Neuronal Dynamics - Computational Neuroscience[Lectures by Wulfram Gerstner]

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2019-05-10 20:38:05
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https://lcnwww.epfl.ch/gerstner/NeuronalDynamics-MOOCall.html https://neuronaldynamics.epfl.ch/book.html
好好学习_(:з」∠)_
视频选集
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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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