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.