Emulating the Hippocampus: Hyperdimensional Memory Encoding and Retrieval with PyTorch
Introduction
The hippocampus is a vital structure within the human brain which is integral to memory formation, consolidation and retrieval. Emulating its functions in artificial systems has been a longstanding objective in both neuroscience and artificial intelligence (AI).
This article explores my approach leveraging Hyperdimensional Computing (HDC) to mimic hippocampal functions using PyTorch, a widely adopted deep learning framework. By encoding the MNIST dataset — comprising handwritten digits — into high-dimensional hypervectors, storing them in a memory bank and implementing retrieval & consolidation mechanisms, this model provides valuable insights into biologically inspired memory systems and their applications in machine learning.
Hyperdimensional Computing: A Paradigm Shift
Hyperdimensional Computing (HDC), also known as Vector Symbolic Architectures (VSA), represents information using high-dimensional vectors, typically ranging from 1,000 to 10,000 dimensions. This approach diverges from traditional low-dimensional computing paradigms, offering unique advantages inspired by the brain’s cognitive processes.
