Kavli Affiliate: Eliska Greplova
| First 5 Authors: Vinicius Hernandes, Eliska Greplova, , ,
| Summary:
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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