A computational approach to a neuromorphic sequential memory bio-inspired on the Hippocampus and Entorhinal Cortex formation

The brain is considered one of the most powerful and efficient machines in existence. This is why neuromorphic engineering is trying to mimic biology to develop new systems that incorporate these superior capabilities. Within this field, bio-inspired learning and memory systems are still a challenge to be solved, and this is where the hippocampus and entorhinal cortex is involved. This brain formation acts as a short-term memory capable of learning by self-association of memories from different sources of information. In this work, we propose a fully functional bio-inspired spike-based hippocampus and entorhinal cortex sequential memory system capable of learning memories, recalling individual and sequential memories from a single fragment, and forgetting them. This model has been implemented on SpiNNaker using Spiking Neural Networks, and a set of experiments were performed to demonstrate its correct operation. This model will pave the road for the development of future more complex neuromorphic systems with a large amount of applicability.