1820 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 demonstrates that as walking speed increases, humans transition from a discrete-dominated, less smooth movement regime with fewer muscle synergies to a more stable, rhythmic pattern characterized by higher kinematic smoothness and increased neuromuscular dimensionality, with a critical transition zone occurring around 3–3.5 km/h.
This study demonstrates that pathological cortico-STN beta coupling in Parkinson's disease is exclusively confined to brief beta burst epochs rather than being sustained during non-burst periods, suggesting that adaptive neuromodulation strategies should target these transient events.
This paper proposes a six-layer computational framework that models coupled neural oscillator networks as analog RLC circuits, demonstrating how subthreshold resonance, phase-based binding, and tunable impedance landscapes enable memory, pattern completion, and multiplexed computation while bridging the gap between linear electrical descriptions and nonlinear attractor dynamics.
This study utilizes directly reprogrammed human neurons to demonstrate that aging leads to mitochondrial health decline and impaired mitophagy, characterized by the accumulation of damaged mitochondria in unacidified autolysosomes, thereby providing a critical model for understanding age-related neurodegeneration and identifying therapeutic targets.
This study demonstrates that a single session of 10 Hz rTMS over the right dorsolateral prefrontal cortex can effectively reduce automatic alcohol approach tendencies in healthy drinkers by strengthening top-down neural control mechanisms, as evidenced by enhanced N2 amplitudes and improved avoidance responses.
By combining densely sampled fMRI data with artificial neural network-based encoding models, this study reveals that category-selective areas in the occipitotemporal cortex exhibit fine-grained, distinct feature sensitivities for bodies, hands, and tools that go beyond broad categorical tuning.
This study found that high-definition transcranial random noise stimulation (HD-tRNS) applied to the right dorsolateral prefrontal cortex provided no measurable benefit for learning or performance transfer in young pilots undergoing complex cognitive task training compared to a sham group.
This study demonstrates that fractional anisotropy derived from diffusion-weighted MRI serves as a robust, non-invasive surrogate marker for brain tissue stiffness in healthy humans, showing a strong negative correlation with the shear modulus of a hyperelastic Ogden model across multiple datasets.
This study identifies distinct but interacting neurocomputational mechanisms in the human brain that support multisensory learning by dissociating structure-based statistical learning, reward-based reinforcement learning, and outcome-surprise processing into complementary, modality-general neural networks.
This paper introduces Live Spike Sorting (LSS), a robust system built on the Kilosort platform that enables real-time, high-fidelity sorting of single-neuron spikes from large-scale recordings, demonstrating performance comparable to offline methods and superior to traditional threshold-based approaches for applications like brain-computer interfaces.