In this task, you are given a paper review. Based on the review, your job is to identify language and generate "en" if the review is in English or generate "es" if the review is in Spanish. Note that URLs in the text have been replaced with [Link].

This paper explores the ability of nonlinear recurrent neural networks to account for neural response properties that have otherwise eluded the ability of other models. A multilayer rnn is trained to imitate the stimulus-response mapping measured from actual retinal ganglion cells in response to a sequence of natural images. The rnn performs significantly better, especially in accounting for transient responses, than conventional LN/GLM models. This work is an important step in understanding the nonlinear response properties of visual neurons. Recent results have shown that the responses of even retinal ganglion cells in response to natural movies are difficult to explain in terms of standard receptive field models. So this presents an important challenge to the field. If we even had *a* model that works, it would be a starting point. So this work should be seen in that light. The challenge now of course is to tease apart what the rnn is doing. Perhaps it could now be pruned and simplified to see what parts are critical to performance. It would have been nice to see such an analysis. Nevertheless this result is a good first start and I think important for people to know about. I am a bit confused about what is being called a "movie." My understanding is that it is essentially a sequence of unrelated images shown for 1 sec. each. But then it is stated that the "frame rate" is 1/8.33 ms. I think this must refer to the refresh rate of the monitor, right? I would guess that the deviations from the LN model are even stronger when you show actual dynamic natural scenes - i.e., real movies. Here I would expect the rnn to have an even more profound effect, and potentially be much more informative.
en