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Neuronal Dynamics - Computational Neuroscience[Lectures by Wulfram Gerstner]

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2019-05-10 20:38:05

https://lcnwww.epfl.ch/gerstner/NeuronalDynamics-MOOCall.html https://neuronaldynamics.epfl.ch/book.html

neural dynamics

Wulfram Gerstner

computational neuro

飘零の落英发消息

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1.1 - Neurons and Synapses : Overview

10:18

1.2 - The Passive Membrane

21:18

1.2 - Math detour : Linear differential equation

22:45

1.3 - Leaky Integrate-and-Fire Model

08:11

1.4 - Generalized Integrate and Fire Models

17:04

1.5 - Quality of Integrate-and-Fire Models

05:13

2.1 - Biophysics of neurons

05:22

2.2 - Reversal potential and Nernst equation

11:11

2.3 - Hodgkin-Huxley Model

19:25

2.4 - Threshold in the Hodgkin Huxley Model

26:18

2.5 - Detailed Biophysical Models

12:58

3.1 - Synapses

15:17

3.2 - Synaptic short term plasticity

09:50

3.3a - Dendrite as a Cable

11:29

3.3b - Derivation of the Cable Equation

10:10

3.4 - Cable equation

10:04

3.5 - Compartmental Models

14:23

4.1 - From Hodgkin Huxley to 2D

18:34

4.1 - MathDetour 1 : Separation of time scales

11:12

4.1 - MathDetour 2 : Exploiting similarities

16:41

4.2 - Phase Plane Analysis

17:18

4.3a - Analysis of a 2D neuron model - pulse input

12:27

4.3b - Analysis of a 2D neuron model - constant input

09:06

4.3 - MathDetour: Stability of fixed points

19:04

4.4a - Type I and Type II Neuron Models

16:23

4.4b - Firing threshold in 2D models

21:40

4.5 - Nonlinear Integrate-and-Fire Model

16:21

5.1 - Variability of spike trains

06:56

5.2 - Sources of Variability

10:35

5.3a - Three definitions of rate code

12:53

5.3b - Poisson Model

15:22

5.4a - Stochastic spike arrival

15:32

5.4b - Membrane potential fluctuations

13:01

5.5 - Stochastic spike firing in integrate and fire models

08:32

6.1 - Escape noise

15:41

6.2 - lnterspike intervals & renewal processes

29:55

6.3 - Likelihood of a spike train

18:40

6.4a - Comparison of noise models

19:07

6.4b - From diffuse noise to escape noise

07:23

6.5 - Rate Codes versus Temporal Codes

06:18

7.1 - Models and data

11:19

7.2a - AdEx: Adaptive exponential integrate-and-fire

11:12

7.2b - Firing patterns and phase plane analysis

10:50

7.3 - Spike Response Model (SRM)

15:27

7.4 - Generalized Linear Model (GLM)

07:41

7.5a - Parameter estimation

14:25

7.5b - Parameter estimation for spike times

07:48

7.6 - Modeling in vitro data

08:10

7.7 - Helping Humans

11:28

8.1 - Introduction : human memory and networks of neurons

03:48

8.2 - Classification by similarity

05:03

8.3 - Math Detour : Magnetic Materials

09:15

8.4 - Hopfield Model

14:24

8.5 - Learning of Associations

08:32

8.6 - Storage Capacity

14:41

9.1 - Attractor networks

07:34

9.2 - Stochastic Hopfield model

17:54

9.3 - Energy landscape

13:42

9.4 - Toward biology 1 : Low-activity patterns

06:25

9.5 - Toward biology 2 : Spiking neurons

15:48

10.1 - Population activity

09:13

10.2 - Cortical populations : Columns and receptive fields

06:30

10.3 - Connectivity - in cortex and in models

10:46

10.4 - Asynchronous state

12:30

10.4b - Mean-field argument

10:15

10.5 - Stationary mean-field and asynchronous state

15:48

10.6 - Random Networks and balanced state

21:13

11.1 - Aims and challenges for this chapter

07:15

11.2 - Transients

16:20

11.3 - Spatial continuum (Cortex)

03:06

11.4 - Spatial continuum (model)

17:49

11.5 - Solution types

07:54

11.6 - Perception

09:31

12.1 - Introduction : Aims and challenges for this chapter

04:26

12.2 - Perceptual Decision Making

14:51

12.3 - Theory of Decision Dynamics (Cortex)

10:54

12.4 - Solutions : symmetric case and biased case

08:09

12.5 - Simulations and Experiments

08:04

12.6 - Decisions, actions, volition

06:07

13.1 - Synaptic plasticity : motivation and aims

06:06

13.2 - Classification of plasticity

16:37

13.3 - Model of Short-Term Plasticity (see other MOOC)

00:24

13.4 - Models of Long-Term Plasticity : Hebbian learning and Bienenstock-Cooper

16:53

13.5 - STDP : Spike-Timining Dependent Models of Plasticity

10:43

13.6 - From Spiking Models to Rate Modles

09:14

13.6b - From Spiking Models to Rate Modles (math)

26:59

13.7 - Triplet STDP Model

10:49

13.8 - Online Learning of Memories

15:11

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