This section contains lectures which were provided by several HBP PIs.
Future Computing
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W1 Gerstner Wulfram: Neurons and Synapses: Overview
Lecture: Gerstner Wulfram (EPFL) - Neurons and Synapses: Overview
Neuronal Dynamics - Computational Neuroscience of Single Neurons (Online Course 2013)
Week 1: A first simple neuron model
Duration: 10min
Future Medicine
Future Neuroscience
- Born Jan: Sleep's critical role for memory
- Dehaene Ghislaine: Origins of the human mind in the infant braint
- Dehaene Stanislas: "Reading in the Brain"
- Dehaene Stanislas: Recent Advances in Understanding the Brain Mechanisms of Consciousness
- Einevoll Gaute: From cellular/network models to tissue simulation
- Einevoll Gaute: What can we learn from multielectrode recordings of extracellular potentials in the brain?
- Goebel Rainer: Functional Brain Imaging and Brain-Computer Interfaces: Past, present, Future
- Grant Seth: The Problem of synapse complexity
- Grün Sonja: Data driven analysis of spatio-temporal cortical interaction
- Lachaux Jean-Philippe: 1998 - 2013: the new wave of human intra-cranial electrophysiology
- Lachaux Jean-Philippe: Attention, distraction and the war in our brain: Jean-Philippe Lachaux at TEDxEMLYON
- Maass Wolfgang: Does the brain play dice?
- Mortone Maryann: Where do we go from here: databases and ontologies
- Pavone Francesco: Brain research aims to advance neuromorphic computing
- Pavone Francesco: Journey into the Brain: from Single Synapse to Whole Brain Anatomy
- Segev Idan: Design principles for dendritic inhibition
- W1 Gerstner Wulfram: Generalized Integrate and Fire Models
- W1 Gerstner Wulfram: Leaky Integrate- and- Fire Model
- W1 Gerstner Wulfram: Linear differential equation
- W1 Gerstner Wulfram: Neurons and Synapses: Overview
- W1 Gerstner Wulfram: Quality of Integrate-and-Fire Models
- W1 Gerstner Wulfram: The Passive Membrane
- W2 Gerstner Wulfram: Biophysics of neurons
- W2 Gerstner Wulfram: Detailed Biophysical Models
- W2 Gerstner Wulfram: Hodgkin-Huxley Model
- W2 Gerstner Wulfram: Reversal potential and Nernst equation
- W2 Gerstner Wulfram: Threshold in the Hodgkin Huxley Model
- W3 Gerstner Wulfram: Cable equation
- W3 Gerstner Wulfram: Compartmental Models
- W3 Gerstner Wulfram: Dendrite as a Cable
- W3 Gerstner Wulfram: Derivation of the Cable Equation
- W3 Gerstner Wulfram: Synapses
- W3 Gerstner Wulfram: Synaptic short term plasticity
- W4 Gerstner Wulfram: Analysis of a 2D neuron model - constant input
- W4 Gerstner Wulfram: Analysis of a 2D neuron model - pulse input
- W4 Gerstner Wulfram: Exploiting similarities
- W4 Gerstner Wulfram: Firing threshold in 2D models
- W4 Gerstner Wulfram: From Hodgkin Huxley to 2D
- W4 Gerstner Wulfram: Nonlinear Integrate-and-Fire Model
- W4 Gerstner Wulfram: Phase Plane Analysis
- W4 Gerstner Wulfram: Separation of time scales
- W4 Gerstner Wulfram: Stability of fixed points
- W4 Gerstner Wulfram: Type I and Type II Neuron Models
- W5 Gerstner Wulfram: Membrane potential fluctuations
- W5 Gerstner Wulfram: Poisson Model
- W5 Gerstner Wulfram: Sources of Variability?
- W5 Gerstner Wulfram: Stochastic spike arrival
- W5 Gerstner Wulfram: Stochastic spike firing in integrate and fire models
- W5 Gerstner Wulfram: Three definitions of rate code
- W5 Gerstner Wulfram: Variability of spike trains
- W6 Gerstner Wulfram: Comparison of noise models
- W6 Gerstner Wulfram: Escape noise
- W6 Gerstner Wulfram: From diffuse noise to escape noise
- W6 Gerstner Wulfram: Interspike intervals & renewal processes
- W6 Gerstner Wulfram: Likelihood of a spike train
- W6 Gerstner Wulfram: Rate Codes versus Temporal Codes
- W7 Gerstner Wulfram: AdEx: Adaptive exponential integrate-and-fire
- W7 Gerstner Wulfram: Firing patterns and phase plane analysis
- W7 Gerstner Wulfram: Generalized Linear Model (GLM)
- W7 Gerstner Wulfram: Helping Humans
- W7 Gerstner Wulfram: Modeling in vitro data
- W7 Gerstner Wulfram: Models and data
- W7 Gerstner Wulfram: Parameter estimation
- W7 Gerstner Wulfram: Parameter estimation for spike times
- W7 Gerstner Wulfram: Spike Response Model (SRM)