Poster
in
Workshop: Memory in Artificial and Real Intelligence (MemARI)

Constructing compressed number lines of latent variables using a cognitive model of memory and deep neural networks

Sahaj Singh Maini · James Mochizuki-Freeman · Chirag Shankar Indi · Brandon Jacques · Per B Sederberg · Marc Howard · Zoran Tiganj


Abstract:

Humans use log-compressed number lines to represent different quantities, including elapsed time, traveled distance, numerosity, sound frequency, etc. Inspired by recent cognitive science and computational neuroscience work, we developed a neural network that learns to construct log-compressed number lines from a cognitive model of working memory. The network computes a discrete approximation of a real-domain Laplace transform using an RNN with analytically derived weights giving rise to a log-compressed timeline of the past. The network learns to extract latent variables from the input and uses them for global modulation of the recurrent weights turning a timeline into a number line over relevant dimensions. The number line representation greatly simplifies learning on a set of problems that require learning associations in different spaces - problems that humans can typically solve easily. This approach illustrates how combining deep learning with cognitive models can result in systems that learn to represent latent variables in a brain-like manner and exhibit human-like behavior manifested through Weber-Fechner law.

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