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Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks
Published on: March 2, 2015
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SpikingLab: modelling agents controlled by Spiking Neural Networks in Netlogo
Cristian Jimenez-Romero1, Jeffrey Johnson1
1Design-Complexity Group, The Open University, Milton Keynes, MK7 6AA UK.
Neural Computing & Applications
|December 8, 2017
Summary
This study introduces a simplified Spiking Neural Network (SNN) simulation tool in Netlogo, making artificial neural circuit development accessible for robotics. It demonstrates control of an artificial insect agent, easing research for students and novices.
Area of Science:
- Computational Neuroscience
- Artificial Intelligence
- Robotics
Background:
- Spiking Neural Networks (SNNs) are crucial for simulating neuronal dynamics.
- Existing SNN tools offer sophisticated models but pose integration challenges with robotic simulations.
- Implementing artificial neural circuits for robot control involves complex, time-consuming steps.
Purpose of the Study:
- To present an accessible tool for simulating simple SNN circuits.
- To facilitate the integration of SNNs with multi-agent simulation environments.
- To simplify the process of developing neural controllers for agents and robots.
Main Methods:
- Developed a novel SNN simulation engine within the Netlogo environment.
- Utilized a simplified integrate-and-fire (I&F) model for neuronal dynamics.
- Implemented Spiking-Dependent Plasticity (STDP) learning and synaptic delays.
Main Results:
- Successfully simulated a functional SNN model in Netlogo.
- Demonstrated the engine's capabilities by controlling an artificial insect agent.
- Validated the tool's effectiveness in representing neuronal dynamics, learning, and synaptic delay.
Conclusions:
- The Netlogo-based SNN engine simplifies the creation and simulation of artificial neural circuits.
- This tool lowers the barrier for students and researchers to explore SNNs in agent-based systems.
- The approach facilitates the development of neural controllers for robotic applications.
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