Representing Behavior with Recurrent Neural Networks Público
Saran, Ishan (Spring 2020)
Abstract
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.
Table of Contents
Section 1 - Introduction 1
1.1 Defining the problem 1
1.2 Traditional approaches to quantifying behavior 1
1.3 Stereotyped behaviors and modern approaches 2
1.4 Recurrent Neural Networks 3
1.5 RNNs to represent behavior 6
Section 2 - Data 7
2.1 Fly recording experiments 8
2.2 Pose estimation with LEAP 8
Section 3 - Methods to fit data 8
3.1 Data pre-processing 8
3.1.1 Converting to joint angle time series 8
3.1.2 Median-filtering the data 10
3.1.3 Splitting the data into train-validation sets 11
3.1.4 Standardizing the data 11
3.2 Building a dynamical model: feeding the data into an RNN 13
3.2.1 RNN Hyperparameters 13
3.2.2 1-layer RNN hyperparameters 14
3.2.3 2-layer RNN hyperparameters 14
3.2.4 3-layer RNN hyperparameters 14
Section 3 - Methods to fit data
3.3 Building a behavioral representation 14
3.3.1 Extracting hidden states from RNN 14
3.3.2 PCA on hidden states of RNN 15
3.3.3 Wavelet transform of principle components 16
3.3.4 Creating a behavior map with t-SNE 17
3.4 Identifying stereotyped behaviors 17
3.4.1 Peak-finding with Gaussian smoothing 17
3.4.2 Watershed transformation 18
3.4.3 Composite movies 19
Section 4 - Results 21
4.1 Model errors 21
4.2 Joint angle behavior map 22
4.3 RNN-generated behavior maps 24
4.3.1 1-layer RNN 24
4.3.2 “Glitch” regions 25
4.3.3 Comparing different RNN architectures 26
4.4 Closed-loop networks 29
Section 5 - Discussion 31
5.1 RNNs on flies versus rats 31
5.2 Future directions 33
Section 6 - Conclusions 33
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