06: Maschinelles Lernen 2 - Fortgeschrittene Verfahren, Vorlesung, SS 2017, 07.07.2017

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06 |
0:00:00 Starten
0:00:21 Motivation Video: SPAUN
0:03:27 Overview
0:04:27 Neurons anatomy
0:05:35 Neurons in action
0:07:42 Modelling Neurons
0:09:04 Synaptic plasticity
0:11:41 Coding information
0:14:06 Why not backpropagation in spiking networks?
0:17:05 Os backpropagation biologically possible
0:21:43 Synaptic plasticity as learning
0:23:41 Different types of long-term synaptic plasticity
0:24:18 Formalization of precise spiketime plasticity rules
0:27:23 Example: Hebbian and anti-hebbian learning rules
0:28:55 Formalization of rate-based plasticity rules
0:32:56 Formalization of reward-based plasticity rules
0:34:50 Other neuromodulators
0:36:32 Structural plasticity - rewiring synapses
0:40:14 Unsupervised learning with STDP
0:42:43 Supervised learning with STDP - Associative learning
0:44:18 SPORE - Synaptic Plasticity with Online Reinforcement learning
0:45:45 SPORE in action - Binary classification
0:47:08 Spiking Networks as kernel methods
0:48:44 Linear reression on rates - Neural Engineeering Framework
... mehr0:52:13 Regression on Post-Synaptic Potentials - Liquid State Machines
0:54:07 Liquid State Machines in real life
0:56:21 Neuromorphic chips - spiking network
1:01:52 Neuromorphic sensors -Silicon retina (DVS)
1:03:54 Short-term visual prediction of address events
1:04:36 Connecting robots to spiking networks
1:05:41 Grasping motions with spiking networks
1:06:37 End-to-end robot control - Braitenberg vehicles
1:09:44 mBuilding Braitenberg vehicles in simulation
1:11:53 Where to o next?