Biological modeling of neural networks
Weekly outline
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Lecturer: Wulfram Gerstner.
Assistants: Chiara Gastaldi, Noé Gallice and Martin Barry
Lectures on Monday 9.15-13 - BS170
Neuronal networks, consisting of neurons and synapses that form changeable connexions between the neurons, are thought to be the basis of learning, memory, and thinking. In this course we develop and use mathematical modeling techniques to describe neuronal activity and discuss aspects of neuronal dynamics, learning, and memory.
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LECTURE 1:
A First introduction and overview of course.
B Coding by Spikes (action potentials).
C Model of a passive membrane.
D Leaky integrate-and-fire model.
E Nonlinear integrate-and-fire model.
F Quality of integrate-and-fire models: comparison with experiments. -
LECTURE 2: Detailed Neuron Models:
A The Nernst Equation.
B Hodgkin-Huxley Model.
C Model of synaptic input. -
LECTURE 3: Two dimensional neuron models:
- Reduction of Hodgkin Huxely equations
- FitzHugh-Nagumo
- Phase plane analysis.
- Reduction of Hodgkin Huxely equations
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LECTURE 4:
- Complement to 2-dimensional neuron models
- Type I and Type II neurons
- Complement to 2-dimensional neuron models
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LECTURE 5: Introduction to Hopfield neural networks
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LECTURE 6: Synaptic plasticity, Hebbian learning and Hopfield model
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LECTURE 7:
Networks of Neurons, Population Activity, mean field argument
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LECTURE 8:
Neural Networks with spatial structure, competition, field equations, decision processes
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Easter Holidays
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LECTURE 9:
Connected Populations: perception, decision, and competition
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LECTURE 10:
Synaptic plasticity and Learning
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LECTURE 11:
Variability and noise: Autocorrelation
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LECTURE 12:
Optimizing Neuron Models For Coding and Decoding
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LECTURE 13:
Synaptic plasticity and Learning
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Date, time, place : Please check on IS Academia
Allowed material:
- Bring writing material (Pen, etc.).
- Paper will be provided.
- You can bring a single A5 (half the size of A4) sheet, handwritten, on which you are allowed write (recto-verso) whatever you think might be useful.
- Nothing else. (In particular no books, lecture notes, mobile phones, laptops, calculators, etc.)
You must have your student card (CAMIPRO) with you for the exam.
You can find examples of exams from previous years below.