Rejoinder of "Identifiability of latent-variable and structural-equation models: from linear to nonlinear".

The article discusses the implementation of independent component analysis (ICA) in the brain and its potential for modeling brain computations. The author suggests that while modeling the brain on statistical and objective levels is promising, modeling the algorithmic level is more challenging due to the difficulty of measuring single neurons and their learning. The author also discusses the interpretation of nonlinear ICA as defining an exponential model and the use of self-supervised learning methods in machine learning. Overall, the article highlights the complexities and challenges of nonlinear ICA as a statistical model and its potential applications in understanding brain computations. [Extracted from the article]

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