Representing Fly Behavior with Recurrent Neural Networks

Behavior is a complex process that operates across many time and length

scales, in many different contexts, and differentially to a variety of external

and internal stimuli. The necessity to quantify behavior in a precise and

meaningful manner, however, is growing as the advent of new technologies -

optogenetics, connectomics, optical imaging techniques - in the world of

neuroscience has led to an explosion of assembling and analyzing large swaths

of neural data. Here we investigate recurrent neural networks (RNNs) as a

model of the underlying dynamics of Drosophila melanogaster and find the

behavioral representation it constructs similar to representations built from the

previously published results of postural time series. This is a markedly different

result from that of RNNs applied to rat models and we investigate the

implications.