Implementing engrams from a machine learning perspective: matching for prediction
This work addresses the challenge of bridging neuroscience and machine learning to explore memory implementation, but it is incremental as it builds on existing autoencoder techniques without presenting new empirical results.
The authors tackled the problem of implementing engrams as memory structures by proposing a computer system using neural networks, specifically autoencoders with latent neural spaces for compressed information storage and retrieval, as a step towards predictive learning.
Despite evidence for the existence of engrams as memory support structures in our brains, there is no consensus framework in neuroscience as to what their physical implementation might be. Here we propose how we might design a computer system to implement engrams using neural networks, with the main aim of exploring new ideas using machine learning techniques, guided by challenges in neuroscience. Building on autoencoders, we propose latent neural spaces as indexes for storing and retrieving information in a compressed format. We consider this technique as a first step towards predictive learning: autoencoders are designed to compare reconstructed information with the original information received, providing a kind of predictive ability, which is an attractive evolutionary argument. We then consider how different states in latent neural spaces corresponding to different types of sensory input could be linked by synchronous activation, providing the basis for a sparse implementation of memory using concept neurons. Finally, we list some of the challenges and questions that link neuroscience and data science and that could have implications for both fields, and conclude that a more interdisciplinary approach is needed, as many scientists have already suggested.