Sample Syllabus: Core Elective Course on Computational Neuroscience: From biophysics to circuit dynamics and cognitive functions

 

Course director: Xiao-Jing Wang (Meyer Building, Room 752)

 

When: Fall semester, Mondays and Wednesdays, 9:00-10:30am Where: Meyer Room 760

Recitations: 1h once a week or 2h every other week. Time TBD Office hour: TBD

 

Description: This is a new, core elective course on computational neuroscience for NYU’s neuroscience program.

 

Computational Neuroscience is a highly cross-disciplinary field where theory and modeling are used to study how the brain works. The course will be an introduction to this subject, covering basic concepts and models, neural circuit mechanisms of various brain functions, such as information coding and processing, learning and memory, working memory, decision-making. The course is designed for students to become well versed in key concepts and tools, canonical circuit mechanisms and computational principles for cross-level understanding from neural biophysics to recurrent networks to functions and behavior.

 

The prerequisites are Math Tools (NEURL-GA.2201) and Systems Neuroscience (NEURL-GA2202), or permission by the course director. Some familiarity with differential equations is desirable.

 

Graduate students in the Systems and Computational Neuroscience track are recommended to take this course before more specialized elective computational courses; students from other fields are welcome, as well as higher level undergraduates majoring in diverse areas (ranging from Physics to Cognitive Science).

 

Grade will be based on class and recitation participation (20%), weekly reading (10%), regular homework that requires computer model simulations using Python (70%).

 

Weekly reading: 2-3 book chapters and papers

 

Reference books:

 

Theoretical Neuroscience, by P. Dayan and L. F. Abbott, MIT Press, 2001

 

Lecture Notes, Xiao-Jing Wang (Manuscript tentatively entitled “Computational Neuroscience of Elemental Cognition”).

Neuroscience: Exploring the Brain, by M. F. Bear, B. W. Connors and M. A. Paradiso, Lippincott Williams & Wilkins; 3rd Edition, 2006

 

The Synaptic Organization of the Brain, by G. M. Shepherd, Oxford University Press, 5th Edition, 2003

 

Biophysics of Computation, by C. Koch, Oxford University Press, 1999

 

Spikes, Decisions and Actions, by H. Wilson, Oxford University Press, 1999

 

Nonlinear Dynamics and Chaos, by S. Strogatz, Westview Press, 2nd Edition, 2001

 

Schedule

 

9/2 (Wednesday) Introduction to Neuroscience. Neural microcircuits, neocortex.

Biophysics of neural membrane and synapses 9/7 (Monday) Labor Day, no class

9/9 (Wednesday) integrate-and-fire model, Spike-frequency Adaptation.

(homework #1)

 

9/14 (Monday) Hodgkin-Huxley theory of action potential. (homework #2)

 

9/16 (Wednesday) Biophysics of synaptic interactions

 

9/21 (Monday) Short-term synaptic plasticity. (homework #3)

9/23 (Wednesday) Elements of graph theory and network science. 9/28 (Monday) Balanced excitatory-inhibitory recurrent circuits, neural

variability and noise correlation.

 

9/30 (Wednesday) Network synchronization. Mean-field theory (rate models versus spiking models).

 

10/5 (Monday) modeling the visual cortex. (homework #4)

 

10/7 (Wednesday) Population coding 10/12 (Monday) Population decoding 10/14 (Wednesday) Bayesian theory

10/19 (Monday) Multi-sensory integration

10/21 (Wednesday) High-dimensional neural population dynamics, manifold discovery

 

10/26 (Monday) SfN meeting, no class 10/28 (Wednesday) SfN meeting, no class

11/2 (Monday) Hebbian plasticity and homeostatic regulation. 11/3 (Thursday) Midterm grades due.

11/4 (Wednesday) Hopfield network model of associative memory. Attractor networks. (homework #5)

 

11/9 (Monday) Working memory and selective attention (homework #6) 11/11 (Wednesday) Decision-making from behavioral models to physiology. 11/16 (Monday) Neural circuit models of decision-making. (homework #7) 11/18 (Wednesday) Reinforcement learning

11/23 (Monday) Models of value-based choice. (homework #8). 11/25 (Wednesday) Basics of machine learning

11/30 (Monday) Training artificial neural networks. (homework #9)

 

12/2 (Wednesday) Recurrent neural networks and AI.

 

12/7 (Monday) Large-scale, multi-regional brain circuits: connectome & dynamics. (homework #10)

 

12/9 (Wednesday) Distributed cognition in the brain 12/21 (Monday) Final grades due.