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Found 5 papers, 1 papers with code
Efficient non-conjugate Gaussian process factor models for spike countdata using polynomial approximations
Gaussian Process Factor Analysis (GPFA) hasbeen broadly applied to the problem of identi-fying smooth, low-dimensional temporal struc-ture underlying large-scale neural recordings. However, spike trains are non-Gaussian, whichmotivates combining GPFA with discrete ob-servation models for binned spike count data. The drawback to this approach is that GPFApriors are not conjugate to count model like-lihoods, which makes inference challenging. Here we address this obstacle by introduc-ing a fast, approximate inference method fornon-conjugate GPFA models.
Neuroformer: Multimodal and Multitask Generative Pretraining for Brain Data
State-of-the-art systems neuroscience experiments yield large-scale multimodal data, and these data sets require new tools for analysis.
Identifying signal and noise structure in neural population activity with Gaussian process factor models
Here we address this shortcoming by proposing ``signal-noise'' Poisson-spiking Gaussian Process Factor Analysis (SNP-GPFA), a flexible latent variable model that resolves signal and noise latent structure in neural population spiking activity.
Deep Adversarial Belief Networks
We present a novel adversarial framework for training deep belief networks (DBNs), which includes replacing the generator network in the methodology of generative adversarial networks (GANs) with a DBN and developing a highly parallelizable numerical algorithm for training the resulting architecture in a stochastic manner.
Efficient non-conjugate Gaussian process factor models for spike count data using polynomial approximations
We demonstrate that PAL estimators achieve fast and accurate extraction of latent structure from multi-neuron spike train data.
