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
- NO CLASS
- 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.