal health, suggesting TTLL1 inhibition as a promising therapeutic strategy for Alzheimer's disease.
This study demonstrates that functional connectomes, particularly during movie-watching tasks, effectively predict memory performance, while the resulting brain-cognition gap serves as a dopamine-modulated biomarker linking lower dopamine binding and cardiovascular risk to reduced cognitive resilience.
The paper introduces openretina, a modular Python package designed to unify fragmented retinal research by providing a standardized, open-source framework for training, evaluating, and interpreting neural network models across diverse species and datasets to foster collaborative progress in computational neuroscience.
This study demonstrates that internal error-monitoring (ERN) in 12-month-old infants not only precedes but developmentally predicts the emergence of conceptual self-representation six months later, suggesting that the ability to detect one's own errors is a foundational mechanism for building a sense of self.
The paper presents Recurrent Integration Fractal Theory (RIFT), a novel framework proposing that consciousness is a causally efficacious, holographic endospace generated by fractal compression of sensory data within recurrent neural loops, which enables the Self to autopoietically control its molecular substrate through lipid-mediated ion channel regulation, thereby ensuring irreducibility, unity, and temporal continuity.
By applying lesion network mapping to 88 neurosurgical patients, this study identifies a causal brain network where posterior midline hubs constrain self-transcendent experience while brainstem and anterior midline regions facilitate it, revealing a distributed architecture overlapping with the default mode and frontoparietal control networks.
This paper introduces DCM-SR, a novel generative framework that overcomes the limitations of conventional Dynamic Causal Modelling by enabling continuous, time-varying parameter estimation without data segmentation, thereby allowing for the principled decomposition of slow cortical potentials and the investigation of neural mechanisms underlying sequential cognitive processes like preparation and motor inhibition.