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 study identifies distinct spectrotemporal signatures in the awake macaque brain that differentiate driving inputs, characterized by broadband phase coherence and robust firing, from modulatory inputs, which exhibit narrowband phase alignment without concurrent spiking, thereby revealing a widespread mechanism for coordinating multisensory and motor influences on perception.
This study demonstrates that novel words containing identifiable morphemes trigger neurocognitive semantic processing and fine-grained semantic representations comparable to existing words, suggesting that the distinction between processing real and novel words is quantitative rather than qualitative and depends on the reliability of linguistic form pointing to meaning.
This paper proposes and validates a corticostriatal neural network model demonstrating that massive cortical convergence creates a low-dimensional bottleneck that separates control signals from time-encoding dynamics, thereby unifying diverse dorsolateral striatum functions such as action chunking, duration estimation, and motor timing.
This study demonstrates that human balancing performance on a real-world inverted pendulum is strongly constrained by the plant's passive dynamics, with shorter, faster-responding rods proving significantly more difficult to stabilize than longer ones despite participants' ability to learn the task.
This study utilizes large-scale EEG and MEG data to reveal that natural object representations in the human brain undergo a rapid, transient expansion in dimensionality peaking within 100 milliseconds, a dynamic process that correlates with decoding accuracy but exceeds the explanatory power of current behavioral and deep neural network models.
Despite strong population-level correspondence, this study reveals that individual-level functional connectivity fingerprints derived from fMRI and MEG show surprisingly low similarity, with structural variability accounting for most age-related variance shared between the modalities while MEG uniquely captures cognitive traits not reflected in fMRI.
This study demonstrates that premovement suppression of corticospinal excitability is a multifactorial process influenced by both preparatory and initiation-related mechanisms, with the magnitude of suppression varying across simple reaction time, choice reaction time, and go/no-go tasks depending on their specific preparation and response requirements.
Using EEG and multivariate pattern analysis, this study demonstrates that human temporal perception relies on parallel, functionally distinct neural processes that encode physical duration and categorical decisions along orthogonal dimensions, allowing them to be dissociated despite their behavioral interdependence.
This study demonstrates that in common marmosets, the dentate gyrus encodes stationary and moving self-positions independently, while the CA3 subfield integrates these spatial inputs with temporal-order information to form conjunctive spatiotemporal maps essential for episodic memory.
This study analyzes a large-scale, multi-vendor DTI dataset of 2,700 healthy controls to demonstrate that age, sex, MRI vendor, and atlas selection are significant sources of variance that must be accounted for when interpreting brain microstructural metrics.