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Spatial Memory in a Spiking Neural Network with Robot Embodiment.

Sergey A Lobov^1,2,3, Alexey I Zharinov¹, Valeri A Makarov^1,4

¹Neurotechnology Department, Lobachevsky State University of Nizhny Novgorod, 23 Gagarin Ave., 603950 Nizhny Novgorod, Russia.

Sensors (Basel, Switzerland)

|April 30, 2021

Summary

This study introduces a spiking neural network (SNN) that creates internal representations for spatial memory. The SNN enables robots to learn and adapt to changing environments, avoiding hazards effectively.

Keywords:

STDP cognitive maps learning neurorobotics spiking neural networks vector field of functional connections vector field of synaptic connections

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Area of Science:

Computational Neuroscience
Artificial Intelligence
Robotics

Background:

Cognitive maps and spatial memory are crucial for understanding brain function.
Spiking neural networks (SNNs) offer a biologically plausible model for brain processes.

Purpose of the Study:

To develop an SNN capable of generating internal environmental representations and implementing spatial memory.
To investigate the SNN's ability to control robotic behavior in dynamic environments.

Main Methods:

Utilized a non-specific SNN architecture shaped by Hebbian synaptic plasticity.
Embodied the SNN in a robot navigating an arena with safe and dangerous zones.
Employed a synaptic vector field approach to measure global network memory and learning curves.

Main Results:

The trained SNN effectively guided the robot to avoid dangerous areas.
The robot exhibited imperfect learning, visiting dangerous zones, which aids relearning in dynamic environments.
The SNN successfully remapped environmental hazards, preventing catastrophic interference and enabling adaptation.

Conclusions:

The developed SNN successfully models spatial memory and cognitive map formation.
The SNN demonstrates adaptive behavior and resilience to environmental changes, mimicking biological learning.
This approach offers a novel method for creating intelligent agents capable of learning and adapting in complex, dynamic worlds.