Banino, Andrea; (2020) Investigating memory: an intersection of neuroscience and artificial intelligence. Doctoral thesis (Ph.D), UCL (University College London).

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Abstract

In recent years, deep neural networks have enjoyed tremendous successes in a variety of challenging tasks. Despite these breakthroughs, there remain key areas in which humans are still strikingly superior: the ability to learn in a one-shot fashion (episodic memory) and spatial navigation being two core examples. Fortuitously, these areas are topics in neuroscience that have deep theoretical and empirical foundations. Consequently, in this body of work we drew on this opportunity to develop neuroscience-inspired architectures that support navigation and episodic memory – and in so doing, we also provided new neuroscientific insights. Specifically we identified architectural constraints in neural network models that allowed the emergence of spatial representation that resemble the ones found in the mammalian brain (e.g. place cells, grid cells, head direction cells). Grid cells in particular are believed to provide multi-scale periodic representation that functions as a metric for coding space which is critical to plan direct trajectories to goals. To test this hypothesis we used our artificial agent to show that emergent grid-like representations furnish it with a Euclidean spatial metric and associated vector operations, providing a foundation for proficient navigation. As such, our results supported neuroscientific theories that see grid cells as critical for vector-based navigation. In a second line of work we focused on episodic memory and in particular on the role of the hippocampus in generalisation. We employed a classic associative inference task from the human neuroscience literature - the paired associative inference task (PAI) - to carefully probe the reasoning capacity of existing memory-augmented neural networks. Surprisingly, we found that current architectures struggle to reason over long distance associations. Consequently we developed a new memory architecture inspired by research on how the hippocampus supports generalisation. This new architecture was capable of solving the PAI tasks, as well as other challenging machine learning tasks. To sum up, in our work we showed a considerable potential for synergy between neuroscience and deep learning, whereby the latter can be used as a tool to validate theories from the former. At the same time we also showed that neuroscience can be used to inspire artificial architectures capable of solving hard memory tasks, where traditional methods have failed.

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