Neuron Structure and Action Potential

• Identify and label the structural components of a neuron
• Explain how resting membrane potential is maintained at -70 mV
• Describe the ionic events during an action potential (depolarisation → repolarisation → hyperpolarisation)
• Define threshold potential, all-or-nothing law, and refractory period
• Compare saltatory vs. continuous conduction and myelinated vs. unmyelinated fibres

Level 1 — Foundations

A neuron (nerve cell) is the structural and functional unit of the nervous system. It has three main regions:

• Cell body (soma): contains the nucleus, Nissl granules (rough ER for protein synthesis), and most organelles. Metabolic centre of the neuron.
• Dendrites: short, branched projections that receive signals and carry them towards the cell body (afferent to soma).
• Axon: single long projection that carries signals away from the cell body (efferent from soma). Ends in axon terminals (synaptic knobs).

The axon of many neurons is wrapped in a myelin sheath formed by Schwann cells (PNS) or oligodendrocytes (CNS). Myelin is a lipid-rich insulating layer. Gaps between adjacent Schwann cells are called nodes of Ranvier — the only places where ionic exchange occurs in myelinated fibres.

Types of neurons by function:

• Sensory (afferent) — carry impulses from receptors to CNS
• Motor (efferent) — carry impulses from CNS to effectors
• Interneurons (association) — connect sensory and motor neurons within CNS

Level 2 — JEE / NEET depth

Resting Membrane Potential (RMP) = −70 mV

The inside of the neuron is negative relative to outside. Maintained by:

1. Na⁺/K⁺ ATPase pump — actively pumps 3 Na⁺ out and 2 K⁺ in (electrogenic, net outward positive charge).
2. Selective permeability — at rest, membrane is ~100× more permeable to K⁺ than Na⁺; K⁺ leaks out down concentration gradient, leaving fixed anions (proteins) inside.
3. Concentration gradients: [K⁺] inside >> outside; [Na⁺] outside >> inside.

Action Potential — ionic sequence:

Threshold potential = −55 mV (15 mV depolarisation from rest). All-or-nothing law: if stimulus reaches threshold, a full action potential fires; sub-threshold stimuli produce only local graded potentials that decay. Refractory period: absolute refractory (no AP possible — Na⁺ channels inactivated) followed by relative refractory (larger stimulus can trigger AP).

Conduction velocity:

• Myelinated fibres: saltatory conduction — impulse "jumps" node to node; velocity 70–120 m/s (A fibres).
• Unmyelinated fibres: continuous conduction along entire membrane; velocity 0.5–2 m/s (C fibres).
• Larger axon diameter → faster conduction (less resistance).

Problem: At rest, the membrane potential is –70 mV. A stimulus opens voltage-gated
Na⁺ channels. Trace the potential change and explain what stops depolarisation.

Step 1 — Resting state:
  Na⁺/K⁺ ATPase maintains –70 mV; Na⁺ channels CLOSED; K⁺ leak channels open.

Step 2 — Stimulus reaches threshold (–55 mV):
  Voltage-gated Na⁺ channels open → Na⁺ floods IN (high [Na⁺] outside + positive
  outside both drive Na⁺ in).

Step 3 — Peak depolarisation (+40 mV):
  Na⁺ channels inactivate (inactivation gate closes) → Na⁺ entry STOPS.
  Voltage-gated K⁺ channels now open (they open slowly).

Step 4 — Repolarisation:
  K⁺ exits rapidly (high [K⁺] inside + membrane now positive inside) → potential
  falls back toward –70 mV.

Step 5 — Hyperpolarisation (–80 mV):
  K⁺ channels close slowly → slight overshoot below –70 mV.

Step 6 — Recovery:
  Na⁺/K⁺ ATPase pump restores concentration gradients → resting –70 mV restored.

Answer: Depolarisation is stopped by Na⁺ channel inactivation (inactivation gate),
NOT by the reversal of the driving force alone.

• Draw and label a multipolar neuron; identify Nissl bodies and nodes of Ranvier.
• Write the sequence of ionic events during an action potential with membrane potential values.
• Differentiate myelinated from unmyelinated nerve fibres (table: speed, Schwann cells, nodes of Ranvier, examples).
• State the all-or-nothing law and explain its significance.
• Name the ions involved in RMP and the mechanism that maintains it.

• Fig. 21.1: Structure of a neuron — soma, dendrites, axon, myelin sheath, nodes of Ranvier, axon terminals. Label all parts.
• Fig. 21.2: Transmission of nerve impulse — show ionic movement at node of Ranvier during saltatory conduction.
• Graph: Action potential vs. time curve showing depolarisation peak (+40 mV), repolarisation, and after-hyperpolarisation.

• What is the resting membrane potential of a neuron and what ion pump maintains it?
• Name the ion that enters the axon during depolarisation.
• Why is conduction in myelinated fibres faster than in unmyelinated fibres?
• What happens during the absolute refractory period?
• Stretch: If the Na⁺/K⁺ ATPase is blocked by ouabain, will the very next action potential fire? What will happen after many firings? Explain using ion gradients.

NCERT Chapter 21 link: Chapter 21 "Neural Control and Coordination" covers neuron structure, nerve impulse, synapse, and CNS/PNS organisation — the action potential section maps to pages 336–341.

Exam connections: NEET frequently asks numerical/value-based questions on RMP (−70 mV), threshold (−55 mV), and peak depolarisation (+40 mV). Expect one MCQ on saltatory conduction and one on refractory period every year.

Study strategy: Draw the action potential curve from memory daily for 3 days; annotate ion movements at each phase. Use flashcards for numerical values. Solve previous 10 years' NEET Qs on this topic.

• Use the platform-native neuron simulator to adjust stimulus intensity below and above threshold and observe all-or-nothing behaviour; vary myelin thickness and observe conduction velocity changes.
• Mirror / body / home activity: Use a long rubber tube to model saltatory conduction — mark nodes of Ranvier with tape, tap each node in sequence and compare to tapping every point (unmyelinated). Photograph and add to portfolio.
• Voice or text reflection with AI Mentor: Explain to a younger sibling why a neuron is like a "switch" (all-or-nothing) rather than a "dimmer" (graded), using a light-switch example from home.

• "Explain the resting membrane potential to a Class 6 student using a crowded room (Na⁺) versus an empty room (K⁺) analogy from a school or market setting."
• "What is one common mistake students make when answering 'what stops depolarisation?', and how would you catch yourself making it?"
• Stretch: "How does the principle of the action potential connect to nerve conduction disorders like multiple sclerosis, or to how pain-killers (local anaesthetics) work? Link this to a future career in medicine or biotech."

• Complete the core practice + one extension activity (photo, table, short reflection, or mini-project) for base XP + topic badge.
• 5-7 day streak or family discussion note = multiplier + visible artifact in parent/principal dashboard.
• Best real-world application stories (anonymised) featured on class or national leaderboard.

• Build a simple electrical circuit with a switch to model the all-or-nothing firing; measure voltage across resistor to represent membrane potential. Document with multimeter readings.
• Direct link to AI Mastery (neural network architectures are inspired by neurons), Health & Medicine (pharmacology of Na⁺ channel blockers), and Cyber Defenders (signal encryption analogy).
• Coding extension: Write a Python script that simulates action potential firing (binary: fires if input ≥ threshold, silent otherwise) and prints the membrane potential sequence.

This material emphasises experiential "learning by doing", competency (apply/create/analyse), vocational exposure, critical thinking, and multidisciplinary connections. Designed to feed live worlds, AI Mentor (with memory), gamification, robotics, parent analytics, and future skills — not just exam prep.

Portfolio Evidence Idea: Your photo/table/reflection/project + one sentence on "How this helps me in real life or a possible future path."

Open the Practice tab for aligned questions (easy/medium/hard + case-based) with full AI scaffolding.

See curriculum for cross-links and the full future-skills/robotics chapters.