Download presentation

Presentation is loading. Please wait.

Published byKorey Rennels Modified over 3 years ago

1
Dendritic computation

2
Passive contributions to computation Active contributions to computation Dendrites as computational elements: Examples Dendritic computation

3
r V m = I m R m Current flows uniformly out through the cell: I m = I 0 /4 r 2 Input resistance is defined as R N = V m (t)/I 0 = R m /4 r 2 Injecting current I 0 Geometry matters: the isopotential cell

4
r m and r i are the membrane and axial resistances, i.e. the resistances of a thin slice of the cylinder Linear cable theory

5
riri rmrm cmcm For a length L of membrane cable: r i r i L r m r m / L c m c m L Axial and membrane resistance

6
(1) (2) (1) or where Time constant Space constant The cable equation

7
0 Decay of voltage in space for current injection at x=0, T + I ext (x,t)

8
Electrotonic length Properties of passive cables

9
Johnson and Wu Electrotonic length

10
Properties of passive cables Electrotonic length Current can escape through additional pathways: speeds up decay

11
Johnson and Wu Voltage rise time Current can escape through additional pathways: speeds up decay

12
Koch Impulse response

13
General solution as a filter

14
Step response

15
Electrotonic length Current can escape through additional pathways: speeds up decay Cable diameter affects input resistance Properties of passive cables

16
Electrotonic length Current can escape through additional pathways: speeds up decay Cable diameter affects input resistance Cable diameter affects transmission velocity Properties of passive cables

17
Step response

19
Other factors Finite cables Active channels

20
Rall model Impedance matching: If a 3/2 = d 1 3/2 + d 2 3/2 can collapse to an equivalent cylinder with length given by electrotonic length

21
Active conductances New cable equation for each dendritic compartment

22
Wholl be my Rall model, now that my Rall model is gone, gone Genesis, NEURON

23
Passive computations London and Hausser, 2005

24
Linear filtering: Inputs from dendrites are broadened and delayed Alters summation properties.. coincidence detection to temporal integration Segregation of inputs Nonlinear interactions within a dendrite -- sublinear summation -- shunting inhibition Delay lines Dendritic inputs labelled Passive computations

25
Spain; Scholarpedia Delay lines: the sound localization circuit

26
Passive computations London and Hausser, 2005

27
Mechanisms to deal with the distance dependence of PSP size Subthreshold boosting: inward currents with reversal near rest Eg persistent Na + Synaptic scaling Dendritic spikes Na +, Ca 2+ and NMDA Dendritic branches as mini computational units backpropagation: feedback circuit Hebbian learning through supralinear interaction of backprop spikes with inputs Active dendrites

28
Segregation and amplification

30
The single neuron as a neural network

31
Currents Potential Distal: integration Proximal: coincidence Magee, 2000 Synaptic scaling

32
Synaptic potentialsSomatic action potentials Magee, 2000 Expected distance dependence

33
CA1 pyramidal neurons

34
Passive properties

36
Active properties: voltage-gated channels Na +, Ca 2+ or NDMA receptor block eliminates supralinearity For short intervals (0-5ms), summation is linear or slightly supralinear For longer intervals (5-100ms), summation is sublinear I h and K + block eliminates sublinear temporal summation

37
Active properties: voltage-gated channels Major player in synaptic scaling: hyperpolarization activated K current, I h Increases in density down the dendrite Shortens EPSP duration, reduces local summation

38
Synaptic properties While active properties contribute to summation, dont explain normalized amplitude Shape of EPSC determines how it is filtered.. Adjust ratio of AMPA/NMDA receptors

39
Rall; fig London and Hausser Direction selectivity

40
Back-propagating action potentials

41
Johnson and Wu, Foundations of Cellular Physiology, Chap 4 Koch, Biophysics of Computation Magee, Dendritic integration of excitatory synaptic input, Nature Reviews Neuroscience, 2000 London and Hausser, Dendritic Computation, Annual Reviews in Neuroscience, 2005 References

Similar presentations

OK

Lecture 3: linearizing the HH equations HH system is 4-d, nonlinear. For some insight, linearize around a (subthreshold) resting state. (Can vary resting.

Lecture 3: linearizing the HH equations HH system is 4-d, nonlinear. For some insight, linearize around a (subthreshold) resting state. (Can vary resting.

© 2017 SlidePlayer.com Inc.

All rights reserved.

Ads by Google

Business plan template free download ppt on pollution Ppt on tcp/ip protocols Ppt on power sharing in democracy it is important Ppt on weapons of mass destruction wow Ppt on layers of atmosphere Ppt on nuclear family and joint family system Doc convert to ppt online shopping Ppt on meeting etiquettes pour Ppt on series and parallel circuits equations Ppt on keeping it from harold free download