Computational Neuroscience I: Neural Data Analyses
Psychology 496L/596L - Spring 2022

- 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)

1/12 - Class1: Introduction to biophysical neurons and neural networks.

1/19 - Class2: Basic recording techniques: single and multi unit data. Generating your own data: surrogate datasets, NEURON simulations.

Part I: Single unit data analyses

1/26 - Class3: Spontaneous activity: Spike count, firing rate, CV, return maps, fano factor.

2/2 - Class4: Stimulus driven activity: Histograms, spike triggered average, PSTH.

2/9 - Class5: Reverse correlations, tuning curves, receptive fields, discriminability and ROC curves.

2/16 - Class6: Rhythms and oscillations, autocorrelation, field potentials, power spectra and spectrogram.

2/23 - Class7: Spike timing and spike patterns. Reliability, precision.

3/2 - Class8: Displaying single unit data and analyses. Midterm.

Part II: Multi-unit data analyses

3/9 - NO CLASS

3/16 - Class9: Population vectors, cortical maps.

3/23 - Class10: Dimension reduction: PCA and ICA.

3/30 - Class11: Q&A and discussion about projects

4/6 - Class12: Cross correlations, joint-PSTH, synchrony and coherence.

4/13 - Class13:Introduction to information theory. Measures of information (Shannon Vs Fisher).

4/20 - Class14: Projects presentations.

4/27 - Class15: Projects presentations.

5/4 - Final exam.