A deep learning-inspired model of the hippocampus as storage device of the brain extended dataset
Alessandro Fontana

TL;DR
This paper proposes a neural network-inspired model of the hippocampus as a storage system for the brain's dataset, emphasizing its role in memory and feature learning, inspired by machine learning principles.
Contribution
It introduces a novel perspective that the hippocampus functions primarily as a dataset storage device, based on insights from neural network learning processes.
Findings
Hippocampus stores the brain's dataset for repeated learning.
High-level features are encoded in cortical neurons.
The model bridges neural network principles with biological memory architecture.
Abstract
The standard model of memory consolidation foresees that memories are initially recorded in the hippocampus, while features that capture higher-level generalisations of data are created in the cortex, where they are stored for a possibly indefinite period of time. Computer scientists have sought inspiration from nature to build machines that exhibit some of the remarkable properties present in biological systems. One of the results of this effort is represented by artificial neural networks, a class of algorithms that represent the state of the art in many artificial intelligence applications. In this work, we reverse the inspiration flow and use the experience obtained from neural networks to gain insight into the design of brain architecture and the functioning of memory. Our starting observation is that neural networks learn from data and need to be exposed to each data record many…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsMemory and Neural Mechanisms · Neuroscience and Neuropharmacology Research · Neural dynamics and brain function
