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Convolutional neural networks (CNN) are an emerging technique in modeling neural circuits and have been shown to converge to biologically plausible functionality in cortical circuits via task-optimization. This functionality has not been observed in CNN models of retinal circuits via task-optimization. We sought to observe this convergence in retinal circuits by designing a biologically inspired CNN model of a motion-detection retinal circuit and optimizing it to solve a motion-classification task. The learned weights and parameters indicated that the CNN converged to direction-sensitive ganglion and amacrine cells, cell types that have been observed in biology, and provided evidence that task-optimization is a fair method of building retinal models. The analysis used to understand the functionality of our CNN also indicates that biologically constrained deep learning models are easier to reason about their underlying mechanisms than traditional deep learning models.
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Emergence of direction-selective retinal cell types in task-optimized deep learning models
Convolutional neural networks (CNNs), a class of deep learning models, have experienced recent success in modeling sensory cortices and retinal circuits through optimizing performance on machine learning tasks, otherwise known as task optimization. Previous research has shown task-optimized CNNs to be capable of providing explanations as to why the retina efficiently encodes natural stimuli and how certain retinal cell types are involved in efficient encoding. In our work, we sought to use task-optimized CNNs as a means of explaining computational mechanisms responsible for motion-selective retinal circuits. We designed a biologically constrained CNN and optimized its performance on a motion-classification task. We drew inspiration from psychophysics, deep learning, and systems neuroscience literature to develop a toolbox of methods to reverse engineer the computational mechanisms learned in our model. Through reverse engineering our model, we proposed a computational mechanism in which direction-selective ganglion cells and starburst amacrine cells, both experimentally observed retinal cell types, emerge in our model to discriminate among moving stimuli. This emergence suggests that direction-selective circuits in the retina are ecologically designed to robustly discriminate among moving stimuli. Our results and methods also provide a framework for how to build more interpretable deep learning models and how to understand them.
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- PAR ID:
- 10324411
- Date Published:
- Journal Name:
- Journal of computational biology
- Volume:
- 29
- Issue:
- 4
- ISSN:
- 1066-5277
- Page Range / eLocation ID:
- 370-381
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Convolutional neural networks (CNN) are an emerging technique in modeling neural circuits and have been shown to converge to biologically plausible functionality in cortical circuits via task-optimization. This functionality has not been observed in CNN models of retinal circuits via task-optimization. We sought to observe this convergence in retinal circuits by designing a biologically inspired CNN model of a motion-detection retinal circuit and optimizing it to solve a motion-classification task. The learned weights and parameters indicated that the CNN converged to direction-sensitive ganglion and amacrine cells, cell types that have been observed in biology, and provided evidence that task-optimization is a fair method of building retinal models. The analysis used to understand the functionality of our CNN also indicates that biologically constrained deep learning models are easier to reason about their underlying mechanisms than traditional deep learning models.more » « less
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