0 Chapter 3: Simplified neuron and population models Fundamentals of Computational Neuroscience...

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2 IF simulation

Transcript of 0 Chapter 3: Simplified neuron and population models Fundamentals of Computational Neuroscience...

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Chapter 3: Simplified neuron and population models

Fundamentals of Computational Neuroscience

Dec 09

22 The leaky integrate-and-fire neuron

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33 IF simulation

44 IF gain function

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The inverse of the first passage time defines the firing rate:

55 IF resistance to noise

66 The Izhikevich neuron

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77 The McCulloch-Pitts neuron

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88 The firing rate hypothesis

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The Nobel Prize in Physiology or Medicine 1932

99 Counter example: correlation code (?)

From DeCharms and Merzenich 1996

1010 Integrator or coincidence detector?

From Buracas et al. 1998

1111 Population model

Temporal averaging Population averaging

1212 Population dynamicsFor slow varying input (adiabatic limit), when all nodes do practically the same, same input, etc (Wilson and Cowan,1972):

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1313 Other gain functions

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1414 Fast population response

1515 Further readings

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