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.
This study demonstrates that the resilience of neural circuits to neurodegeneration is primarily determined by the strategic embedding of inhibitory neurons in structurally central positions, which maintains stable collective dynamics despite substantial synaptic and neuronal loss.
This study establishes a human iPSC-based in vitro model demonstrating that replicative senescence in neural progenitors induces astrocyte senescence in both 2D cultures and midbrain organoids, providing a platform to investigate the role of astrosenescence in Parkinson's disease pathology.
Using ultra-high field fMRI, this study demonstrates that frequency magnification in the human primary auditory cortex is better predicted by behavioral frequency discrimination performance than by cochlear resolution, suggesting that cortical processing, rather than peripheral input, constrains auditory hyperacuity.
This study demonstrates that the parafascicular thalamus facilitates cue-reward learning by dynamically regulating head and body orientation to steer attention, as evidenced by calcium activity tracking associative learning and optogenetic disruption impairing both orientation and learning.
This study demonstrates that splitter cells in random recurrent networks emerge generically as a task-driven phenomenon rather than a structural necessity, as their removal triggers robust network reorganization that preserves task performance and population geometry.
This paper presents a deep learning framework that leverages domain adaptation to translate high-resolution microscopy-derived fibre orientation maps from a macaque dataset to in vivo human diffusion MRI, thereby enabling biologically grounded structural connectivity mapping without requiring microscopy data at inference.
This study demonstrates that stroke survivors with no prior BCI experience can achieve real-time, individual finger-level control of a robotic hand using motor imagery and deep learning decoders, revealing that discriminable neural signals for fine motor control persist and can be leveraged through adaptive neural reorganization.
This study demonstrates that the subcellular localization of PTEN is a critical determinant of its function in neuronal development, revealing that cytoplasmic and membrane-targeted PTEN are required to fully restrain dendritic complexity and spine density, while nuclear-localized PTEN only partially rescues specific morphological defects.
This study demonstrates that the geometry of feedback vectors during learning determines whether recurrent networks can achieve zero-shot parallel composition of independently learned behaviors, revealing that separable dynamical subspaces enabled by distinct feedback allow for flexible recombination of tasks without joint training.
This study introduces a directed human structural connectome (DHSC) by transferring macaque tracer data to humans, demonstrating that incorporating anatomical directionality reveals a biologically plausible hierarchical organization and significantly improves the modeling of large-scale brain dynamics compared to traditional undirected networks.