Living-Neuron-Based Autogenerator.

Area of Science:

• Neuroscience
• Computational Neuroscience
• Bioelectronics

Background:

• Integrating biological and artificial neural systems is crucial for advancing neuromorphic computing.
• Existing hybrid systems often face challenges in achieving seamless signal integration and control.

Purpose of the Study:

• To develop and demonstrate a novel closed-loop system for unifying biological and artificial neurons.
• To establish a bidirectional interface for controlling biological neuron signals using artificial components.

Main Methods:

• An electronic circuit based on the FitzHugh-Nagumo model was employed for artificial neuron simulation.
• The system utilized acute hippocampal mouse brain slices for housing biological neurons.
• Local field potentials from biological neurons modulated the electronic circuit's state, and vice versa.

Main Results:

• The hybrid system successfully integrated biological and artificial neurons into a unified oscillatory circuit.
• Living neuron activity triggered the FitzHugh-Nagumo circuit into oscillation.
• Spikes from the artificial circuit synchronized with biological neuron spikes, demonstrating effective control.

Conclusions:

• The developed hybrid electrobiological autogenerator effectively controls biological neuron signals.
• This system offers a promising platform for diverse neuromorphic applications requiring integrated neural processing.