AI Bibliography
AI Bibliography |
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Song, Y. (2021). Predictive coding inspires effective alternatives to backpropagation. Unpublished PhD thesis, University of Oxford. |
| Resource type: Thesis/Dissertation BibTeX citation key: Song2021 View all bibliographic details  |
Categories: Artificial Intelligence, Cognitive Science, Computer Science, Decision Theory, General, Mathematics, Neuroscience Subcategories: Decision making, Forecasting, Human decisionmaking, Human learning, Machine learning, Neural nets, Neurosymbolic Creators: Song Publisher: University of Oxford Collection: |
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| Abstract |
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The brain has developed a sophisticated hierarchical structure where information is processed across multiple layers (Van Essen et al. 1992), which is critical for forming abstraction from raw information. Inspired by this, modern machine learning is largely built on deep artificial neural networks (LeCun, Bengio, et al. 2015), where neurons are organized in layers, and synaptic weights connect neurons of different layers. One important piece of learning is to correct errors in one’s predictions, and the hierarchical structure requires spreading the errors in one’s predictions across multiple layers and neurons — creating the core challenge of learning known as “credit assignment” (Lillicrap, Santoro, et al. 2020). How the brain solves credit assignment is a key question to understand learning and for creating artificial learning machines, and many recent studies (Lillicrap, Santoro, et al. 2020; Richards, Lillicrap, et al. 2019; Whittington and Bogacz 2019; Zipser and Andersen 1988; Singer et al. 2018; Whittington, T. H. Muller, et al. 2020; Banino et al. 2018) approached it from the perspective of backpropagation (Werbos 1974; Rumelhart et al. 1985; Parker 1985; LeCun, Bengio, et al. 2015). However, it has been questioned whether it is possible for the brain to implement backpropagation exactly (Crick 1989; Grossberg 1987), and learning in the brain seems to be more efficient than backpropagation in several other critical aspects (Tsividis et al. 2017). On the other hand, neuroscience researchers have developed many biologically plausible models to solve the credit assignment problem (Rao and Ballard 1999; Whittington and Bogacz 2017; Whittington and Bogacz 2019; Lillicrap, Santoro, et al. 2020). However, biologically plausible models are still behind backpropagation in terms of generalization and efficiency. The thesis is inspired by this mismatch: the brain mseems to be more advanced than backpropagation, while biologically plausible models learn no better than machine learning models. Thus, the overall goal of the thesis is to demonstrate biologically plausible models can inspire alternative and potentially better solutions for credit assignment than backpropagation, and we take inspiration mainly from predictive coding (PC). Specifically, I first propose a variant of PC that is equivalent to backpropagation. I then propose that the original PC has advantages over backpropagation, implementing a fundamentally different principle from backpropagation, which I call “prospective configuration”. Inspired by the above two findings, I finally propose another variant of PC that is potentially more efficient than backpropagation and has a similar generalization quality as backpropagation.
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