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Using Quantum Computing to Infer Dynamic Behaviors of Biological and Artificial Neural Networks
This is a component of the still-nascent topic of applying quantum computing to the modeling and simulations of biological and artificial neural networks.
Learning without gradient descent encoded by the dynamics of a neurobiological model
The success of state-of-the-art machine learning is essentially all based on different variations of gradient descent algorithms that minimize some version of a cost or loss function.
Generalizable Machine Learning in Neuroscience using Graph Neural Networks
Although a number of studies have explored deep learning in neuroscience, the application of these algorithms to neural systems on a microscopic scale, i. e. parameters relevant to lower scales of organization, remains relatively novel.
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