# Bio-inspired spike-based Hippocampus and Posterior Parietal Cortex models for robot navigation and environment pseudo-mapping

arXiv: https://arxiv.org/abs/2305.12892 · HTML: https://mlsanity.com/paper/2305.12892/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 50 references that are in the map.
- First posted on arXiv 22 May 2023.

## Builds on (most cited first, 7 of 7)

- [Deep Learning in Spiking Neural Networks](https://mlsanity.com/paper/1804.08150/) (2018)
- [Spiking Neural Networks and Online Learning: An Overview and Perspectives](https://mlsanity.com/paper/1908.08019/) (2019)
- [Spiking Neural Network on Neuromorphic Hardware for Energy-Efficient Unidimensional SLAM](https://mlsanity.com/paper/1903.02504/) (2019)
- [Gridbot: An autonomous robot controlled by a Spiking Neural Network mimicking the brain's navigational system](https://mlsanity.com/paper/1807.02155/) (2018)
- [Differential Mapping Spiking Neural Network for Sensor-Based Robot Control](https://mlsanity.com/paper/2005.10017/) (2020)
- [A bio-inspired implementation of a sparse-learning spike-based hippocampus memory model](https://mlsanity.com/paper/2206.04924/) (2022)
- [A Neural Network Model of the Entorhinal Cortex and Hippocampus for Event-order Memory Processing](https://mlsanity.com/paper/2111.10535/) (2021)

## Built on by (most cited first, 1 of 1)

- [Bio-inspired computational memory model of the Hippocampus: an approach to a neuromorphic spike-based Content-Addressable Memory](https://mlsanity.com/paper/2310.05868/) (2023)

## Abstract (arXiv)

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.