Multimodal subspace identification for modeling discrete-continuous spiking and field potential population activity
Biorxiv : the Preprint Server for Biology
|July 3, 2023
Summary
We developed a new multiscale subspace identification (multiscale SID) algorithm for efficient learning of multimodal neural data. This method accurately models complex brain activity, improving behavior prediction and reducing computational cost for applications like brain-machine interfaces.
Area of Science:
- Computational Neuroscience
- Machine Learning
- Systems Neuroscience
Background:
- Learning latent state models from multimodal neural data (spiking and field potentials) is key to understanding collective brain dynamics and decoding behavior.
- Efficient unsupervised learning methods are crucial for real-time applications like brain-machine interfaces (BMIs), but are challenging for heterogeneous spike-field data.
Conclusions:
- Multiscale SID is an accurate and computationally efficient method for learning dynamical latent state models from multimodal neural data.
- This algorithm is particularly beneficial for real-time applications requiring efficient learning, such as brain-machine interfaces.
- The method advances the ability to model complex neural dynamics and decode behavior using fused multimodal information.
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