CompNeurosci Lab - Dr Gunnar BLOHM

Fall 2021 Syllabus

Classroom: online
Day, time: Mon, 8:30-11:30am

Office hours: Jonny Coutinho (TA) - please make an appointment!

Office hours with Jonny are for questions on opinion paper, team-based learning projects and paper presentations. For lecture-related questions, please contact me.

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• Week 1: Introduction (computational hierarchy of the brain)
  

• Week 2: Single neuron models (differential equations)

• Week 3: Spike coding (information theory)
  

• Week 4: Sensory processing
  

Lecture
Sensory processing

Sensation

World-brain interface
Sensory organs
Sensory pre-processing

Sensory processing

Sensory receptive fields
Gain modulation
Self-organizing maps

Slides (see OnQ)

Seminar (week 5):
Bradley & Goyal (2008) - PP5
Aumentado-Armstrong et al. (2015) - PP6

• Week 5: Eye movements (state space models)

Lecture
Eye movements

Introduction to eye movements

Types
Neural substrates
Control problems

Modelling eye movements

Linear systems theory
Modelling the eye (Laplace formalism)
Gaze holding (the neural integrator)
Motor command generation

Coordinating different eye (and head) movements

Problems and solutions

Slides (see OnQ)

Seminar (week 7):
Ghasia, et al. (2008) - PP7
Orban de Xivry et al. (2013) - PP8

• Week 6: Bayesian integration of information (statistics) - no class!

Team-based learning project Reading portfolio Deneve & Pouget (2004) Körding & Wolpert (2006) Probabilities primer (optional) Project - groups Deadline for submission: Oct 16, 11:59pm

"Tell me, and I will forget.                                     Show me, and I may remember.                                     Involve me, and I will understand." (Chinese proverb)

• Week 7: Decision making (rate models)
  

• Week 8a: Fall term break (no class)

• Week 8b: Optimal control theory and arm movements - no class!

Team-based learning project Reading portfolio Scott (2012) Diedrichsen, et al. (2010) Shadmehr & Krakauer (2008) Project - groups Deadline for submission: Nov 6, 11:59pm

"Any fool can know. The point is to understand." (Albert Einstein)

• Week 9: Adaptation and learning

Lecture
Adaptation and learning

Introduction

What is learning?
Reward
Plasticity

Motor learning

Systems level approach

Reinforcement learning (RL)
Synaptic plasticity

Hebbian learning
Physiology and biophysics

Learning in neural networks

Supervised vs. unsupervised learning
Conditioning and reinforcement learning

Slides (see OnQ)

Seminar (week 10):
Izhikevich (2007) - PP11
Kool et al. (2016) - PP12

• Week 10: Modelling attention
  

Lecture
Modelling attention

What is attention?

Overt vs. covert attention
Visual attention
Neural mechanisms

Modelling visual attention (Itti & Koch)

Feature maps
Saliency maps
Top-down modulation: expectation & task demands
Selective tuning model & hierarchies (Tsotsos)

Neglect

Slides (see OnQ)

Seminar (week 11):
Miconi & VanRullen (2016) - PP13
Akbas & Eckstein (2017) - PP14

• Week 11: Memory
  

Lecture
Memory

Introduction

Definition & processes
Long-term vs. short-term memory

Short-term memory

Neuronal behaviour
Potential mechanisms
Link to attention

Memory theories (long-term)

Associative and auto-associative memory
Distributed memory
Sparse coding and capacity

Disorders

Amnesia

Slides (see OnQ)
 
Seminar (week 12):
Wilson & Linster (2008) - PP15
Couey, et al. (2013) - PP16
Schneegans & Bays (2017) - PP17

• Week 12: Q&A
  

• Exam
  

Further readings:

• suggested readings: 

• Ordinary differential equations (ODE):   Primer 1   |   Primer 2   |   Primer 3
• Integrate and fire models:   Gerstner web   |   Miller web   |   review
  

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