We focus throughout on how our results can aid the analysis and interpretation of experimental data. We quantify these results using nonlinear measures of intrinsic dimensionality and linear decodability of latent variables, and provide mathematical arguments for why such useful predictive representations emerge. Using a recurrent neural network model trained to predict a sequence of observations we show that network dynamics exhibit low-dimensional but nonlinearly transformed representations of sensory inputs that map the latent structure of the sensory environment. Specifically, we ask whether and when network mechanisms for sensory prediction coincide with those for extracting the underlying latent variables. Here, we investigate the hypothesis that a means for generating representations with easily accessed low-dimensional latent structure, possibly reflecting an underlying semantic organization, is through learning to predict observations about the world. Their success is often ascribed to the emergence of the taskвЂ™s low-dimensional latent structure in the network activity вЂ“ i.e., in the learned neural representations. Artificial neural networks have recently achieved many successes in solving sequential processing and planning tasks.

Matt weiler inetwork