# Replicating associative learning of rodents with a neuromorphic robot in an open-field arena

## TL;DR

A neuromorphic robot successfully replicates rodent-like associative learning in a real-world environment using biologically inspired neural models.

## Contribution

A compact, biologically inspired neuromorphic robot with 19 spiking neurons demonstrates rodent-like associative learning without labeled data.

## Key findings

- The robot learned to associate visual cues with spatial outcomes using Hebbian plasticity.
- Functional learning behavior was confirmed across multiple trials through sensorimotor interactions.
- The model provides principles for synaptic weight and threshold design in neuromorphic systems.

## Abstract

This study emulates associative learning in rodents by using a neuromorphic robot navigating an open-field arena. The goal is to investigate how biologically inspired neural models can reproduce animal-like learning behaviors in real-world robotic systems. We constructed a neuromorphic robot by deploying computational models of spatial and sensory neurons onto a mobile platform. Different coding schemes—rate coding for vibration signals and population coding for visual signals—were implemented. The associative learning model employs 19 spiking neurons and follows Hebbian plasticity principles to associate visual cues with favorable or unfavorable locations. Our robot successfully replicated classical rodent associative learning behavior by memorizing causal relationships between environmental cues and spatial outcomes. The robot’s self-learning capability emerged from repeated exposure and synaptic weight adaptation, without the need for labeled training data. Experiments confirmed functional learning behavior across multiple trials. This work provides a novel embodied platform for memory and learning research beyond traditional animal models. By embedding biologically inspired learning mechanisms into a real robot, we demonstrate how spatial memory can be formed and expressed through sensorimotor interactions. The model’s compact structure (19 neurons) illustrates a minimal yet functional learning network, and the study outlines principles for synaptic weight and threshold design, guiding future development of more complex neuromorphic systems.