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  3. Organic Electrochemical Neurons: Nonlinear Tools For Complex Dynamics.
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  3. Organic Electrochemical Neurons: Nonlinear Tools For Complex Dynamics.

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Organic Electrochemical Neurons: Nonlinear Tools for Complex Dynamics.

Gonzalo Rivera-Sierra1, Roberto Fenollosa1, Juan Bisquert1

  • 1Universitat Politècnica de València-Consejo Superior de Investigaciones Científicas, Instituto de Tecnología Química, Camino de Vera, València 46022, Spain.

ACS Applied Electronic Materials
|February 9, 2026

View abstract on PubMed

Summary
This summary is machine-generated.

This study presents a new framework for designing artificial neurons using hybrid oscillator architectures. The research models organic electrochemical neurons, offering insights into oscillation generation for advanced bioelectronic applications.

Keywords:
Hopf bifurcationbioinspired circuitsneuromorphic electronicsnonlinear dynamicsorganic electrochemical transistor (OECT)organic mixed ionic–electronic conductors (OMIEC)

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Area of Science:

  • Neuroscience
  • Electronic Engineering
  • Dynamical Systems Theory

Background:

  • Hybrid oscillator architectures combining feedback and negative resistance oscillators are key for artificial neuron design.
  • Organic electrochemical transistors (OECTs) offer potential for bioelectronic devices due to their negative differential resistance properties.

Purpose of the Study:

  • To introduce a modeling and analysis framework for amplifier-assisted organic electrochemical neurons.
  • To leverage nonlinear dynamical systems theory for understanding hybrid oscillator circuits.

Main Methods:

  • Formulating the system as coupled differential equations for membrane voltage and internal state variables.
  • Utilizing nullclines, phase space, and bifurcation analysis to characterize dynamics.
  • Applying dynamic systems theory to analyze circuits with operational amplifiers and OECTs.

    • Main Results:

    • Identified conditions for self-sustained oscillations in amplifier-assisted organic electrochemical neurons.
    • Characterized the nonlinear dynamics governing oscillation generation.
    • Demonstrated the utility of dynamic systems theory in analyzing complex hybrid circuits.

    Conclusions:

    • The proposed framework provides a simplified yet rigorous model for analyzing hybrid oscillator circuits.
    • This approach reveals core mechanisms of oscillation generation, aiding in the design of artificial neurons.
    • The framework is generalizable for creating tunable, biologically inspired oscillatory systems for various applications.