0 Chapter 3: Simplified neuron and population models Fundamentals of Computational Neuroscience Dec 09.

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Presentation transcript:

0 Chapter 3: Simplified neuron and population models Fundamentals of Computational Neuroscience Dec 09

1 The leaky integrate-and-fire neuron

2 IF simulation

3 IF gain function The inverse of the first passage time defines the firing rate:

4 IF resistance to noise

5 The Izhikevich neuron +

6 The McCulloch-Pitts neuron

7 The firing rate hypothesis Edgar Adrian The Nobel Prize in Physiology or Medicine 1932

8 Counter example: correlation code (?) From DeCharms and Merzenich 1996

9 Integrator or coincidence detector? From Buracas et al. 1998

10 Population model Temporal averagingPopulation averaging

11 Population dynamics For slow varying input (adiabatic limit), when all nodes do practically the same, same input, etc (Wilson and Cowan,1972): Gain function:

12 Other gain functions

13 Fast population response

14 Further readings