NSCI 401: Introduction to Modelling in Neuroscience

Syllabus (2011-2012)


Classroom: Botterell Hall, Room 129
Day, time: Tuesdays, 11:30am - 14:30pm

Office hours: Scott Murdison (TA) - Wed, Thu, Fri - 1-3pm (please make an appointment!)
Botterell Hall, Rm 230
Office hours with Scott Murdison are for questions on JC and presentation papers. For lecture-related questions, please contact myself.


Course organization (Sept 13)
Paper selection, course outline
Slides

Lecture (Sept 13):
The computational anatomy of the brain
Hierarchy
Brain structures and their functional role
Information flow
Cortical organization
Slides

Journal club (Sept 20):
Reeke & Sporns (1993) - common JC


Lecture (Sept 20):
Single neuron models
Introduction to the working principles of neurons
Membrane potentials
Ion channels
An excursion into electricity
Modelling single neuron behaviour
Synapses
Synaptic inputs
Integrate and fire models
Hodgkin-Huxley neuron model
Other models
Slides

Seminar (Sept 27):
Izhikevich (2003) - D. Chau, J. Liu
Rolls, et al. (2008) -

Journal club (Sept 27):
Naundorf, et al. (2006) - D. Moynes, C. Normandeau, Y. Wang


Lecture (Sept 27):
Spike coding
What is a code?
Spike train properties
Spike rates
Tuning curves
Spike count variability
Poisson statistics
Spike coding
Cosine tuning
Population coding
A few words about decoding
Slides

Seminar (Oct 4):
Petersen, et al. (2002) -
de la Rocha, et al. (2007) -

Journal club (Oct 4):
Averbeck, et al. (2006) - S. Calli, K. Lunny


Lecture (Oct 4):
Sensory processing
Sensation
World-brain interface
Sensory organs
Sensory pre-processing
Sensory processing
Sensory receptive fields
Gain modulation
Self-organizing maps
Visual processing specificities
Slides

Seminar (Oct 11):
Bradley & Goyal (2008) - J. Cheung, E. Song
Keller & Hahnloser (2009)

Journal club (Oct 11):
Cohen & Andersen (2002) - D. Chau, J. Liu


Lecture (Oct 11):
Bayesian integration of information
Why the brain must deal with statistics
Sensory noise
Processing noise (neuronal noise)
Primer on probability theory
Conditional probabilities
Calculating with probabilities
Bayes theorem
Bayesian integration of information
Problem-based learning example: movement decoding
The influence of expectations and priors
A note on causality and inference
Slides

Please read this and participate!

Seminar (Oct 18):
Ma, et al. (2006) - C. He, A. Lubbert
Ernst & Banks (2002) -

Journal club (Oct 18):
Körding (2007) - J. Cheung, E. Song, R. Zhang


Lecture (Oct 18):
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 25):
Ghasia, et al. (2008) -
Ramat, et al. (2007) - D. Moynes, Y. Wang

Journal club (Oct 25):
Cullen & Angelaki (2008) - C. McIntyre


Lecture (Oct 25):
Optimal control theory and arm movements
Introduction to motor control
The purpose of movement
Human vs. machine
Behavioural characteristics & neurophysiology
Control theory
Motor synergies
Open-loop control
Internal models
Optimal feedback control
Optimal control
Mathematical formulation
Optimization principles & cost functions
Evidence from the CNS
Slides

Seminar (Nov 1):
Harris & Wolpert (1998) - K. McConvey, C. Normandeau
Hatsopoulos & Donoghue (2009) -

Journal club (Nov 1):
Wolpert & Ghahramani (2000) - M. Wolff, A. in't Veld


Lecture (Nov 1):
Decision making
What are decisions?
Involved brain structures
Basal ganglia
Cortex
Models of decision making
Race models
Diffusion models
Optimal decision criteria
Conflict resolution - winner-take-all
Applications
Neuroeconomics
Deficits (Parkinson's, Huntington's)
Slides

Seminar (Nov 8):
Resulaj, et al. (2009) - M. Wolff, A. in't Veld
Barraclough, et al. (2004) - B. Chang, Z. Sharp

Journal club (Nov 8):
Rangel, et al. (2008) - M. Cvercko, L. Reid, S. Godard


Lecture (Nov 8):
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 22):
Itti & Koch (2001) - S. Calli, L. Reid
Cohen & Maunsell (2009) - R. Zhang, K. Lunny

Journal Club (Nov 22):
Dayan, et al. (2000) - B. Chang, K. McConvey, Z. Sharp

NOTE: There will be no class during the week of Nov 15 due to SfN


Lecture (Nov 22):
Adaptation and learning
Introduction
What is learning?
Reward
Plasticity
Synaptic plasticity
Hebbian learning
Physiology and biophysics
Learning in neural networks
Supervised vs. unsupervised learning
Conditioning and reinforcement learning
Slides

Seminar (Nov 29):
Izhikevich (2007) - C. McIntyre
Law & Gold (2009) - M. Cvercko, S. Godard

Journal Club (Nov 29):
Huang, et al. (2011) - C. He, A. Lubbert


Lecture (Nov 29):
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


Dec 8, 7-10pm
Fall 2010 exam questions (FYI)
FINAL EXAM QUESTIONS !!!!



Further readings:
Peter Dayan & Larry F. Abbott. Theoretical Neuroscience. MIT-Press, 2001
Thomas P. Trappenberg. Fundamentals of Neuroscience (2nd Ed.). Oxford University Press, 2010



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