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Dynamics of a functional neural circuit without capacitor embedding.

Junen Jia1, Guodong Ren1, Chunni Wang1

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This study introduces a novel capacitorless neural circuit using memristors and Josephson junctions for multi-physics sensing. The circuit demonstrates adaptable neural firing patterns and stochastic resonance, offering insights for neuromorphic devices.

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Area of Science:

  • Neuromorphic Engineering
  • Nonlinear Dynamics
  • Sensor Technology

Background:

  • Traditional neural circuits rely on capacitors for energy storage, limiting design flexibility.
  • Developing integrated systems capable of sensing multiple physical parameters is a key challenge in neuromorphic engineering.

Purpose of the Study:

  • To propose a novel functional neural circuit model without capacitors.
  • To achieve multi-physics field sensing capabilities for electromagnetic fields and temperature.
  • To investigate the dynamics and firing patterns of the proposed capacitorless neural circuit.

Main Methods:

  • A novel circuit model replacing capacitors with charge-controlled memristors (CCMs) and Josephson junctions coupled with thermistors.
  • Derivation of a dimensionless theoretical model using Kirchhoff's law and Helmholtz's theorem.
  • Systematic investigation of neural firing patterns modulated by critical current, ion channel switching rate, and ambient temperature.

Main Results:

  • The circuit exhibits intermittent switching between periodic, bursting, and chaotic firing modes.
  • Distinct output signal characteristics were observed: chaotic states (high amplitude, low frequency) and periodic states (low amplitude, high frequency).
  • The model demonstrates stochastic resonance, with temperature lowering the noise intensity threshold for its induction.

Conclusions:

  • The capacitorless neural circuit is biologically plausible and functionally reliable.
  • The findings provide novel design insights for highly integrated neuromorphic devices and intelligent sensor systems.
  • This work advances the development of advanced sensing and information processing systems.