Course Instructor: | Mark Kramer (email, 3-4591) |

Course Hours: | 12:30-2 PM / TTh / MCS B23 |

Office Hours: | 2:00-3:30 PM / TTh, or by appointment |

Textbook: | MATLAB for Neuroscientists |

Handouts . . . |

The plan - it's still flexible . . .

**Week 1**

Lecture: Introduction + two views of neuroscience + field trip.

- You'll need to create an AD account to use the computers in Engineering. Fill out the forms here.

Reading:

Other introductory MATLAB resources:

- Introduction to the basics
of MATLAB.

- Introduction to programming in MATLAB.
- Introduction to more advanced programming in MATLAB
- Introductory tutorials from MathWorks (the creators of MATLAB).

**Week 2**

Lecture: Observations of spiking neurons and their quantification

Lab: code,
d1.mat, d2.mat, d3.mat

Optional Reading:

*MATLAB for Neuroscientists, Ch 13 & 26*- A good discussion of what we'll cover in class / lab: Teich MC and Khanna SM, J Acoust Soc Am (1985) vol. 77 (3) pp. 1110-28.
- A much more advanced discussion: Kass RE, Ventura V, Brown EN, J Neurophysiol (2005) Jul;94(1):8-25.

**Week 3**

Lecture: Statistical models of spiking data

Lab: The Poisson model, code.

Optional Reading:

*MATLAB for Neuroscientists, Ch 13 & 26*- A good discussion of what we'll cover in class / lab: Teich MC and Khanna SM, J Acoust Soc Am (1985) vol. 77 (3) pp. 1110-28.
- Poisson or not Poisson - a short discussion: Averbeck BB, Neuron (2009) 62(3):310-311.
- A much more advanced discussion: Shadlen MN, Newsome WT, J Neurosci (1998), 18(10):3870-3896.
- The Fano factor distribution for a Poisson process: Eden UT and Kramer MA, J Neurosci Meth, 190(1):149-152, 2010.

**Week 4**

Lecture: Simple biophysical models of spiking data

Lab: The integrate and fire model neuron and the leaky integrate and fire model neuron, code.

Here's the my_IF_model.m we wrote in lab.

Optional Reading:

*MATLAB for Neuroscientists, Ch 11, 12, 22*- A short paper about the integrate and fire neuron: Abbott LF, Brain Research Bulletin (1999), 50(5/6):303-304.
- Gerstner and Kistler's website about the leaky integrate and fire neuron. They wrote a good book about neuron models too.
- Izhikevich's website about his popular neuron model (the
*Izhikevich*neuron). There's a fantastic, short paper too.

**Week 5**

Lecture: More complicated biophysical models of spiking data

Lab: The Hodgkin Huxley neuron model code and HH0.m.

Optional Reading:

*MATLAB for Neuroscientists, Ch 20, 21*- The Hodgkin Huxley paper: Hodgkin and Huxley, J Physiol (Lond) (1952) vol. 117 (4) pp. 500-44. See pages 500-519 for main model derivation. BE CAREFUL: They set the resting potential to zero. To get the values accepted today, I shifted the voltage by 70 mV in class.
- Gerstner and Kistler's website about the Hodgkin-Huxley neuron. They wrote a good book about neuron models too. BE CAREFUL: They set the resting potential to zero (as Hodgkin-Huxley did). To get the values accepted today, I shifted the voltage by 70 mV in class.
- The dynamics of the Hodgkin-Huxley model are extremely complicated and an area of active research. Here's an example.

**Week 6**

Lecture: An introduction to Fourier series.

Lab: Analysis of rhythmic data. Lecture slides as pdf.

Download the lecture data set 6_data.mat and the M-file that includes MATLAB code to analyze these data.

This week's Challenge Problems.

Other data sets for challenges: lfp1.mat, lfp2.mat.

Optional Reading:

*MATLAB for Neuroscientists, Ch 7, 8*

- A good, and readable, reference for data analysis from an applied perspective: Numerical Recipes in C (online).

- A more technical reference, but still from the neuroscience perspective: Signal Processing for Neuroscientists.

**Week 7**

Lecture: Networks in neuroscience

Lab: Analyzing and constructing networks code and make_small_world.m.

Lab figures: net1 and net2.

Optional Reading:

- What we covered in lecture.
- A good review paper about networks in neuroscience.
- A readable introduction to network analysis.
- A seminal paper by about small world networks.