Part 2: Neurophysiology & Action Potentials

Basic Neural Function

The nervous system is the body's command-and-control network — a breathtakingly complex signaling system comprising roughly 86 billion neurons and an even greater number of supporting glial cells. Every sensation you experience, every thought you think, every movement you make, and every organ your body regulates depends on the electrical and chemical signals coursing through this living circuitry.

In Part 1, we established that homeostatic control loops require sensors, integrating centers, and effectors connected by afferent and efferent pathways. The nervous system is those pathways — and much more. Understanding how individual neurons generate and transmit signals is the foundation for understanding everything from a simple knee-jerk reflex to consciousness itself.

Neuron Structure & Types

A neuron has four functional regions, each specialized for a different role in signal processing:

The axon hillock — where the axon emerges from the soma — is the trigger zone. This tiny region has the highest density of voltage-gated Na⁺ channels in the entire neuron, giving it the lowest threshold for firing an action potential. It's the "decision point" that determines whether the neuron fires or stays silent.

Glial Cells & Roles

Glial cells (neuroglia) outnumber neurons and are essential for nervous system function — far from being passive "glue," they actively regulate the neuronal environment, form myelin, and participate in signaling.

Resting Membrane Potential

At rest, a typical neuron has a voltage of approximately −70 mV across its membrane (inside negative relative to outside). This resting membrane potential (RMP) is the charged battery that makes all electrical signaling possible. It exists because of three factors:

1. Ion concentration gradients: The Na⁺/K⁺-ATPase maintains high K⁺ inside (155 mM) and high Na⁺ outside (145 mM)
2. Selective membrane permeability: At rest, the membrane is ~40× more permeable to K⁺ than Na⁺ (through K⁺ leak channels)
3. Electrogenic Na⁺/K⁺-ATPase: Pumps 3 Na⁺ out for every 2 K⁺ in, creating a net negative charge inside (contributes about −3 mV)

Since K⁺ permeability dominates at rest, the RMP sits close to the K⁺ equilibrium potential (−98 mV) but not at it — because there's a small but significant Na⁺ leak pulling the voltage positive, settling around −70 mV.

import numpy as np
import matplotlib.pyplot as plt

# Goldman-Hodgkin-Katz equation: how permeability ratios set the resting potential
# Vm = (RT/F) * ln((P_K*[K]o + P_Na*[Na]o + P_Cl*[Cl]i) /
#                    (P_K*[K]i + P_Na*[Na]i + P_Cl*[Cl]o))

R = 8.314    # J/(mol·K)
T = 310      # K (37°C)
F = 96485    # C/mol

# Concentrations (mM) — from Part 1
K_o, K_i = 4, 155
Na_o, Na_i = 145, 12
Cl_o, Cl_i = 120, 4

# Vary the P_Na/P_K ratio to show its effect on membrane potential
ratios = np.linspace(0.01, 0.5, 100)
Vm_values = []

for ratio in ratios:
    P_K = 1.0
    P_Na = ratio
    P_Cl = 0.45
    numerator = P_K * K_o + P_Na * Na_o + P_Cl * Cl_i
    denominator = P_K * K_i + P_Na * Na_i + P_Cl * Cl_o
    Vm = (R * T / F) * np.log(numerator / denominator) * 1000  # mV
    Vm_values.append(Vm)

plt.figure(figsize=(9, 5))
plt.plot(ratios, Vm_values, 'b-', linewidth=2)
plt.axhline(y=-70, color='green', linestyle='--', alpha=0.7, label='Typical RMP (−70 mV)')
plt.axhline(y=-55, color='red', linestyle='--', alpha=0.7, label='Threshold (−55 mV)')
plt.axvline(x=0.04, color='gray', linestyle=':', alpha=0.7, label='Resting P_Na/P_K ≈ 0.04')
plt.xlabel('P_Na / P_K Permeability Ratio', fontsize=12)
plt.ylabel('Membrane Potential (mV)', fontsize=12)
plt.title('Membrane Potential vs Na⁺/K⁺ Permeability Ratio (GHK)', fontsize=14)
plt.legend(fontsize=10)
plt.grid(True, alpha=0.3)
plt.tight_layout()
plt.show()

# Print key values
for r in [0.04, 0.1, 0.3, 0.5]:
    P_Na = r
    num = 1.0*K_o + P_Na*Na_o + 0.45*Cl_i
    den = 1.0*K_i + P_Na*Na_i + 0.45*Cl_o
    vm = (R*T/F) * np.log(num/den) * 1000
    print(f"P_Na/P_K = {r:.2f}  →  Vm = {vm:.1f} mV")

Electrical Signaling

The resting potential is the starting point — now we explore how neurons use that stored electrical energy to generate signals. The action potential (AP) is the fundamental unit of long-distance neural communication: a brief, explosive reversal of membrane potential that propagates along the axon without diminishing in amplitude.

Ion Channels

Ion channels are membrane proteins that form selective pores, allowing specific ions to flow down their electrochemical gradients at rates up to 100 million ions per second. They are classified by their gating mechanism:

Action Potential Phases

The action potential is a stereotyped sequence of membrane potential changes, driven by the coordinated opening and closing of voltage-gated channels. It unfolds in 5 phases over ~1-2 milliseconds:

import numpy as np
import matplotlib.pyplot as plt

# Hodgkin-Huxley style action potential simulation (simplified)
# Uses gating variables m, h, n for Na+ and K+ channels

dt = 0.01  # ms
t = np.arange(0, 8, dt)  # 8 ms simulation

# Parameters (Hodgkin-Huxley, squid giant axon — scaled)
C_m = 1.0      # µF/cm²
g_Na = 120.0   # mS/cm² (max Na+ conductance)
g_K = 36.0     # mS/cm² (max K+ conductance)
g_L = 0.3      # mS/cm² (leak conductance)
E_Na = 50.0    # mV
E_K = -77.0    # mV
E_L = -54.4    # mV

# Gating variable rate functions
def alpha_m(V): return 0.1 * (V + 40) / (1 - np.exp(-(V + 40) / 10))
def beta_m(V):  return 4.0 * np.exp(-(V + 65) / 18)
def alpha_h(V): return 0.07 * np.exp(-(V + 65) / 20)
def beta_h(V):  return 1.0 / (1 + np.exp(-(V + 35) / 10))
def alpha_n(V): return 0.01 * (V + 55) / (1 - np.exp(-(V + 55) / 10))
def beta_n(V):  return 0.125 * np.exp(-(V + 65) / 80)

# Initial conditions
V = np.zeros(len(t))
V[0] = -65.0
m = alpha_m(V[0]) / (alpha_m(V[0]) + beta_m(V[0]))
h = alpha_h(V[0]) / (alpha_h(V[0]) + beta_h(V[0]))
n = alpha_n(V[0]) / (alpha_n(V[0]) + beta_n(V[0]))

m_arr, h_arr, n_arr = [m], [h], [n]

# Stimulus current: brief pulse at t = 1 ms
I_stim = np.zeros(len(t))
I_stim[(t >= 1) & (t < 1.5)] = 10.0  # µA/cm²

for i in range(1, len(t)):
    # Ionic currents
    I_Na = g_Na * m**3 * h * (V[i-1] - E_Na)
    I_K  = g_K * n**4 * (V[i-1] - E_K)
    I_L  = g_L * (V[i-1] - E_L)

    # Voltage update
    dV = (I_stim[i] - I_Na - I_K - I_L) / C_m * dt
    V[i] = V[i-1] + dV

    # Gating variable updates
    m += (alpha_m(V[i]) * (1-m) - beta_m(V[i]) * m) * dt
    h += (alpha_h(V[i]) * (1-h) - beta_h(V[i]) * h) * dt
    n += (alpha_n(V[i]) * (1-n) - beta_n(V[i]) * n) * dt

    m_arr.append(m)
    h_arr.append(h)
    n_arr.append(n)

fig, (ax1, ax2) = plt.subplots(2, 1, figsize=(10, 7), sharex=True)

ax1.plot(t, V, 'b-', linewidth=2)
ax1.axhline(y=-55, color='red', linestyle='--', alpha=0.5, label='Threshold')
ax1.set_ylabel('Membrane Potential (mV)', fontsize=12)
ax1.set_title('Hodgkin-Huxley Action Potential Simulation', fontsize=14)
ax1.legend()
ax1.grid(True, alpha=0.3)

ax2.plot(t, m_arr, 'r-', linewidth=1.5, label='m (Na⁺ activation)')
ax2.plot(t, h_arr, 'r--', linewidth=1.5, label='h (Na⁺ inactivation)')
ax2.plot(t, n_arr, 'b-', linewidth=1.5, label='n (K⁺ activation)')
ax2.set_xlabel('Time (ms)', fontsize=12)
ax2.set_ylabel('Gating Variable', fontsize=12)
ax2.legend(fontsize=10)
ax2.grid(True, alpha=0.3)

plt.tight_layout()
plt.show()

Threshold & Refractory Periods

The threshold (typically ~−55 mV for most neurons) is the critical membrane voltage at which enough Na⁺ channels open to trigger the explosive positive feedback cycle. Below threshold, graded potentials decay passively. At threshold, the all-or-nothing law takes over.

Saltatory Conduction

In unmyelinated axons, the action potential propagates continuously — each patch of membrane depolarizes the next, like a row of dominoes. This is slow (~1 m/s). Myelination changes everything.

Myelin (formed by oligodendrocytes in the CNS and Schwann cells in the PNS) wraps axons in an insulating lipid sheath, with gaps called Nodes of Ranvier occurring every ~1 mm. Ion channels are concentrated at these nodes, and the action potential "jumps" from node to node — this is saltatory conduction (Latin saltare = "to leap").

Synaptic Transmission

Action potentials carry signals along axons, but neurons don't physically touch each other. Communication between neurons (and between neurons and effector cells) occurs at specialized junctions called synapses. The synapse is where information is processed, filtered, amplified, or inhibited — it's the "decision point" of neural circuitry.

Chemical vs Electrical Synapses

Neurotransmitter Release Cycle

Chemical synaptic transmission follows a precise sequence of events, all of which are potential targets for drugs and disease:

EPSP/IPSP Summation

When a neurotransmitter binds its receptor, it produces a small, graded change in the postsynaptic membrane potential:

• EPSP (Excitatory Postsynaptic Potential): Depolarization toward threshold (e.g., glutamate opens Na⁺/K⁺ channels → net depolarization of ~+1 mV)
• IPSP (Inhibitory Postsynaptic Potential): Hyperpolarization away from threshold (e.g., GABA opens Cl⁻ channels → hyperpolarization of ~−1 mV)

A single EPSP (~1 mV) isn't enough to reach threshold (~15 mV above RMP). The neuron must sum many inputs through temporal summation (rapid-fire from one synapse) and spatial summation (simultaneous input from many synapses). A typical cortical neuron receives 5,000–10,000 synaptic inputs and integrates them all at the axon hillock.

import numpy as np
import matplotlib.pyplot as plt

# Simulate EPSP/IPSP summation at a neuron
dt = 0.1   # ms
t = np.arange(0, 50, dt)
V_rest = -70.0  # mV
V_threshold = -55.0
tau = 5.0  # membrane time constant (ms)

# Individual synaptic inputs (time of input, amplitude in mV, type)
inputs = [
    (5, 3.0, 'EPSP'),
    (8, 3.0, 'EPSP'),     # temporal summation with first
    (10, 3.0, 'EPSP'),
    (10, 2.5, 'EPSP'),    # spatial summation (simultaneous)
    (15, -4.0, 'IPSP'),   # inhibitory input
    (25, 4.0, 'EPSP'),
    (26, 4.0, 'EPSP'),
    (27, 4.0, 'EPSP'),
    (28, 4.0, 'EPSP'),    # enough temporal summation to reach threshold
]

V = np.full(len(t), V_rest)

for i in range(1, len(t)):
    # Passive decay toward resting potential
    dV_decay = -(V[i-1] - V_rest) / tau * dt
    V[i] = V[i-1] + dV_decay

    # Add synaptic inputs (as instantaneous voltage jumps)
    for inp_t, amp, inp_type in inputs:
        if abs(t[i] - inp_t) < dt:
            V[i] += amp

fig, ax = plt.subplots(figsize=(10, 5))
ax.plot(t, V, 'b-', linewidth=2, label='Membrane Potential')
ax.axhline(y=V_threshold, color='red', linestyle='--', alpha=0.7, label='Threshold (−55 mV)')
ax.axhline(y=V_rest, color='green', linestyle='--', alpha=0.5, label='Resting (−70 mV)')

# Mark inputs
for inp_t, amp, inp_type in inputs:
    color = 'green' if inp_type == 'EPSP' else 'red'
    ax.axvline(x=inp_t, color=color, alpha=0.2, linewidth=1)

ax.set_xlabel('Time (ms)', fontsize=12)
ax.set_ylabel('Membrane Potential (mV)', fontsize=12)
ax.set_title('Synaptic Integration: EPSP/IPSP Summation', fontsize=14)
ax.legend(fontsize=10)
ax.grid(True, alpha=0.3)
ax.annotate('Temporal\nSummation', xy=(8, -62), fontsize=9, ha='center', color='#132440')
ax.annotate('IPSP', xy=(15, -68), fontsize=9, ha='center', color='#BF092F')
ax.annotate('Reaches\nThreshold!', xy=(29, -55), fontsize=9, ha='center', color='red', fontweight='bold')
plt.tight_layout()
plt.show()

Synaptic Plasticity

Synaptic plasticity — the ability of synapses to strengthen or weaken over time — is the cellular basis of learning and memory. It's why practice makes perfect and why traumatic experiences leave lasting marks on the brain.

Sensory & Motor Systems

The nervous system's purpose is to receive information about the world (sensory), decide what to do (integration), and execute actions (motor). These three functions form the basis of all neural processing, from the simplest reflex to the most complex voluntary movement.

Receptors & Transduction

Sensory transduction is the process of converting a physical stimulus (light, sound, pressure, heat, chemicals) into an electrical signal (receptor potential) that the nervous system can process. Each receptor type is specialized for a specific stimulus modality:

Ascending Sensory Pathways

Sensory information travels from the periphery to the cerebral cortex via two main ascending pathways, each carrying different types of sensation:

Motor Cortex & Corticospinal Tract

Voluntary movement originates in the primary motor cortex (precentral gyrus, Brodmann area 4). Upper motor neurons here send their axons down through the corticospinal tract — the main pathway for voluntary skeletal muscle control.

The corticospinal tract crosses at the pyramidal decussation in the medulla — which is why the right brain controls the left body and vice versa. Damage above the decussation (stroke in the right motor cortex) causes contralateral (left-sided) weakness. Damage below (spinal cord injury) causes ipsilateral weakness.

Reflex Arcs

A reflex is a rapid, involuntary, stereotyped response to a stimulus — the fastest type of neural processing because it doesn't require conscious brain involvement. The simplest reflexes involve as few as two neurons:

Autonomic Nervous System

The autonomic nervous system (ANS) is the involuntary division of the nervous system — it regulates visceral functions (heart rate, digestion, breathing, pupil size, glandular secretion) without conscious effort. It's the "autopilot" that keeps your internal organs running while you focus on the external world.

Sympathetic vs Parasympathetic

Neuroeffector Junctions

Autonomic neurons communicate with their target organs through neuroeffector junctions. Unlike the precise neuromuscular junction, autonomic fibers have diffuse swellings called varicosities that release neurotransmitter over a wider area.

Organ-Specific Autonomic Responses

Higher-Level Neurophysiology

The preceding sections covered the building blocks — individual neurons, synapses, and circuits. Now we explore how these elements combine to produce complex, organism-level phenomena: pain perception, sleep, learning, and the integration of neural and endocrine signaling.

Pain Pathways & Modulation

Pain is not simply a sensation — it's a complex experience that the brain actively constructs, modulates, and sometimes creates without any peripheral input at all. Understanding pain physiology is critical for clinical medicine because pain is the most common reason patients seek medical care.

The body also has a descending pain modulation system — pathways from the brainstem (periaqueductal gray, raphe nuclei, locus coeruleus) that project down to the dorsal horn and can suppress pain signals. These pathways release endorphins, enkephalins, serotonin, and norepinephrine. This system explains why soldiers in battle may not feel severe injuries until after the threat has passed.

Sleep-Wake Regulation

Sleep is an active, brain-regulated process — not simply "turning off." It involves the coordinated switching between two systems:

• Ascending Reticular Activating System (ARAS): Brainstem nuclei (locus coeruleus, raphe, tuberomammillary) release NE, serotonin, histamine → maintain wakefulness
• Ventrolateral Preoptic Area (VLPO): GABAergic/galanergic neurons in the hypothalamus → inhibit ARAS → promote sleep

These two systems mutually inhibit each other, creating a flip-flop switch — you're either awake or asleep, with rapid transitions between states (stabilized by orexin/hypocretin neurons in the lateral hypothalamus). Loss of orexin neurons causes narcolepsy, where the switch becomes unstable and patients fall asleep abruptly.

Learning & Memory

Memory formation involves multiple brain regions and molecular mechanisms operating at different timescales:

Neuroendocrine Integration

The hypothalamic-pituitary axis is the master junction between the nervous and endocrine systems. The hypothalamus receives neural input about the internal and external environment and converts it into hormonal output through two routes:

This system links every major homeostatic challenge — stress (CRH → ACTH → cortisol), reproduction (GnRH → LH/FSH), growth (GHRH → GH), metabolism (TRH → TSH → T3/T4) — to the nervous system's assessment of the body's state. We'll explore each axis in detail in Part 7 (Endocrine Regulation).

Interactive Tool: Membrane Potential Calculator

Conclusion & Next Steps

Neurophysiology is the electrical language of the body — from the resting membrane potential that stores energy like a charged battery, through the explosive all-or-nothing action potential, across synapses where chemical messages are transmitted and processed, to the integrated functions of sensation, movement, autonomic regulation, and higher cognition. In this article, we covered:

• Neural Basics: Neuron structure (dendrites → soma → axon → terminal), classification, glial cells, and the ionic basis of resting membrane potential (~−70 mV)
• Electrical Signaling: Ion channel types, the 5 phases of the action potential, threshold, all-or-nothing principle, refractory periods, and saltatory conduction
• Synaptic Transmission: Chemical vs electrical synapses, the 7-step release cycle, EPSP/IPSP summation, pharmacological targets, and synaptic plasticity (LTP/LTD)
• Sensory & Motor: Receptor types, ascending pathways (DCML, STT), motor cortex, corticospinal tract, and reflex arcs
• Autonomic NS: Sympathetic vs parasympathetic, receptor pharmacology (α, β, muscarinic, nicotinic), and autonomic tone
• Higher Functions: Pain modulation (gate control theory), sleep-wake regulation, memory systems, and neuroendocrine integration