Introduction to Computational Neuroscience

Psychology 4/503c - (this class has been archived).

- General Information.

- Do I have the necessary computational and biological background to take this class? Self Test.

- Data and Code (secure, need password)

- Other classes in computational neuroscience

- Readings

Week 1: Introduction to Modeling

(Sejnowski, Koch, Churchland, 1988) and (Abbott, 2008)

Other optional readings:
(Olsen et al, 2007)
: Examples of models, neuromorphic engineering emphasis.
(Lytton 2008): Multilevel modeling in the context of epilepsy. How to understand a phenomenon at multiple levels of investigation (from channels to large networks) using computational models.
(Gerstner and Naud, 2009): How good are neural models, detailed Vs abstract models.
23 problems in systems neuroscience. Oxford University Press, 2005. van Hemmen and Sejnowski (Eds): As the title suggests...! wide-field view of computational issues from insects to monkeys. Good examples of how basic principles cut across preparation and modeling levels. requires some background in neuroscience.

Week 2: Introduction to NEURON

(Hines, Carnevale 2001) (Hines, Carnevale 2000)

Week 3: Sodium, Potassium and the Action Potential

(Hodgkin and Huxley, 1952) (Naundorf et al. 2006):
Clayton's Slides

Week 4: The Current Flora

(Khorkova et al. 2007) (Traub et al. 1991):
Jessica' Slides, Brian's Slides

Week 5: Calcium Dynamics

... no assigned readings ...

Week 6: Morphology and Dendritic Integration (passive dendrites)

(Rall 2003) (Stuart and Spruston 1998):
Derek's Slides
Greg Slides

Week 7: Midterm (Oct 6th) and Dendritic Processing (active dendrites)

Note: Papers covered are those from week 4-6 included. The take home portion can be found in the assignment area of the website.

Week 8: Synaptic transmission. The receptor flora

(Wilson Laurent 2005) (Kuhn et. al. 2004)
Adam and Kat slides

Week 9: Realistic synaptic transmission - Short term synaptic dynamics

(Abbott and Regehr, 2004) (Pfister et. al. 2010)
Eliot slides

Week 10: Small networks and central pattern generators

(Lieb and Frost, 1997) (Purvis et al, 2007)
Rafael and Sarah slides

Week 11: Q and A session: Update on projects and debugging

... no assigned readings ...

Week 12: Simplified models of neurons and networks

(Koch 1997)
Laurel's slides
Links for today's Emergent demos: The AX tutorial

Project presentations:

Jessica and Derek: The learning network
Rafael and Ryan: Identifying good figures
Greg and Sarah: Model of glia-neuron interaction
Clayton and Alie: A single compartment model of intrinsic hair cell tuning to low-frequency stimuli
Adam and Kat: Modeling the local field potential
Brian: Phase Precession in a Modified Dual Oscillator Model
Eliot and Laurel: Biophysical models of working memory

Week 14: Final
Wednesday, Dec 15th, 1pm in the usual room: Comprehensive (lecture and simulation materials), final project presentations (as posted above) and papers covered in weeks 4-12 (graduate students) and 4-8 (undergraduate students).