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Found 3 papers, 1 papers with code
Stimulus domain transfer in recurrent models for large scale cortical population prediction on video
However, in many cases this approach requires that the model is able to generalize to stimulus statistics that it was not trained on, such as band-limited noise and other parameterized stimuli.
Compressive neural representation of sparse, high-dimensional probabilities
This paper shows how sparse, high-dimensional probability distributions could be represented by neurons with exponential compression.
Learning unbelievable probabilities
On the contrary, here we show that many probability distributions have marginals that cannot be reached by belief propagation using any set of model parameters or any learning algorithm.
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