Syllabus (2019-2020)
Classroom:
Botterell Hall, Rm 449
Day, time:
Winter term, Tue 12:30pm-3:30pm
- Jan 7: Introduction & Math tutorial
What is computational
neuroscience
Why model brain function
Introduction to the computational anatomy of the brain
Math tutorial
Ordinary differential
equations (ODEs)
Slides
- Jan 21: Spiking single
neurons
- Jan 28: Spiking neural networks
Reading:
Stein, Gossen
& Jones (2005)
Neuronal firing variability
Spike time variability
Efficient coding hypothesis
Spiking networks
Phase oscillations and synaptic coupling
Synchronization and phase locking
Examples
Hebbian learning
Associative memory
Synaptic plasticity
Mathematical formulation of Hebbian learning
Slides |
Matlab code 1 (Izhiekevich)
|
Matlab
code 2 (single Deneve neuron)
|
Matlab code 3
(multiple Deneve neurons)
- Feb 4: Rate-based feed-forward artificial neural networks
Introduction
From spikes to firing rates
Neural transfer functions
Feed-forward networks
Perceptron
Radial-basis function networks
Training algorithms
Gradient descent (back-propagation or Widrow-Hoff)
Unsupervised learning
k-means
Slides |
Matlab code 1
|
Matlab code 2 &
Training set
- Feb 11: Rate-based recurrent artificial neural networks
Introduction
From feed-forward to recurrent networks
Competitive networks
Self-organizing maps (Kohonen maps)
Neural field theory
Path integration
Network stability and chaos
Slides |
Matlab code 1
|
Matlab code 2
|
Matlab code 3
- Feb 25: Modelling at the systems level
- Mar 3: Bayesian statistics
Reading: Ma,
Kording, Goldreich book (chapter 1)
Introduction to Bayesian problems
Bayes’ theorem
Probabilities primer
Conditional probabilities
Population codes
Coding and decoding
Representing uncertainty with population codes
Bayesian integration
Cue combination
Estimation of priors
Causality and inference
Discussion
Slides
| Matlab code
| Data set
- Mar 24: Optimal control
theory
Reading:
Scott (2004)
Arm movement behaviour
Optimal feedback control (OFC)
Approach
Control
Estimation
Principles of OFC
Examples
Role of biomechanics
Slides |
Matlab code
- Mar 31: Reinforcement
learning
Reading: Ludwig,
et al. (2011)
Introduction
The reinforcement learning problem
Agent-environment interactions
Markov properties
Value functions
Solutions
TD(0)
On-policy TD control (Sarsa)
Off-policy Q-learning
Actor-Critic methods
Enter Zoom lecture
here
Slides
| Data set
| Matlab
code
How-to-model
guide
- Apr 28: Specific models
(final assignments)
12:30 - 1:00: Megan
1:00 - 1:30: Janis
1:30 - 2:00: Jonathan
2:00 - 2:30: Joshua
2:30 - 3:00: Emils
3:00 - 3:30: Sarah
Further
readings:
Please read this about academic
integrity!
