Computational Neuroscience I: Neural Data Analyses
Psychology 435/535 - Fall 2024

- General Informations.

• Tuesdays, Thursdays, 11:00am-12:15am. Psychology 304
• Syllabus and grading policies. Note: Some knowledge of Matlab is required. You will probably need a laptop, or easy access to a computer with Matlab (free, university license)
• Intructor: Jean-Marc Fellous.
  Office Hour: TDB or by appointment, Psychology 517.
• Some introductory material and useful links for Matlab

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

- Assignments, Slides, Data and Code (secure, need password, see also D2L)

- Readings

- Course Outline (subject to being expanded)

- Week1: Introduction to biophysical neurons and neural networks.

- Week2: Basic recording techniques: single and multi unit data. Generating your own data: surrogate datasets, NEURON simulations.

Part I: Single unit data analyses

- Week3: Spontaneous activity: Spike count, firing rate, CV, return maps, fano factor.

- Week4: Stimulus driven activity: Histograms, spike triggered average, PSTH.

- Week5: Reverse correlations, tuning curves, receptive fields, discriminability and ROC curves.

- Week6: Rhythms and oscillations, autocorrelation, field potentials, power spectra and spectrogram.

- Week7: Displaying single unit data and analyses. Midterm1.

- Week8: Spike timing and spike patterns. Reliability, precision.

Part II: Multi-unit data analyses

- NO CLASS

- Week9: Population vectors, cortical maps.

- Week10: Dimension reduction: PCA and ICA.

- Week11: Cross correlations, joint-PSTH, synchrony and coherence.

- Week12: Displaying multi-unit data and analyses. Midterm2.

- Week13:Introduction to information theory. Measures of information (Shannon Vs Fisher).

- Week14: Projects presentations.

- Week15: Projects presentations.

- Final exam (12/17, 10:30am)