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Found 3 papers, 1 papers with code
Understanding Neural Coding on Latent Manifolds by Sharing Features and Dividing Ensembles
These innovations lead to more interpretable models of neural population activity that train well and perform better even on mixtures of complex latent manifolds.
The Stochastic complexity of spin models: Are pairwise models really simple?
We thus found that the complexity (or simplicity) of a model is not determined by the order of the interactions, but rather by their mutual arrangements.
Variational perturbation and extended Plefka approaches to dynamics on random networks: the case of the kinetic Ising model
We first briefly consider the variational approach based on minimizing the Kullback-Leibler divergence between independent trajectories and the real ones and note that this approach only coincides with the mean field equations from the saddle point approximation to the generating functional when the dynamics is defined through a logistic link function, which is the case for the kinetic Ising model with parallel update.
