Lecture 2 Membrane potentials Ion channels Hodgkin-Huxley model References: Dayan and Abbott, Gerstner and Kistler,
Cell membranes
Lipid bilayer, 3-4 nm thick capacitance c = C/A ~ 10 nF/mm 2
Cell membranes Lipid bilayer, 3-4 nm thick capacitance c = C/A ~ 10 nF/mm 2 Ion channels conductance
Cell membranes Lipid bilayer, 3-4 nm thick capacitance c = C/A ~ 10 nF/mm 2 Ion channels conductance Typical A = mm 2 C ~.1 – 1 nF
Cell membranes Lipid bilayer, 3-4 nm thick capacitance c = C/A ~ 10 nF/mm 2 Ion channels conductance Typical A = mm 2 C ~.1 – 1 nF Q=CV, Q= 10 9 ions |V| ~ 65 mV
Membrane potential Fixed potential concentration gradient
Membrane potential Fixed potential concentration gradient Concentration difference Potential difference Concentration difference maintained by ion pumps, which are transmembrane proteins
Nernst potential Concentration ratio for a specific ion (inside/outside): = 1/k B T ( q = proton charge, z = ionic charge in units of q )
Nernst potential Concentration ratio for a specific ion (inside/outside): = 1/k B T ( q = proton charge, z = ionic charge in units of q ) No flow at this potential difference Called Nernst potential or reversal potential for that ion
Reversal potentials Note: V T = k B T/q = (for chemists) RT/F ~ 25 mv Some reversal potentials: K: mV Na: +50 mV Cl: mV Ca: 150 mV Rest potential: ~ -65 mV ~2.5 V T
Effective circuit model for cell membrane
( C, g i, I ext all per unit area) (“point model”: ignores spatial structure)
Effective circuit model for cell membrane ( C, g i, I ext all per unit area) (“point model”: ignores spatial structure) g i can depend on V, Ca concentration, synaptic transmitter binding, …
Ohmic model One g i = g = const or
Ohmic model One g i = g = const or membrane time const
Ohmic model One g i = g = const or Start at rest: V= V 0 at t=0 membrane time const
Ohmic model One g i = g = const or Final state: Start at rest: V= V 0 at t=0 membrane time const
Ohmic model One g i = g = const or Final state: Start at rest: V= V 0 at t=0 Solution: membrane time const
channels are stochastic
Many channels: effective g = g open * (prob to be open) * N
Voltage-dependent channels
K channel Open probability: 4 independent, equivalent, conformational changes
K channel Open probability: 4 independent, equivalent, conformational changes Kinetic equation:
K channel Open probability: 4 independent, equivalent, conformational changes Kinetic equation: Rearrange:
K channel Open probability: 4 independent, equivalent, conformational changes Kinetic equation: Rearrange: relaxation time: asymptotic value
Thermal rates: u 1, u 2 : barriers
Thermal rates: u 1, u 2 : barriers Assume linear in V :
Thermal rates: u 1, u 2 : barriers Assume linear in V :
Thermal rates: u 1, u 2 : barriers Assume linear in V : Simple model: a n =b n, c 1 =c 2
Thermal rates: u 1, u 2 : barriers Assume linear in V : Simple model: a n =b n, c 1 =c 2 Similarly,
Hodgkin-Huxley K channel
(solid: exponential model for both and Dashed: HH fit)
Transient conductance: HH Na channel 4 independent conformational changes, 3 alike, 1 different (see picture)
Transient conductance: HH Na channel 4 independent conformational changes, 3 alike, 1 different (see picture) HH fits:
Transient conductance: HH Na channel 4 independent conformational changes, 3 alike, 1 different (see picture) HH fits:
Transient conductance: HH Na channel 4 independent conformational changes, 3 alike, 1 different (see picture) HH fits: m is fast (~.5 ms) h,n are slow (~5 ms)
Hodgkin-Huxley model
Parameters: g L = mS/mm 2 g K = 0.36 mS/mm 2 g Na = 1.2 ms/mm 2 V L = mV V K = -77 V Na = 50 mV
Spike generation Current flows in, raises V m increases (h slower to react) g Na increases more Na current flows in … V rises rapidly toward V Na Then h starts to decrease g Na shrinks V falls, aided by n opening for K current Overshoot, recovery
Spike generation Current flows in, raises V m increases (h slower to react) g Na increases more Na current flows in … V rises rapidly toward V Na Then h starts to decrease g Na shrinks V falls, aided by n opening for K current Overshoot, recovery Threshold effect
Spike generation (2)
Regular firing, rate vs I ext
Step increase in current
Noisy input current, refractoriness