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A Josephson junction-coupled neuron with double capacitive membranes.

Feifei Yang1, Jun Ma2, Guodong Ren3

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|December 7, 2023
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This study introduces a novel neuron model incorporating a Josephson junction (JJ) to simulate magnetic field effects on neural activity. The model highlights energy dynamics and coherence resonance for realistic neural firing patterns.

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Area of Science:

  • Neuroscience
  • Physics
  • Computational Biology

Background:

  • Neural activity is influenced by electromagnetic fields and electrical stimuli, affecting membrane potential.
  • Existing neuron models may not fully capture distinct physical characteristics relevant to energy level shifts during neural activity.

Purpose of the Study:

  • To develop a novel neuron model that incorporates physical properties like magnetic field sensitivity and membrane capacitance.
  • To investigate the relationship between firing modes, energy levels, and the impact of external electromagnetic fields.

Main Methods:

  • A Josephson junction (JJ) was integrated into a neural circuit to assess magnetic field effects.
  • A double capacitor model with a linear resistor mimicked the cell membrane's capacitive properties.
  • Equivalent Hamiltonian energy was calculated, and noisy disturbances were applied to explore coherence resonance.

Main Results:

  • The new model effectively simulates the influence of external magnetic fields on neural activity.
  • Calculated Hamiltonian energy demonstrated a correlation between firing modes and energy levels.
  • Coherence resonance was observed under noisy excitation, stabilizing neural firing regularity.

Conclusions:

  • The developed neuron model accurately represents the biophysical properties of the cell membrane, including capacitive fields and energy exchange.
  • This model provides insights into the energy mechanisms underlying neural activities, particularly continuous firing.
  • The Josephson junction component enables the detection of subtle magnetic field variations, crucial for understanding neural signal processing.