08: Maschinelles Lernen 2 - Fortgeschrittene Verfahren, Vorlesung, SS 2018, 29.06.2018

Autor

Camilo Vasquez Tieck

Herausgeber

KIT | Webcast

Beteiligtes Institut

Institut für Anthropomatik und Robotik (IAR)

Genre

Vorlesung

Beschreibung

08 |
0:00:00 Start
0:00:57 Motivation - what is intelligence?
0:04:50 Neural control in biology
0:06:39 A global initiative
0:08:57 The Human Brain Project at FZI
0:09:54 Selected literature
0:11:24 Back to biology - the brain
0:13:36 What makes us humans - the cortex
0:15:42 Hierarchical organisation of the cortex
0:17:19 Cortical cells - neurons
0:18:35 How do we learn - a theory
0:23:07 What happens in neurons and synapses ?
0:26:02 Synaptic plasticity enables learning
0:30:39 Reverse-engineering the brain - spike trains
0:33:58 History of artifical neural network models
0:36:33 Abstraction level - what gives rise to the function?
0:37:54 Different abstraction yield different models
0:38:32 Why modeling at spike level?
0:39:53 Modelling at the spike level
0:41:08 Classic spiking neuron model
0:42:11 Popular spiking neuron models
0:43:10 Focus on simplicity
0:44:31 Leaky integrate and fire in action
0:45:56 Focus on realism - Hodgin – Huxley
0:49:25 Focus on matching data - Izhivich Model
0:49:44 A different formulation - The Spike Responce Model
0:50:07 Comparaison between models
0:51:44 Information coding - a spiking network problem
0:52:22 Rate coding- from spiking to analog network
0:53:37 Binary coding - spike train sampling
0:54:43 Gaussian coding - dealing with spatial stimuli
0:56:09 Synchronous coding schemes
0:57:01 Correlations - dealing with spatio-temporal stimuli
0:58:19 I/0- encoding and decoding
0:59:27 What coding does the brain use?
1:00:04 Conclusion

Laufzeit (hh:mm:ss)

01:02:09

Serie

Maschinelles Lernen 2 - Fortgeschrittene Verfahren, Vorlesung, SS 2018

Publiziert am

05.07.2018

Fachgebiet

Informatik

Lizenz

KITopen-Lizenz