Bio-inspired spike-based Hippocampus and Posterior Parietal Cortex
models for robot navigation and environment pseudo-mapping
- URL: http://arxiv.org/abs/2305.12892v1
- Date: Mon, 22 May 2023 10:20:34 GMT
- Title: Bio-inspired spike-based Hippocampus and Posterior Parietal Cortex
models for robot navigation and environment pseudo-mapping
- Authors: Daniel Casanueva-Morato, Alvaro Ayuso-Martinez, Juan P.
Dominguez-Morales, Angel Jimenez-Fernandez, Gabriel Jimenez-Moreno, Fernando
Perez-Pena
- Abstract summary: This work proposes a spike-based robotic navigation and environment pseudomapping system.
The hippocampus is in charge of maintaining a representation of an environment state map, and the PPC is in charge of local decision-making.
This is the first implementation of an environment pseudo-mapping system with dynamic learning based on a bio-inspired hippocampal memory.
- Score: 52.77024349608834
- License: http://creativecommons.org/licenses/by-nc-sa/4.0/
- Abstract: The brain has a great capacity for computation and efficient resolution of
complex problems, far surpassing modern computers. Neuromorphic engineering
seeks to mimic the basic principles of the brain to develop systems capable of
achieving such capabilities. In the neuromorphic field, navigation systems are
of great interest due to their potential applicability to robotics, although
these systems are still a challenge to be solved. This work proposes a
spike-based robotic navigation and environment pseudomapping system formed by a
bio-inspired hippocampal memory model connected to a Posterior Parietal Cortex
model. The hippocampus is in charge of maintaining a representation of an
environment state map, and the PPC is in charge of local decision-making. This
system was implemented on the SpiNNaker hardware platform using Spiking Neural
Networks. A set of real-time experiments was applied to demonstrate the correct
functioning of the system in virtual and physical environments on a robotic
platform. The system is able to navigate through the environment to reach a
goal position starting from an initial position, avoiding obstacles and mapping
the environment. To the best of the authors knowledge, this is the first
implementation of an environment pseudo-mapping system with dynamic learning
based on a bio-inspired hippocampal memory.
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