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David A. Klindt

3 accepted papers

2023

Understanding Neural Coding on Latent Manifolds by Sharing Features and Dividing Ensembles

ICLR 2023poster

Systems neuroscience relies on two complementary views of neural data, characterized by single neuron tuning curves and analysis of population activity. These two perspectives combine elegantly in neural latent variable models that constrain the relationship between latent variables and neural activ…

2021

Removing Inter-Experimental Variability from Functional Data in Systems Neuroscience

NeurIPS 2021spotlight

Integrating data from multiple experiments is common practice in systems neuroscience but it requires inter-experimental variability to be negligible compared to the biological signal of interest. This requirement is rarely fulfilled; systematic changes between experiments can drastically affect the…

2021

Towards Nonlinear Disentanglement in Natural Data with Temporal Sparse Coding

ICLR 2021oral

Disentangling the underlying generative factors from complex data has so far been limited to carefully constructed scenarios. We propose a path towards natural data by first showing that the statistics of natural data provide enough structure to enable disentanglement, both theoretically and empiric…