Bio-inspired computational memory model of the Hippocampus: an approach
  to a neuromorphic spike-based Content-Addressable Memory
Daniel Casanueva-Morato Alvaro Ayuso-Martinez Juan P. Dominguez-Morales Angel Jimenez-Fernandez Gabriel Jimenez-Moreno
The brain has computational capabilities that surpass those of modern
systems, being able to solve complex problems efficiently in a simple way.
Neuromorphic engineering aims to mimic biology in order to develop new systems
capable of incorporating such capabilities. Bio-inspired learning systems
continue to be a challenge that must be solved, and much work needs to be done
in this regard. Among all brain regions, the hippocampus stands out as an
autoassociative short-term memory with the capacity to learn and recall
memories from any fragment of them. These characteristics make the hippocampus
an ideal candidate for developing bio-inspired learning systems that, in
addition, resemble content-addressable memories. Therefore, in this work we
propose a bio-inspired spiking content-addressable memory model based on the
CA3 region of the hippocampus with the ability to learn, forget and recall
memories, both orthogonal and non-orthogonal, from any fragment of them. The
model was implemented on the SpiNNaker hardware platform using Spiking Neural
Networks. A set of experiments based on functional, stress and applicability
tests were performed to demonstrate its correct functioning. This work presents
the first hardware implementation of a fully-functional bio-inspired spiking
hippocampal content-addressable memory model, paving the way for the
development of future more complex neuromorphic systems.