CompNeurosci Lab - Dr Gunnar BLOHM

Fall 2020 Syllabus

Classroom: online
Day, time: live Q&A TBD

Office hours: Jonny Coutinho & Brandon Caie (TAs) - please make an appointment!

Office hours with Jonny (for opinion paper and paper presentations) and Brandon (for TBL projects) are for questions on 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 (Sept 28 - Oct 2):
Sensory processing

Sensation

World-brain interface
Sensory organs
Sensory pre-processing

Sensory processing

Sensory receptive fields
Gain modulation
Self-organizing maps

Slides

Seminar:
Bradley & Goyal (2008)
Aumentado-Armstrong et al. (2015)

• Week 5: Eye movements (state space models)

Lecture (Oct 5-9):
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

Seminar:
Ghasia, et al. (2008)
Orban de Xivry et al. (2013)

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

Team-based learning project (Oct 12 - 16): Reading portfolio Deneve & Pouget (2004) Körding & Wolpert (2006) Probabilities primer (optional) Project - groups Deadline for submission: Oct 18, 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 8: Fall term break (no class)

Nov 2: drop-out deadline

• Week 9: Optimal control theory and arm movements - no class!

Team-based learning project (Nov 2-6): Reading portfolio Scott (2012) Diedrichsen, et al. (2010) Shadmehr & Krakauer (2008) Project - groups Deadline for submission: Nov 8, 11:59pm

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

• Week 10: Adaptation and learning

Lecture (Nov 9-13):
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

Seminar:
Izhikevich (2007)
Kool et al. (2016)

• Week 11: Modelling attention
  

Lecture (Nov 16-20):
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

Seminar:
Miconi & VanRullen (2016)
Akbas & Eckstein (2017)

• Week 12: Memory
  

Lecture (Nov 23-27):
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

Seminar:
Wilson & Linster (2008)
Couey, et al. (2013)
Schneegans & Bays (2017)

• Week 13: 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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