A bio-inspired implementation of a sparse-learning spike-based
hippocampus memory model
- URL: http://arxiv.org/abs/2206.04924v1
- Date: Fri, 10 Jun 2022 07:48:29 GMT
- Title: A bio-inspired implementation of a sparse-learning spike-based
hippocampus memory model
- Authors: Daniel Casanueva-Morato, Alvaro Ayuso-Martinez, Juan P.
Dominguez-Morales, Angel Jimenez-Fernandez, Gabriel Jimenez-Moreno
- Abstract summary: We propose a novel bio-inspired memory model based on the hippocampus.
It can learn memories, recall them from a cue and even forget memories when trying to learn others with the same cue.
This work presents the first hardware implementation of a fully functional bio-inspired spike-based hippocampus memory model.
- Score: 0.0
- License: http://creativecommons.org/licenses/by-nc-sa/4.0/
- Abstract: The nervous system, more specifically, the brain, is capable of solving
complex problems simply and efficiently, far surpassing modern computers. In
this regard, neuromorphic engineering is a research field that focuses on
mimicking the basic principles that govern the brain in order to develop
systems that achieve such computational capabilities. Within this field,
bio-inspired learning and memory systems are still a challenge to be solved,
and this is where the hippocampus is involved. It is the region of the brain
that acts as a short-term memory, allowing the learning and unstructured and
rapid storage of information from all the sensory nuclei of the cerebral cortex
and its subsequent recall. In this work, we propose a novel bio-inspired memory
model based on the hippocampus with the ability to learn memories, recall them
from a cue (a part of the memory associated with the rest of the content) and
even forget memories when trying to learn others with the same cue. This model
has been implemented on the SpiNNaker hardware platform using Spiking Neural
Networks, and a set of experiments and tests were performed to demonstrate its
correct and expected operation. The proposed spike-based memory model generates
spikes only when it receives an input, being energy efficient, and it needs 7
timesteps for the learning step and 6 timesteps for recalling a
previously-stored memory. This work presents the first hardware implementation
of a fully functional bio-inspired spike-based hippocampus memory model, paving
the road for the development of future more complex neuromorphic systems.
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