학술논문
A Bio-inspired Implementation of A Sparse-learning Spike-based Hippocampus Memory Model
Document Type
Periodical
Author
Source
IEEE Transactions on Emerging Topics in Computing IEEE Trans. Emerg. Topics Comput. Emerging Topics in Computing, IEEE Transactions on. PP(99):1-16
Subject
Language
ISSN
2168-6750
2376-4562
2376-4562
Abstract
The brain is capable of solving complex problems simply and efficiently, far surpassing modern computers. In this regard, neuromorphic engineering 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 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 hippocampal memory model with the ability to learn memories, recall them from a fragment of itself (cue) and even forget memories when trying to learn others with the same cue. This model has been implemented on SpiNNaker using Spiking Neural Networks, and a set of experiments were performed to demonstrate its correct operation. This work presents the first simulation implemented on a special-purpose hardware platform for Spiking Neural Networks of a fully functional bio-inspired spike-based hippocampus memory model, paving the road for the development of future more complex neuromorphic systems.