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

- General Informations.

- 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)

- Class1: Introduction to biophysical neurons and neural networks.

- Class2: Basic recording techniques: single and multi unit data. Generating your own data: surrogate datasets, NEURON simulations.

Part I: Single unit data analyses

- Class3: Spontaneous activity: Spike count, firing rate, CV, return maps, fano factor.

- Class4: Stimulus driven activity: Histograms, spike triggered average, PSTH.

- Class5: Reverse correlations, tuning curves, receptive fields, discriminability and ROC curves.

- Class6: Rhythms and oscillations, autocorrelation, field potentials, power spectra and spectrogram.

- Class7: Spike timing and spike patterns. Reliability, precision.

- Class8: Displaying single unit data and analyses. Midterm.

Part II: Multi-unit data analyses


- Class9: Population vectors, cortical maps.

- Class10: Dimension reduction: PCA and ICA.

- Class11: Q&A and discussion about projects

- Class12: Cross correlations, joint-PSTH, synchrony and coherence.

- Class13:Introduction to information theory. Measures of information (Shannon Vs Fisher).

- Class14: Projects presentations.

- Class15: Projects presentations.

- Final exam.