471 papers
This collection explores the emerging intersection of quantum mechanics and biology, where researchers investigate whether quantum phenomena play a functional role in living systems. From understanding how birds navigate using magnetic fields to probing the efficiency of photosynthesis, these studies challenge our traditional view of life as purely classical. While the field is still young and often theoretical, it promises to reveal new layers of complexity in how nature operates at the smallest scales.
Every paper featured here is a preprint sourced directly from arXiv, meaning the research is shared by scientists before formal peer review. At Gist.Science, we process each new entry in this category to ensure accessibility for everyone. You will find both detailed technical summaries for experts and plain-language explanations that break down the core concepts without sacrificing accuracy.
Below are the latest papers in Q-Bio — Nc, offering a fresh look at the quantum frontiers of biological science.
This paper reveals that current vision-language models suffer from "illusion-illusions," erroneously perceiving non-illusory images (such as genuinely crooked lines or differently sized circles) as optical illusions, thereby exposing fundamental processing errors in their visual reasoning.
This study demonstrates that a portable, low-cost setup using two smartphones and a wireless EEG amplifier can successfully record the readiness potential during basketball free-throw shooting in real-world settings, confirming the presence of this brain signal prior to movement but finding no significant correlation between its amplitude and shooting success.
This paper argues that to overcome the limitations of static and fragmented scientific infrastructure, neuroscience should serve as a stress test for adopting a dynamic, AI-aligned ecosystem that fosters decentralized, self-learning collaboration between humans and machines to accelerate discovery and ensure reproducibility.
This paper introduces a geometry-aware deep learning framework that utilizes layer-wise local training to sculpt internal representations for enhanced adversarial robustness, supported by data-dependent statistical mechanics and a phenomenological Hebbian coupling model.
This paper reviews advances in biorealistic neuromechanical models that integrate neural controllers with body and environmental simulations, demonstrating their utility for inferring unmeasurable biophysical variables, generating testable hypotheses, and fostering interdisciplinary collaboration across neuroscience, robotics, and healthcare.
By applying persistent homology to spontaneous activity recordings from human and mouse cortical organoids, this study demonstrates that neural networks exhibit robust, non-redundant topological loop structures that emerge significantly above null models and scale with network size, validating topological data analysis as a tool for resolving intrinsic structure in experimental neural data.
This chapter provides a comprehensive overview of the methodological foundations and practical workflows for analyzing large-scale brain networks using noninvasive EEG and MEG, covering physical principles, source reconstruction, connectivity measures, modern analysis pipelines, and emerging applications in health and disease.
This paper introduces STSBench, a large-scale dataset comprising recordings from over 2,000 neurons in the primate superior temporal sulcus while viewing natural videos, designed to advance the modeling and benchmarking of dorsal stream neuronal responses which have previously been hindered by a lack of sufficient data.
This paper presents a minimal, interpretable diffusion model based on sparse coding with pairwise interactions that mimics V1 horizontal connections to achieve high-performance image denoising while providing mechanistic insights into both biological perceptual inference and the internal workings of black-box diffusion architectures.
This study demonstrates that while static body posture is sufficient for classifying daily activities, the temporal dynamics of movement are essential for reconstructing natural motion, revealing a fundamental dissociation between recognition and generation in human movement analysis.