Lecture 6: Dendrites and Axons Cable equation Morphoelectronic transform Multi-compartment models Action potential propagation Refs: Dayan & Abbott, Ch.

Slides:



Advertisements
Similar presentations
BME 6938 Neurodynamics Instructor: Dr Sachin S. Talathi.
Advertisements

Passage of an action potential
Rae §2.1, B&J §3.1, B&M § An equation for the matter waves: the time-dependent Schrődinger equation*** Classical wave equation (in one dimension):
Cable Theory CSCI Last time What did we do last time? Does anyone remember why our model last time did not work (other than getting infinity due.
Cable Properties Properties of the nerve, axon, cell body and dendrite affect the distance and speed of membrane potential Passive conduction properties.
Passive Properties of Axons: The neuron described as a wire.
BME 6938 Neurodynamics Instructor: Dr Sachin S Talathi.
Neurophysics Adrian Negrean - part 2 -
Dendritic computation. Passive contributions to computation Active contributions to computation Dendrites as computational elements: Examples Dendritic.
Synapses and Multi Compartmental Models
Chapter 4 The Action Potential
The passage and speed of an action potential
Waves_03 1 Two sine waves travelling in opposite directions  standing wave Some animations courtesy of Dr. Dan Russell, Kettering University TRANSVERSE.
Overview Discuss Test 1 Review RC Circuits
Excitable membranes action potential & propagation Basic Neuroscience NBL 120 (2007)
This PowerPoint shows circuit diagrams superimposed on the membrane in order to illustrate current flow in three of the tutorials: The Membrane Tutorial.
Monday April 9, Nervous system and biological electricity II 1. Pre-lecture quiz 2. A review of resting potential and Nernst equation 3. Goldman.
Chapter (6) Introduction to Quantum Mechanics.  is a single valued function, continuous, and finite every where.
Gated Ion Channels A. Voltage-gated Na + channels 5. generation of AP dependent only on Na + repolarization is required before another AP can occur K +
Fourier Transforms - Solving the Diffusion Equation.
Lecture 4- Action Potential propagation and synaptic transmission ©Dr Bill Phillips 2002, Dept of Physiology Continuous Propagation of action potentials.
Resting membrane potential 1 mV= V membrane separates intra- and extracellular compartments inside negative (-80 to -60 mV) due to the asymmetrical.
Action potentials of the world Koch: Figure 6.1. Lipid bilayer and ion channel Dayan and Abbott: Figure 5.1.
Cellular Neuroscience (207) Ian Parker Lecture # 5 - Action potential propagation
Cellular Neuroscience (207) Ian Parker Lecture # 2 - Passive electrical properties of membranes (What does all this electronics stuff mean for a neuron?)
Hodgkin & Huxley II. Voltage Clamp MK Mathew NCBS, TIFR UAS – GKVK Campus Bangalore IBRO Course in Neuroscience Center for Cognitive Neuroscience & Semantics,
Passive Electrical Properties of the Neuron Reference: Eric R. Kandel: Essentials of neural Science and Behavior. P
Cable Theory, Cable Equation, passive conduction, AP propagation The following assumes passive conduction of voltage changes down an axon or dendrite.
Synapses A. Neuromuscular Junction (typical ACh synapse) 1. arrival of action potential at terminal bulb triggers opening of voltage-gated Ca ++ channels.
Action Potentials in Different Nerve Membranes AP = A membrane potential change caused by a flow of ions through ion channels in the membrane Intracellular.
BME 6938 Neurodynamics Instructor: Dr Sachin S. Talathi.
The action potential and cellular excitability (Chapter 9-8 of KS) 1.- The cellular action potential 4.- AP propagation and cable properties of nerve and.
Biological Modeling of Neural Networks Week 4 – Reducing detail - Adding detail Wulfram Gerstner EPFL, Lausanne, Switzerland 4.2. Adding detail - synapse.
Announcements Tutorial next Thursday, Oct 9 –Submit questions to me Mid-term schedule Go vote!
Lecture 3: linearizing the HH equations HH system is 4-d, nonlinear. For some insight, linearize around a (subthreshold) resting state. (Can vary resting.
Biological Modeling of Neural Networks Week 8 – Noisy input models: Barrage of spike arrivals Wulfram Gerstner EPFL, Lausanne, Switzerland 8.1 Variation.
Nens220, Lecture 3 Cables and Propagation
Neuroprosthetics Week 4 Neuron Modelling. Implants excite neurons Retina – ganglion or bipolar cells Retina – ganglion or bipolar cells Cochlea/Ear –
Lecture 7: Stochastic models of channels, synapses References: Dayan & Abbott, Sects 5.7, 5.8 Gerstner & Kistler, Sect 2.4 C Koch, Biophysics of Computation.
Lecture 2 Membrane potentials Ion channels Hodgkin-Huxley model References: Dayan and Abbott, Gerstner and Kistler,
Capacitance and Myelination vs. Conduction Velocity Mengqi Xing, Basheer Subei, Rafael Romero.
Properties of waves: 1.Transverse 2.Longitudinal Propagation of the wave depends on the medium Medium does not travel with the wave (not exact – tsunami)
Biological Modeling of Neural Networks Week 8 – Noisy output models: Escape rate and soft threshold Wulfram Gerstner EPFL, Lausanne, Switzerland 8.1 Variation.
Signal Propagation. The negative electrode (cathode) is the stimulator. Review: About external stimulation of cells: At rest, the outside of the cell.
Time-dependent Schrodinger Equation Numerical solution of the time-independent equation is straightforward constant energy solutions do not require us.
PHYSIOLOGY 1 LECTURE 11 PROPAGATION of ACTION POTENTIALS.
Fifth lecture.
Lecture 4: more ion channels and their functions Na + channels: persistent K + channels: A current, slowly inactivating current, Ca-dependent K currents.
Box 3A The Voltage Clamp Technique
Box A The Remarkable Giant Nerve Cells of a Squid
Passive Cable Theory Methods in Computational Neuroscience 1.
Nerve Impulses.
Lecture 17 ECEN5341/4341 February 21, Nerve Cells 1. Neurons, carry information 2. Glia Cells support functions of insolation and clean up of unwanted.
Lecture 8: Integrate-and-Fire Neurons References: Dayan and Abbott, sect 5.4 Gerstner and Kistler, sects , 5.5, 5.6, H Tuckwell, Introduction.
MODEL OF WHOLE NEURON. This section brings together the entire neuron, combing the dendrite, soma, axon, and presynaptic terminal.
Neuronal Signaling Behavioral and Cognitive Neuroanatomy
Announcements Midterm –Saturday, October 23, 4:30pm Office Hours cancelled today.
AP - Overview (Click here for animation of the gates)
Signal Propagation.
Action Potential & Propagation
14 exam questions (8 on Lectures on lecture 4)
A mathematical model of a neuron
Biological Neural Networks
Electricity in the body
CSE 245: Computer Aided Circuit Simulation and Verification
Neuroprosthetics Week 4 Neuron Modelling.
Digital Signal Processing
Animals have nervous systems that detect external and internal signals, transmit and integrate information, and produce responses. Neurons.
Animals have nervous systems that detect external and internal signals, transmit and integrate information, and produce responses. Neurons.
Saltatory conduction in nerve impulses
Presentation transcript:

Lecture 6: Dendrites and Axons Cable equation Morphoelectronic transform Multi-compartment models Action potential propagation Refs: Dayan & Abbott, Ch 6, Gerstner & Kistler, sects 2.5-6; C Koch, Biophysics of Computation, Chs 2,6

Longitudinal resistance and resistivity

Longitudinal resistance

Longitudinal resistance and resistivity Longitudinal resistance Longitudinal resistivity r L ~ 1-3 k  mm 2

Longitudinal resistance and resistivity Longitudinal resistance Longitudinal resistivity r L ~ 1-3 k  mm 2

Cable equation

current balance:

Cable equation current balance: on rhs:

Cable equation current balance: on rhs:  Cable equation:

Linear cable theory Ohmic current:

Linear cable theory Ohmic current: Measure V relative to rest:

Linear cable theory Ohmic current: Measure V relative to rest: Cable equation becomes

Linear cable theory Ohmic current: Measure V relative to rest: Cable equation becomes Now define electrotonic length and membrane time constant:

Linear cable theory Ohmic current: Measure V relative to rest: Cable equation becomes Now define electrotonic length and membrane time constant: 

Linear cable theory Ohmic current: Measure V relative to rest: Cable equation becomes Now define electrotonic length and membrane time constant:  Note: cable segment of length has longitudinal resistance = transverse resistance:

Linear cable theory Ohmic current: Measure V relative to rest: Cable equation becomes Now define electrotonic length and membrane time constant:  Note: cable segment of length has longitudinal resistance = transverse resistance:

dimensionless units:

Removes,  m from equation.

dimensionless units: Removes,  m from equation. Now remove the hats:

dimensionless units: Removes,  m from equation. Now remove the hats: ( t really means t/  m, x really means x/ )

Stationary solutions No time dependence:

Stationary solutions No time dependence: Static cable equation:

Stationary solutions No time dependence: Static cable equation: General solution where i e = 0 :

Stationary solutions No time dependence: Static cable equation: General solution where i e = 0 :

Stationary solutions No time dependence: Static cable equation: General solution where i e = 0 : Point injection:

Stationary solutions No time dependence: Static cable equation: General solution where i e = 0 : Point injection: Solution:

Stationary solutions No time dependence: Static cable equation: General solution where i e = 0 : Point injection: Solution:

Stationary solutions No time dependence: Static cable equation: General solution where i e = 0 : Point injection: Solution: Solution for general i e :

Boundary conditions at junctions

V continuous

Boundary conditions at junctions V continuous Sum of inward currentsmust be zero at junction

Boundary conditions at junctions V continuous Sum of inward currentsmust be zero at junction closed end:

Boundary conditions at junctions V continuous Sum of inward currentsmust be zero at junction closed end: open end: V = 0

Green’s function Response to delta-function current source (in space and time)

Green’s function Response to delta-function current source (in space and time)

Green’s function Response to delta-function current source (in space and time) Spatial Fourier transform:

Green’s function Response to delta-function current source (in space and time) Spatial Fourier transform:

Green’s function Response to delta-function current source (in space and time) Spatial Fourier transform Easy to solve:

Green’s function Response to delta-function current source (in space and time) Spatial Fourier transform Easy to solve: Invert the Fourier transform:

Green’s function Response to delta-function current source (in space and time) Spatial Fourier transform Easy to solve: Invert the Fourier transform:

Green’s function Response to delta-function current source (in space and time) Spatial Fourier transform Easy to solve: Invert the Fourier transform: Solution for general i e (x,t) :

Pulse injection at x=0,t=0 :

u vs t at various x : x vs t max :

Pulse injection at x=0,t=0 : u vs t at various x : x vs t max : At what t does u peak?

Pulse injection at x=0,t=0 : u vs t at various x : x vs t max : At what t does u peak?

Pulse injection at x=0,t=0 : u vs t at various x : x vs t max : At what t does u peak? 

Pulse injection at x=0,t=0 : u vs t at various x : x vs t max : At what t does u peak? 

Pulse injection at x=0,t=0 : u vs t at various x : x vs t max : At what t does u peak?  

Pulse injection at x=0,t=0 : u vs t at various x : x vs t max : At what t does u peak?   Restoring ,  m :

Compare with no-leak case:

Just diffusion, no decay

Compare with no-leak case: Just diffusion, no decay Now when does it peak for a given x ?

Compare with no-leak case: Just diffusion, no decay Now when does it peak for a given x ?

Compare with no-leak case: Just diffusion, no decay Now when does it peak for a given x ? 

Compare with no-leak case: Just diffusion, no decay Now when does it peak for a given x ? 

Compare with no-leak case: Just diffusion, no decay Now when does it peak for a given x ?  Restoring ,  m :

Finite cable Method of images:

Finite cable Method of images:

Finite cable Method of images: General solution:

Finite cable Method of images: General solution:

Morphoelectronic transform

Frequency-dependent morphoelectronic transforms

Multi-compartment models

Discrete cable equations

Resistance between compartments:

Discrete cable equations Resistance between compartments: Current between compartments:

Discrete cable equations Resistance between compartments: Current between compartments: Current per unit area:

Discrete cable equations Resistance between compartments: Current between compartments: Current per unit area: 

Action potential propagation

a “reaction-diffusion equation”

Action potential propagation a “reaction-diffusion equation” Moving solutions: look for solution of the form

Action potential propagation a “reaction-diffusion equation” Moving solutions: look for solution of the form  Ordinary DE

Action potential propagation a “reaction-diffusion equation” Moving solutions: look for solution of the form  Ordinary DE

Action potential propagation a “reaction-diffusion equation” Moving solutions: look for solution of the form  Ordinary DE HH solved iteratively for s (big success of their model)

Propagation speed a/s 2 must be independent of a

Propagation speed a/s 2 must be independent of a

Propagation speed a/s 2 must be independent of a This is probably why the squid axon is so thick.

Multi-compartment model

Multicompartment calculation

Myelinated axons Nodes of Ranvier: active Na channels

Myelinated axons Nodes of Ranvier: active Na channels

Myelinated axons Treat as multilayer capacitor each layer of thickness  a : Nodes of Ranvier: active Na channels

Myelinated axons Treat as multilayer capacitor each layer of thickness  a : Nodes of Ranvier: active Na channels

Myelinated axons Treat as multilayer capacitor each layer of thickness  a : Integrate up from a1 to a2 (inverse capacitances add) Nodes of Ranvier: active Na channels

Myelinated axons Treat as multilayer capacitor each layer of thickness  a : Integrate up from a1 to a2 (inverse capacitances add) Nodes of Ranvier: active Na channels

Myelinated axons Treat as multilayer capacitor each layer of thickness  a : Integrate up from a1 to a2 (inverse capacitances add) Negligible leakage between nodes: cable equation becomes Nodes of Ranvier: active Na channels

Myelinated axons Treat as multilayer capacitor each layer of thickness  a : Integrate up from a1 to a2 (inverse capacitances add) Negligible leakage between nodes: cable equation becomes Diffusion constant: Nodes of Ranvier: active Na channels

How much myelinization? optimal a 1 /a 2 Find the value of y = a 1 /a 2 that maximizes D

How much myelinization? optimal a 1 /a 2 Find the value of y = a 1 /a 2 that maximizes D

How much myelinization? optimal a 1 /a 2 Find the value of y = a 1 /a 2 that maximizes D 

How much myelinization? optimal a 1 /a 2 Find the value of y = a 1 /a 2 that maximizes D  Agrees with experiment

Speed of propagation Diffusion equation with diffusion constant

Speed of propagation Diffusion equation with diffusion constant 

Speed of propagation Diffusion equation with diffusion constant   Speed of propagation proportional to a 2

Speed of propagation Diffusion equation with diffusion constant   Speed of propagation proportional to a 2 (cf a 1/2 for unmyelinated axon)