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Found 4 papers, 2 papers with code
Expressivity of Neural Networks with Random Weights and Learned Biases
Landmark universal function approximation results for neural networks with trained weights and biases provided impetus for the ubiquitous use of neural networks as learning models in Artificial Intelligence (AI) and neuroscience.
Capturing cross-session neural population variability through self-supervised identification of consistent neuron ensembles
Classification of consistent versus unfamiliar neurons across sessions and accounting for deviations in the order of consistent recording neurons in recording datasets over sessions of recordings may then maintain decoding performance.
Targeted Neural Dynamical Modeling
These approaches, however, are limited in their ability to capture the underlying neural dynamics (e. g. linear) and in their ability to relate the learned dynamics back to the observed behaviour (e. g. no time lag).
Machine learning for neural decoding
Despite rapid advances in machine learning tools, the majority of neural decoding approaches still use traditional methods.
