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Found 2 papers, 1 papers with code
Decomposing neural networks as mappings of correlation functions
Understanding the functional principles of information processing in deep neural networks continues to be a challenge, in particular for networks with trained and thus non-random weights.
Inference of a mesoscopic population model from population spike trains
We derive the likelihood of both single-neuron and connectivity parameters given this activity, which can then be used to either optimize parameters by gradient ascent on the log-likelihood, or to perform Bayesian inference using Markov Chain Monte Carlo (MCMC) sampling.
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