1285 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 establishes a theoretical framework linking neural population codes to uncertainty by demonstrating that ancillary information, quantified through mutual information and Fisher information loss, inherently produces non-Gaussian estimation errors and defines an upper bound on the uncertainty knowledge accessible from neural activity alone.
This paper analytically demonstrates that two coupled oscillating brain circuits with delayed, odd-function interactions inherently implement a proportional-derivative (PD) control law near synchrony, establishing a direct link between oscillatory synchronization and feedback control without requiring additional mechanisms.
This study identifies a specific GABAergic inhibitory neuron circuit in the *Drosophila* antennal lobe that computes direction-selective signals from bilateral odor inputs, enabling flies to detect and navigate odor motion independent of wind sensing.
This longitudinal neuroimaging study reveals that the maturation of corticostriatal circuits follows distinct trajectories where peak adolescent connectivity in the ventral striatum predicts alcohol initiation, while a failure of the dorsal striatum to show normative connectivity decreases is associated with escalated alcohol use in adulthood.
Using fMRI, this study demonstrates that learning to read a second language establishes a new, parallel neural representation in the Visual Word Form Area alongside the stable native-language representation, thereby supporting the Overlay Model over the Competing Model of neural resource allocation.
This study establishes the deepest human axonal proteome to date by combining microfluidic separation with DIA-MS on an Orbitrap Astral mass spectrometer to profile compartment-resolved proteomes in iPSC-derived neurons, revealing distinct axonal signatures and providing a quantitative reference for understanding neurodegenerative disease mechanisms.
This study introduces a scalable unsupervised framework that analyzes resting-state fMRI data from nearly 20,000 UK Biobank participants to identify latent functional brain subgroups, revealing significant associations between these distinct neural patterns and diverse cognitive, lifestyle, health, and genetic factors.
This study demonstrates that excitatory delay-coupling between brain regions mechanistically explains the prevalence of in-phase and antiphase functional connectivity, challenging the assumption that zero-lag synchronization is merely volume conduction and introducing a new metric, the Phase Relationship Index, to detect functionally relevant connectivity changes.
In a longitudinal study of over 4,500 youth, researchers found that exposure to threat (but not deprivation or unpredictability) selectively drives a dose-dependent expansion of the somato-cognitive action network toward sensorimotor regions, which in turn mediates the link between early adversity and poorer cognitive outcomes.
This study demonstrates that integrating prosthetic limbs into amputated crickets restores coordinated locomotion not by replicating intact anatomy, but by re-establishing load-mediated sensory feedback that drives the self-organization of distributed sensorimotor networks, revealing a hierarchical control architecture where temporal coordination is selectively recovered over spatial precision.