Wulfram Gerstner

0:11:19

CNS7.1 - Models and data

1:05:10

Three-Day Symposium on Deep Learning & Neural Networks: Wulfram Gerstner

1:26:47

Wulfram Gerstner - Surprise, Curiosity, and Reward: from Neuroscience to AI - EPFL Virtual Symposium

0:02:04

Teaser - Neuronal Dynamics of Single Neurons

0:03:45

Introduction - Loss Landscape of Neural Networks - EPFL Virtual Symposium

0:14:31

RL0.1 - Introduction To Reinforcement Learning and Artificial Neural Networks

0:10:35

CNS5.2 - Sources of Variability?

0:09:06

CNS4.3B - Analysis of a 2D neuron model - constant input

0:11:28

CNS7.7 - Helping Humans

0:10:21

Introduction: Reinforcement Learning and the Brain

0:06:25

NDC2.4 - Toward biology 1: Low-activity patterns

0:19:07

CNS6.4A - Comparison of Noise Models

0:08:10

CNS7.6 - Modeling in vitro data

0:07:40

Coarse Brain Anatomy and Relation to Reinforcement Learning

0:04:26

NDC5.1 - Introduction: Aims and challenges for Decision Models in Neuroscience

0:46:12

Andrew Barto - Surprise, Curiosity, and Reward: from Neuroscience to AI - EPFL Virtual Symposium

0:06:56

CNS5.1 - Variability of Spike Trains

0:16:37

NDC6.2 - Classification of plasticity

0:12:30

NDC3.4A - Asynchronous state

0:10:18

CNS1.1 - Neurons and synapses: Overview

0:07:23

CNS6.4B - From Diffuse Noise to Escape Noise

0:49:39

Eliana Vassena - Surprise, Curiosity, and Reward: from Neuroscience to AI - EPFL Virtual Symposium

0:15:11

NDC6.8 - Online Learning of Memories (Zenke Model)