Exploring Biological Neuronal Correlations with Quantum Generative Models

Vinicius Hernandes, Eliska Greplova
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Abstract

Understanding of how biological neural networks process information is one of the biggest open scientific questions of our time. Advances in machine learning and artificial neural networks have enabled the modeling of neuronal behavior, but classical models often require a large number of parameters, complicating interpretability. Quantum computing offers an alternative approach through quantum machine learning, which can achieve efficient training with fewer parameters. In this work, we introduce a quantum generative model framework for generating synthetic data that captures the spatial and temporal correlations of biological neuronal activity. Our model demonstrates the ability to achieve reliable outcomes with fewer trainable parameters compared to classical methods. These findings highlight the potential of quantum generative models to provide new tools for modeling and understanding neuronal behavior, offering a promising avenue for future research in neuroscience.
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用量子生成模型探索生物神经元相关性
了解生物神经网络如何处理信息是当代最大的科学难题之一。机器学习和人工神经网络的进步使得神经元行为建模成为可能,但经典模型往往需要大量参数,从而使可解释性变得复杂。量子计算通过量子机器学习提供了另一种方法,它可以用更少的参数实现高效训练。在这项工作中,我们引入了一个量子生成模型框架,用于生成合成数据,捕捉生物神经元活动的空间和时间相关性。与经典方法相比,我们的模型能够以较少的可训练参数获得可靠的结果。这些发现凸显了量子生成模型为神经元行为建模和理解提供新工具的潜力,为神经科学的未来研究提供了一条光明大道。
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