NEUR 415, Theoretical Neuroscience, Spring 2003
Lectures, first 13 by Cox, next 13 by Gabbiani
Single Cell Models
Goal: Achieve a quantitative understanding of this
cartoon
Attitude: "teaching physiology without a mathematical description of
the underlying dynamical processes is like teaching planetary motion to
physicists without mentioning or using Kepler's laws; you can observe that
there is a full moon every 28 days, but without Kepler's laws you cannot
determine when the next total lunar or solar eclipse will be" (Keener & Sneyd)
Membrane conductance, capacitance & resting potential
Notes. The first
Assignment is due Tuesday January 21, in class.
Linear ordinary differential equations, exact and numerical methods
Notes
The (patch) Hodgkin-Huxley Equations
Notes The second
Assignment is due Tuesday January 28, in class.
Phase-Plane Methods for Reduced Systems
Notes
The Compartmental Passive Fiber
Notes The third
Assignment is due Tuesday February 4, in class.
The Continuum Passive Fiber
Notes The fourth
Assignment is due Tuesday February 11, in class.
Appending a Soma and adding branches
Notes The fifth
Assignment is due Tuesday February 18, in class.
The Synapse
Notes The sixth
Assignment is due Tuesday February 25, in class.
AMPA & NMDA receptors
Notes
Spinal Calcium & Synaptic Plasticity
Notes
Neuronal Coding
Quantal hypothesis and models of synaptic release
Lecture material.
Spontaneous activity and quantification of neuronal variability
Lecture material
and seventh assignment plus supporting material.
Integration of synaptic inputs in dendritic trees
Lecture material and eigth assignment
plus supporting material. Assignment due Thursday, April 3, in class.
Simplified models of neuronal activity
Lecture material and
assignment 9. Due Thursday, April 10, in class.
Encoding of stimuli by instantaneous firing rate
Lecture material and
assignment 10. Due Thursday, April 17, in class.
Models of V1 simple and complex cells
Lecture material and
assignment 11. Due Thursday, April 24, in class.
Reverse-correlation methods for receptive field estimation
Lecture material
Detection of motion and ROC analysis
and
Encoding of time-varying stimuli and linear estimation methods
Lecture notes
Final Exam
Final Exam Description