CompNeurosci Lab - Dr Gunnar BLOHM

Classroom: Mackintosh-Corry Hall, Room D214
Day, time: Mondays, 11:30am - 2:30pm

Office hours: Jonathan Coutinho (TA) - please make an appointment!
Botterell Hall, Rm 257
Office hours with Jonathan are for questions on team-based learning projects and paper presentations. For lecture-related questions, please contact me.

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All student presentations can be found here!

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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 (Oct 1):
Sensory processing

Sensation

World-brain interface
Sensory organs
Sensory pre-processing

Sensory processing

Sensory receptive fields
Gain modulation
Self-organizing maps

Slides

Seminar (Oct 15):
Bradley & Goyal (2008) - Aleks B, Sara G, Claudia T
Aumentado-Armstrong et al. (2015) - Marcelo D, Kate M, Maria S

• Week 5: Bayesian integration of information (statistics) - no class!
  

• Week 6: Eye movements (state space models)

Lecture (Oct 15):
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 (Oct 22):
Ghasia, et al. (2008) - Karen B, Alec L, Amin E
Orban de Xivry et al. (2013) - Adam A, Bailey B, Jonathan W

• Week 7: Decision making (rate models)
  

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

Team-based learning project (Oct 23 - Nov 5): Reading portfolio Scott (2012) Diedrichsen, et al. (2010) Shadmehr & Krakauer (2008) Project - groups Deadline for submission: Nov 5, 11:59pm

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

• Week 9: Adaptation and learning

Lecture (Nov 5):
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 (Nov 12):
Izhikevich (2007) - Kristen L, Alex R, Joseph T
Kool et al. (2016) - David C, Obinna O, Nazgol K

• Week 10: Modelling attention
  

Lecture (Nov 12):
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 (Nov 19):
Miconi & VanRullen (2016) - n/a
Akbas & Eckstein (2017) - Andy G, Erin M, Rachel S

• Week 11: Memory
  

Lecture (Nov 19):
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
Alzheimer's

Slides

Seminar (Nov 26):
Wilson & Linster (2008) - Victoria K, Jonathan L, Justin N
Couey, et al. (2013) - Michael A, Bernice H, Jessica K
Schneegans & Bays (2017) - Sophie C, Sarah H, Victoria C

• Week 12: TBL Project debriefing, 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
  

Please read this about academic integrity!