Introduction to Neural Data Analyses

Psychology 496/596L - Fall 2018

- General Information.


- Assignments, Data and Code (secure, need password)

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

- Readings

- Course Outline (likely to be 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 Spike timing and spike patterns. Reliability, precision.

Week8 Displaying single unit data and analyses. Midterm.

Part II: Multi-unit data analyses

Week9 Population vectors, cortical maps.

Week10 Dimension reduction: PCA and ICA.

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

Week12 Introduction to information theory. Measures of information (Shannon Vs Fisher).

Week13 Displaying multi-unit data and analyses.

Week14 Projects presentations.