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Found 6 papers, 2 papers with code
Switching Autoregressive Low-rank Tensor Models
In contrast, SLDSs can capture long-range dependencies in a parameter efficient way through Markovian latent dynamics, but present an intractable likelihood and a challenging parameter estimation task.
Deep learning approaches for neural decoding: from CNNs to LSTMs and spikes to fMRI
Deep learning has been shown to be a useful tool for improving the accuracy and flexibility of neural decoding across a wide range of tasks, and we point out areas for future scientific development.
The Roles of Supervised Machine Learning in Systems Neuroscience
Over the last several years, the use of machine learning (ML) in neuroscience has been rapidly increasing.
Machine learning for neural decoding
Despite rapid advances in machine learning tools, the majority of neural decoding approaches still use traditional methods.
Puzzle Imaging: Using Large-scale Dimensionality Reduction Algorithms for Localization
Current high-resolution imaging techniques require an intact sample that preserves spatial relationships.
Physical Principles for Scalable Neural Recording
Simultaneously measuring the activities of all neurons in a mammalian brain at millisecond resolution is a challenge beyond the limits of existing techniques in neuroscience.
Neurons and Cognition Biological Physics
