1813 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 establishes a comprehensive framework for layer-specific wide-field calcium imaging in mice by introducing depth-dependent registration maps and deconvolution techniques to correct for light scattering, thereby enabling the detailed characterization of mesoscale functional connectivity across cortical layers during awake resting states.
This study establishes a topological framework linking human brain functional connectivity to underlying neurotransmission, demonstrating that regional variations in rsfMRI and MEG synchronization are robustly predicted by the distribution of neurotransmitter receptors and transporters, with specific patterns replicated across healthy cohorts and altered in clinical populations.
This study demonstrates that age-related anatomical development of hippocampal fiber tracts, particularly the rapid growth of short-range medial-temporal connections and the pruning of long-range occipito-parietal tracts, drives the functional specialization of the human hippocampus during childhood and adolescence.
This paper presents a large-scale, open-access dataset that synchronizes two-photon calcium imaging of the primary auditory cortex with auditory discrimination behavior in mice, formatted as standardized tensors to serve as a benchmark for developing and testing AI-driven neural decoding algorithms.
This study challenges the prevailing model of SynGAP1 dispersion during synaptic plasticity by demonstrating that endogenous SynGAP1 remains stably anchored at synapses while its local translation is transiently suppressed to constrain plasticity.
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.