1296 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 presents a theoretical model of nonlinear recurrent neural networks with low-rank connectivity that explains how small modulations of effective connectivity enable flexible multi-task computation by selecting distinct low-dimensional neural manifolds while avoiding interference-induced chaos.
This paper proposes a latent Gaussian process extension of the Simplified Reference Tissue Model (LGPE-SRTM) that employs a hierarchical framework with a conditionally linear mixed-effects structure to enable efficient, scalable, and robust population-level inference on time-varying neurotransmitter dynamics in dynamic PET data without restrictive parametric assumptions.
This study demonstrates that elevating mitochondrial calcium uniporter (MCU) expression in the hippocampus accelerates calcium uptake and enhances respiratory efficiency in proportion to bioenergetic demand without increasing susceptibility to calcium overload.
Through two preregistered EEG experiments, this study demonstrates that precision-weighted integration of prior expectations and sensory evidence in visual motion decisions occurs primarily during late response planning stages rather than early sensory processing, thereby challenging predictive processing accounts.
This paper presents a low-cost, open-source closed-loop reinforcement learning system that enables efficient, goal-directed optimization of spatiotemporal stimulation patterns in topologically constrained in vitro neuronal networks by characterizing their state-dependent responses and demonstrating that learning agents can identify non-trivial stimulation strategies to evoke specific target activity motifs.
This study reveals that the spatial pattern of resting-state EEG alpha-BOLD coupling is significantly predicted by the cortical gradients of specific gene expression profiles, including layer 6 VIP interneurons, excitatory layer 5 neurons, and the GRIN2C NMDA receptor subunit, thereby identifying concrete neurobiological candidates for future investigation.
This study demonstrates that pupil constriction, independent of visual stimulation, triggers distinct activity in both the human retina and visual cortex, likely as a response to the sudden reduction in light exposure.
This paper presents a recurrent neural network model that learns to select computations by integrating rational meta-reasoning with meta-learning, thereby offering a mechanistic account of how the brain achieves flexible and efficient adaptive control of thought.
This study demonstrates that the size of visuomotor rotation significantly influences both the likelihood of spontaneous strategy development and the specific learning style (gradual, stepwise, or exploratory) adopted by individuals, challenging the view of explicit adaptation as a homogeneous process.
Using hybridization chain reaction, this study reveals that cerebrospinal fluid-contacting neurons in the spinal cord express receptors for glutamate, somatostatin, and LDL, thereby establishing diverse chemosensory pathways that facilitate long-distance communication between neurons and glia via the cerebrospinal fluid.