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We’ll now ground this in a fun application: We’ll train RNN character-level language models. This will then allow us to generate new text one character at a time. That is, we’ll give the RNN a huge chunk of text and ask it to model the probability distribution of the next character in the sequence given a sequence of previous characters. Okay, so we have an idea about what RNNs are, why they are super exciting, and how they work.

We developed a method for automatically classifying each frame of a video into a set of user-defined behaviors. Our open-source software, called DeepEthogram, provides the code and user interface necessary to label videos and train DeepEthogram models. Importantly, by design, CNNs ignore absolute spatial location and thus are able to identify behaviors even when animals are in different locations and orientations within a behavioral arena (Figure 3—figure supplement 12). We show that modern computer vision methods for action detection based on pretrained deep neural networks can be readily applied to animal behavior datasets. DeepEthogram performed well on multiple datasets and generalized across videos and animals, even for identifying rare behaviors. DeepEthogram joins a growing community of open-source computer vision applications for biomedical research (Datta et al., 2019; Anderson and Perona, 2014; Egnor and Branson, 2016). We anticipate this software package will save researchers great amounts of time, will lead to more reproducible results by eliminating inter-researcher variability, and will enable experiments that may otherwise not be possible by increasing the number of experiments a lab can reasonably perform or the number of behaviors that can be investigated.

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