Neuromodeling (M1 ENS Ulm)

Teacher: Manuel Beiran

Introductive tutorial: Evolution of a population

Introductive tutorial

Problem Set 2: Quantitative models of behavior

Problem Set 2: Quantitative models of behavior.

[Problem Set 2 Quantitative models of behavior] Problem 1: Rescola-Wagner Rule

Problem 1: The Rescola-Wagner Rule

[Problem Set 2 Quantitative models of behavior] Problem 2: Decision strategy for flower sampling by bees

Problem 2: Decision strategy for flower sampling by bees.

[Problem Set 2 Quantitative models of behavior] Problem 3: The drift diffusion model of decision-making

Problem 3: The drift diffusion model of decision-making.

[Problem Set 2 Quantitative models of behavior] Problem 4: Reinforcement learning in a maze

Problem 4: Reinforcement learning in a maze.

Problem Set 3: Spike trains

Problem 1: Poisson spike trains.

[Problem Set 3 Spike trains] Problem 1: Poisson spike trains

Problem 1: Poisson spike trains.

[Problem Set 3 Spike trains] Problem 2: Analysis of spike trains

Problem 2: Analysis of spike train.

[Problem Set 3 Spike trains] Problem 3: Integrate-and-Fire neuron

Problem 3: Integrate-and-Fire neuron.

[Problem Set 3 Spike trains] Problem 4: The Hodgkin-Huxley model

Problem 4: the Hodgkin-Huxley model.

Problem Set 4: Networks

Problem Set 4: Networks.

[Problem Set 4 Networks] Problem 1: Neuron with Autapse

Problem 1: Neuron with Autapse.

[Problem Set 4 Networks] Problem 2: Circuit with mutual inhibition

Problem 2: Circuit with mutual inhibition.

[Problem Set 4 Networks] Problem 3: Hopfield model

Problem 3: Hopfield model.

Lecture 1: Neuromodeling

Manuel Beiran: manuel.beiran-at-ens.fr

Lecture 2: Rescorla-Wagner Rule

Classical conditioning

Lecture 3: Exploration-exploitation dilemma

Computational model of behavior:

Lecture 4: Drift-diffusion models

Reminder: differential equations we’ll encounter

Lecture 5: Integrate-and-fire neurons

A Cell ⟺ An RC circuit

Lecture 6: Hodgkin-Huxley model

Cell membrane = semi-permeable

[Project] Coherent Patterns of Activity from Chaotic Neural Networks

Final Project: Coherent Patterns of Activity from Chaotic Neural Networks.