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2016

Linear dynamical neural population models through nonlinear embeddings

Gao, Yuanjun, Archer, Evan, Paninski, Liam et al.

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A body of recent work in modeling neural activity focuses on recovering low-dimensional latent features that capture the statistical structure of large-scale neural populations.

Most such approaches have focused on linear generative models, where inference is computationally tractable.
Here, we propose fLDS, a general class of nonlinear generative models that permits the firing rate of each neuron to vary as an arbitrary smooth function of a latent, linear dynamical state.
This extra flexibility allows the model to capture a richer set of neural variability than a purely linear model, but retains an easily visualizable low-dimensional latent space.

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