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ConsciousnessLearn · Explore · Joseph Anady

Potassium activation closing rate

The exponential β_n decreases as voltage rises and multiplies n in the gate’s closing term. At a fixed voltage, α_n and β_n together yield equilibrium n=α_n/(α_n+β_n) and relaxation time 1/(α_n+β_n). The expression is one component of the coupled membrane model.

Implementation mathematicsSource recovered; runtime not executed
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gh-eq-29152d4536eec1d5source-comment-equation
βn(V)=0.125∗exp(−(V+65)/80)β_n(V) = 0.125*exp(-(V+65)/80)

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The exponential β_n decreases as voltage rises and multiplies n in the gate’s closing term. At a fixed voltage, α_n and β_n together yield equilibrium n=α_n/(α_n+β_n) and relaxation time 1/(α_n+β_n). The expression is one component of the coupled membrane model.

Terms & definitions

TermMeaning in this source
V

Membrane voltage

beta_n

Potassium activation closing rate

How it works

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Original context

Read the surrounding source passage
    // α_n(V) = 0.01(V+55)/(1-exp(-(V+55)/10))
    float alpha_n(float V_) const {
        float x = V_ + 55.0f;
        if (std::abs(x) < 1e-6f) return 0.1f;
        return 0.01f * x / (1.0f - std::exp(-x / 10.0f));
    }

    // β_n(V) = 0.125*exp(-(V+65)/80)
    float beta_n(float V_) const {
        return 0.125f * std::exp(-(V_ + 65.0f) / 80.0f);
    }

    // Ionic currents
    float I_Na() const { return g_Na * m*m*m * h * (V - E_Na); }
    float I_K() const { return g_K * n*n*n*n * (V - E_K); }
    float I_L() const { return g_L * (V - E_L); }

    // Time step using Euler method
Exact original notation
β_n(V) = 0.125*exp(-(V+65)/80)

Related builds

Sources & provenance

01
C++ neural model components
Lines 146–146 · 33aa84f2aa61
02
C++ neural model components
Lines 146–146 · 33aa84f2aa61

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