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Found 4 papers, 2 papers with code
Expressive probabilistic sampling in recurrent neural networks
We show that the firing rate dynamics of a recurrent neural circuit with a separate set of output units can sample from an arbitrary probability distribution.
Neural Co-Processors for Restoring Brain Function: Results from a Cortical Model of Grasping
Approach: To achieve goal-directed closed-loop neurostimulation, we propose "neural co-processors" which use artificial neural networks and deep learning to learn optimal closed-loop stimulation policies, shaping neural activity and bridging injured neural circuits for targeted repair and rehabilitation.
Predictive Coding Theories of Cortical Function
Specifically, the Rao-Ballard hierarchical predictive coding model assumes that the top-down feedback connections from higher to lower order cortical areas convey predictions of lower-level activities.
Improved Training of Sparse Coding Variational Autoencoder via Weight Normalization
We propose a few heuristics to improve the training of SVAE and show that a unit $L_2$ norm constraint on the decoder is critical to produce sparse coding filters.
