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Designing and Implementing Nervous System Simulations on LEGO Robots

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Braitenberg Vehicles as Developmental Neurosimulation.

Stefan Dvoretskii¹, Ziyi Gong², Ankit Gupta³

¹Technical University of Munich.

Artificial Life

|July 26, 2022

Summary

This summary is machine-generated.

This study models artificial organism development, moving beyond the blank slate approach. Developmental embodied agents exhibit complex behaviors, from simple responses to collective motion, advancing artificial brain networks.

Keywords:

Braitenberg vehicles Development artificial intelligence embodied cognition

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Area of Science:

Neurobiology
Artificial Intelligence
Behavioral Science

Background:

Traditional artificial and biological neural network models often treat the mature brain as a blank slate, neglecting developmental learning.
Biological development and learning are crucial for understanding complex behaviors.

Purpose of the Study:

To model the development of artificial organisms capable of exhibiting complex behaviors.
To explore developmental embodied agents and their implementation.

Main Methods:

Introduced three distinct approaches for developmental embodied agents: a Braitenberg vehicle-genetic algorithm hybrid, a multisensory Hebbian learning model, and multi-agent systems.
Developed developmental Braitenberg vehicles (dBVs) to simulate emergent behaviors.

Main Results:

The developed dBVs demonstrated a range of behaviors, including stimulus responses and collective motion.
Showcased use cases like optimized spatial cognition, linking behavioral and neural models, and cumulative classification.

Conclusions:

The developmental neurosimulation approach offers a novel perspective on artificial brain networks, embodied cognition, feedback, and emergence.
Future applications in developmental neurosimulation are promising.