Conduction Velocity · Node of Ranvier · MS Disease
🧠 Tier: HSC / Early UG · Axon Physiology
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§ 01
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Parameter 15.0
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T sim (ms)500
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§ 02
The Idea, Step by Step
▸ From a Slow Crawl to a Lightning Leap
START — The bucket brigade
Picture passing a bucket of water down a line of people. Hand-to-hand all the way down is slow. But if a few strong people stand far apart and toss the bucket between them, it races down the line. A myelinated nerve fibre works like the second line: the electrical signal effectively jumps from gap to gap instead of crawling along every patch of membrane. That speed is why your hand can leave a hot stove before you consciously feel the burn.
BUILD — Nodes, jumps, and a simple speed rule
Myelin is a fatty insulating wrap — built by Schwann cells in the body and oligodendrocytes in the brain — coiled around the axon. It leaves bare gaps, the nodes of Ranvier (about 1 mm apart), each crammed with sodium channels. The action potential is only rebuilt at the nodes; under each insulated stretch the current spreads quickly and passively to the next node. This leaping is called saltatory conduction (Latin saltare, to leap). A handy rule of thumb is $v \approx 6d$, where $d$ is the outer fibre diameter in micrometres and $v$ is the speed in metres per second. A 10 μm fibre gives $v \approx 6 \times 10 = 60$ m/s.
DEEPEN — Why insulation buys speed
The key quantity is the cable length constant $\lambda = \sqrt{R_m\,d/(4R_a)}$ — how far a passive voltage signal travels before it fades. Wrapping the axon about 150 times multiplies the membrane resistance $R_m$ and shrinks the membrane capacitance $C_m$, so $\lambda$ grows roughly tenfold and far less charge $Q = C_m V$ is needed to depolarise the next stretch. Once $\lambda$ exceeds the node spacing, one node's spike easily drives the next past threshold. Bare axons lack this trick, so their speed only creeps up as $v \propto \sqrt{d}$. In multiple sclerosis, myelin is stripped away, $\lambda$ collapses below the node spacing, and the signal can die between nodes — a conduction block.
TRY THIS — In the sim above
Press Play and step through the presets to compare regimes. Push the parameter sliders toward the well-myelinated extreme and watch how readily the signal carries; then drop them toward the demyelinated extreme to see conduction falter — the same failure that underlies MS symptoms. Finally, raise the input slider and notice how a stronger drive can rescue a marginal fibre, a hint of why temperature shifts (the Uhthoff phenomenon) can swing symptoms either way.
Schwann cells (PNS)/oligodendrocytes (CNS) wrap axon ~150 times. R_m,myelin = 150×R_m ≈ 3×10⁶ Ω·cm². C_m,myelin = C_m/150 ≈ 0.007 μF/cm². λ_myelin = 10× λ_bare. Charge needed to depolarise: Q = C_m V → 150× less charge needed per unit length.
STEP 2 — Saltatory Conduction
Nodes of Ranvier (1 μm wide, 1 mm spacing): 1000 Na⁺ channels/μm². AP at one node → current flows passively to next node (λ >> 1 mm). Depolarises next node above threshold → new AP. Jump time ≈ 0.05 ms → v ≈ 1 mm/0.05 ms = 20 m/s. 10 μm myelinated: 60 m/s vs 0.5 m/s unmyelinated.
STEP 3 — Multiple Sclerosis
Autoimmune myelin destruction: R_m drops 100×, C_m increases 100×, λ decreases 10×. AP from one node cannot reach next (λ < node spacing) → conduction block. Uhthoff phenomenon: symptoms worsen with fever (Na⁺ kinetics faster at high T → shorter AP → conduction block more likely). Treatment: 4-aminopyridine (K⁺ blocker) prolongs AP duration.
▸ Primary References
Hodgkin (1964) Conduction of the Nervous Impulse; Hille Ion Channels Ch.3
🧠 ConceptualWhat is the core mathematical insight of Myelination & Saltatory Conduction?▼
The Myelination & Saltatory Conduction framework provides a rigorous bridge between microscopic neural parameters and macroscopic observables. The key insight is that complex emergent behaviours — oscillations, memory, coding precision — can be derived from simple mathematical rules governing individual neurons and their connections. The quantitative framework enables testable predictions about circuit function from experimentally measurable quantities.
Key: Mathematical rigour connects single-neuron parameters to network behaviour. Every emergent property (oscillation, attractor, code) is derivable from the microscopic rules.
🌍 AppliedHow is Myelination & Saltatory Conduction used in neurotechnology?▼
The Myelination & Saltatory Conduction framework is directly applied in brain-computer interfaces and computational medicine. Mathematical models derived from these principles optimise stimulation parameters for DBS, predict drug effects on network dynamics, decode neural signals for BCI applications, and guide the design of neuromorphic hardware. The quantitative framework enables model-based optimisation rather than empirical trial-and-error approaches in clinical settings.
Key: Direct applications in BCI design, DBS parameter optimisation, pharmacological modelling, and neuromorphic chip architecture.
🔬 SimulationHow do I use the simulation for Myelination & Saltatory Conduction?▼
Configure the parameter sliders on the right panel, select a preset to set up a canonical example, then press Play. The Main View shows the primary mathematical relationship; the Phase Plane shows geometric structure; the Time Series shows temporal evolution; the Parameter Sweep reveals sensitivity to inputs. All displayed quantities correspond directly to terms in the equations in Section 2. Export the data as CSV for further analysis.
Key: Each tab maps to a specific aspect of the mathematics. Use Parameter Sweep to find bifurcation points and Phase Plane to understand stability.
💡 Non-ObviousWhat is counterintuitive about Myelination & Saltatory Conduction?▼
The most surprising result in Myelination & Saltatory Conduction research is that small parameter changes can produce qualitatively different dynamics (bifurcations). A system that is stable for one parameter value can suddenly oscillate, burst, or become chaotic with a tiny perturbation. This sensitivity to parameters is the hallmark of nonlinear systems and has profound implications: the same neural circuit can perform completely different computations depending on its neuromodulatory state, which shifts parameters near bifurcation boundaries.
Key: Bifurcations: tiny parameter changes → qualitative behavioural change. Neuromodulators exploit this by operating near bifurcation points to switch neural circuit function.
📐 ComputationalWhat numerical methods and pitfalls apply to Myelination & Saltatory Conduction?▼
The appropriate numerical method depends on stiffness: for near-spike dynamics (HH, AdEx) use RK4 with dt ≤ 0.025 ms or implicit methods; for mean-field equations (WC, Amari) use Euler or RK4 with dt ≤ 0.5–1 ms. Always verify accuracy by halving dt and checking solution convergence. The most common errors: wrong units (mixing mV, ms, nS), missing initial conditions (not setting w₀=b×v₀ in Izhikevich), and incorrect boundary conditions in Fokker-Planck or cable equation simulations.
Key: Verify stability by halving dt. Most errors: wrong units, incorrect initial conditions, violated model assumptions. Check the model validity range before trusting any result.
§ 04 Resources
Gerstner et al. — Neuronal Dynamics. Free: neuronaldynamics.epfl.ch
Dayan & Abbott — Theoretical Neuroscience, MIT Press, 2001
Izhikevich — Dynamical Systems in Neuroscience. Free: dynamicalsystems.org
Neuromatch Academy — neuromatch.io (free comp neuro tutorials)
§ 05
Misconceptions & Common Errors
Sub-block A — Conceptual
❌"The Myelination & Saltatory Conduction model is only theoretical with no experimental support."
✅The Myelination & Saltatory Conduction framework has been extensively validated. Key predictions have been confirmed in electrophysiology, imaging, and pharmacological experiments. Modern neuroscience uses these models as quantitative tools integrated with the experimental cycle — for prediction, interpretation, and guidance of experiments.
📖 Hodgkin (1964) Conduction of the Nervous Impulse; Hille Ion Channels Ch.3
Sub-block B — Numerical
❌Applying Myelination & Saltatory Conduction equations outside their range of validity (wrong N, dt, or approximation regime).
✅Every model has a validity regime. Always check: (1) Is N large enough for mean-field? (2) Is dt small enough (halve dt and verify solution converges)? (3) Are units consistent (mV, ms, nS throughout)? (4) Are initial conditions correct? When in doubt, compare to direct simulation of the full system.
🔍 Why: Violating assumptions gives quantitatively or qualitatively wrong results — and the error is often invisible without explicit validation.
§ 05 References
Hodgkin (1964) Conduction of the Nervous Impulse; Hille Ion Channels Ch.3
Gerstner et al. — Neuronal Dynamics, Cambridge, 2014