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Recurrent networks are trained to memorize their input better, often in thehopes that such training will increase the ability of the network to predict.We show that networks designed to memorize input can be arbitrarily bad atprediction. We also find, for several types of inputs, that one-node networksoptimized for prediction are nearly at upper bounds on predictive capacitygiven by Wiener filters, and are roughly equivalent in performance to randomlygenerated five-node networks. Our results suggest that maximizing memorycapacity leads to very different networks than maximizing predictive capacity,and that optimizing recurrent weights can decrease reservoir size by half anorder of magnitude.

The difference between memory and prediction in linear recurrent networks