Memory Networks as Neurocomputational Models of Cognitive Functions

Date

2024-7-16

Author

ALTINUÇ, Sinan Onur

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This thesis develops a cognitive computational framework for exploring memory networks as neurocomputational models of syntax and rule learning, with a particular focus on Neural Turing Machines (NTMs). It contrasts NTMs with recurrent neural networks such as long short-term memory (LSTM) and Legendre memory unit (LMU) architectures. By using artificial grammars to evaluate syntactic processing capabilities and specifically assessing the rule-learning and generalization abilities of these neural architectures, it studies the role of memory mechanisms in cognitive models, particularly the role of external memory. The experiments involve training neural networks on various types of grammar, including regular, context-free grammars (CFGs) and mildly context-sensitive grammars (MCFGs), with a focus on hierarchical structures and long-distance dependencies. Comparative analysis in the thesis shows that unlike regular recurrent networks, neural networks with external memory structures such as NTM were able to learn and generalize CFGs beyond training data by learning the rules. Supported also by meta-analysis, this thesis shows that augmented memory networks can generalize grammars with MCSG properties with a few exceptions. This capacity of the memory networks is compared with linguistic, and neuroscientific studies to understand the structural biases that might be required for these tasks and their potential neural correlates in the human brain. This thesis suggests that the ability to represent and store explicit representations in an external memory might be a key factor for rule-based symbolic operations on neural networks with implications for neuroscience and AI.

Subject Keywords

Memory Networks, Rule Learning, Generalization, Computational Models of Mind, Cognitive Models, Working Memory

URI

https://hdl.handle.net/11511/110953

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Graduate School of Informatics, Thesis