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.
By integrating large-scale neuroimaging data with multimodal biological maps, this study reveals that human brain reorganization under electrical perturbation follows multiscale principles linking molecular and cellular constraints to network-level plasticity, which in turn predicts behaviorally relevant symptom changes.
This paper proposes a hybrid predictive coding model demonstrating that delusions arise not from faulty inference but as an adaptive reorganisation of generative models triggered by inadequate initialisation and excessive certainty, thereby explaining their thematic stability and resistance to contradictory evidence.
This paper demonstrates that convolutional neural networks can develop a unified neural code for both object category and category-independent features, and provides a theoretical framework based on manifold geometry to explain how such joint representations enable accurate decoding of continuous variables while maintaining classification performance.
This study reveals that in ALS, hyperactive homeostatic mechanisms paradoxically increase motoneuron excitability in response to riluzole treatment, thereby counteracting the drug's intended suppressive effects and explaining its limited clinical efficacy, while simultaneously highlighting a neuroprotective benefit through the normalization of motoneuron cell size.
This study demonstrates that unsupervised autoencoding models trained on MT-like optic flow signals, rather than supervised self-motion estimation tasks, provide the best computational explanation for the neural tuning properties of the primate dorsal area MSTd.
This study reveals that while stimulus familiarity (habituation) and reward association engage distinct disinhibitory circuits to differentially modulate pyramidal cell responsivity in mouse visual cortex, both forms of learning converge on reducing the PV-to-SST input ratio to shift short-term adaptation from depression toward sensitization.
By combining computational simulations with human motoneuron recordings, this study reveals that high-frequency common synaptic inputs drive heterogeneous, frequency-dependent entrainment in distinct motoneuron subpopulations—a phenomenon masked in conventional whole-pool analyses but detectable via a novel firing-locked method.
By integrating neuroimaging, gene expression data, and network modeling, this study reveals that the development of highly-connected cortical hubs in the human brain is driven not by preferential targeting from higher-order thalamic nuclei, but by the interdependent spatiotemporal constraints of wiring distance and the specific maturation timing of thalamocortical innervation.
This study reveals that the human language network follows a non-linear developmental trajectory characterized by early localization, a transiently distributed state with a connectivity dip during adolescence, and a return to refined localization in adulthood, thereby reconciling localized and distributed architectural views.
This study combines data analysis and computational modeling to demonstrate that phasic dopamine not only drives learning through reward prediction errors but also directly modulates conditioned responding, challenging the traditional view that its effects are solely mediated by value learning.