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Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks

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Neuromorphic function learning with carbon nanotube based synapses.

Karim Gacem¹, Jean-Marie Retrouvey, Djaafar Chabi

¹CEA, IRAMIS, Service de Physique de L’Etat Condensé (CNRS URA 2464), Laboratoire d’Electronique Moléculaire, Gif-sur-Yvette, France.

|September 4, 2013

Summary

This summary is machine-generated.

Carbon-nanotube memory devices act as artificial synapses in neuromorphic circuits, enabling function learning. These nanoscale synapses demonstrate robust learning capabilities despite variability, paving the way for defect-tolerant computing architectures.

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Area of Science:

Neuromorphic Engineering
Materials Science
Computer Science

Background:

Neuromorphic circuits leverage artificial synapses for defect-tolerant architectures.
Experimental demonstrations of function learning using non-CMOS nanoscale memory devices are scarce.

Purpose of the Study:

To demonstrate carbon-nanotube-based memory elements as artificial synapses for function learning in neuromorphic circuits.
To evaluate the potential for parallel learning of complex functions using these nanoscale synapses.

Main Methods:

Utilizing carbon-nanotube memory elements as artificial synapses.
Integrating these synapses with conventional neurons.
Applying a supervised learning algorithm for function training.

Main Results:

Successfully trained an ensemble of eight carbon-nanotube memory devices to perform multiple three-input linearly separable Boolean logic functions.
Demonstrated function learning capabilities despite inherent device-to-device variability.
Showcased the re-trainability of the same device ensemble for successive functions.

Conclusions:

Carbon-nanotube memory devices show significant promise as artificial synapses for functional learning in neuromorphic computing.
This approach offers a viable pathway towards building defect-tolerant and highly parallelizable computing systems.
The study represents a key experimental validation of nanoscale building blocks for advanced neural network functionalities.