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Found 4 papers, 3 papers with code
Latent Diffusion for Neural Spiking Data
Here, we present Latent Diffusion for Neural Spiking data (LDNS), a diffusion-based generative model with a low-dimensional latent space: LDNS employs an autoencoder with structured state-space (S4) layers to project discrete high-dimensional spiking data into continuous time-aligned latents.
Inferring stochastic low-rank recurrent neural networks from neural data
A central aim in computational neuroscience is to relate the activity of large populations of neurons to an underlying dynamical system.
A Practical Guide to Sample-based Statistical Distances for Evaluating Generative Models in Science
To demonstrate how these distances are used in practice, we evaluate generative models from different scientific domains, namely a model of decision-making and a model generating medical images.
Neural Latents Benchmark '21: Evaluating latent variable models of neural population activity
We curate four datasets of neural spiking activity from cognitive, sensory, and motor areas to promote models that apply to the wide variety of activity seen across these areas.
