What is it about?
The work is about modeling spatial cells in Hippocampal formation by firstly understanding the theoretical basis of getting grid cells from principal component analysis (PCA), which turned out to be a special form of Bessel function. Then applying the autoencoder model, which functions the same as the PCA model but gives the flexibility to integrate the vision which we get from the virtual environments and model other experimental studies.
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Why is it important?
This research is important as it paves the way to develop a comprehensive model of the hippocampal formation. This is because of the deep learning technique used in the model that provides the flexibility to do multi-sensory fusion. This technique can also be used in task-based training, and hidden neuron responses can be studied, which should mimic the responses of neurons seen in an experimental study.
Read the Original
This page is a summary of: An integrated deep learning‐based model of spatial cells that combines self‐motion with sensory information, Hippocampus, August 2022, Wiley, DOI: 10.1002/hipo.23461.
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