Computational Analysis and Modeling of Biological Signals and Systems

This syllabus will change to reflect the background and interests of class participants. We'll start with an adaptation of the syllabus that was used the last time the course was given, and work from there. Some new lecture notes will be provided, and the links below (which start as previous years' versions) will be updated and expanded.

  1. (2 weeks) Linear algebra notation/concepts
    Early Color Vision: psychophysics and physiology of color matching; application of subspace analysis.
    [Also: R. Moore, "An Introduction to the Mathematics of Digital Signal Processing", Part I & Part II]
  2. (1 week) Linear shift-invariant systems, impulse responses, FIR filters.
  3. (2 weeks) Frequency-domain representationsEuler's formulabackground of the DFTproperties of the DFT;the Fourier family. The DFT as a rotation of coordinates. Frequency and amplitude modulation, resonances. Windowing in the time domain; spectrograms. 
    Frequency-domain analysis in biological systems: tonotopic mapping in the auditory system. Frequency-domain processing of sound and images. Spectral shaping, pitch detection. Analyzing natural vocalizations.
  4. (1 week) Data-dependent coordinate transformations: Applications of eigenvalues, Singular Value Decomposition, PCA, ICA etc. in subspace-based modeling. Some non-Fourier approaches to dimensionality-reduction of time-series data.
  5. (2 weeks) Linear constant-coefficient difference equation form of causal FIR and IIR filters
    The z transform
    . More on the z transform and the LPC spectrum. Notes on LPC analysis.
  6. (1 week) Sampling. Sample rate conversion; dimensionality and the reconstruction of a signal from subsamples. Sampling of continuous signals: frequency-domain effects of models of sampling.. Effects of quantization.
  7. (1 week) Recent work in "neural nets": DNNs, RNNs, LSTMs, etc.
  8. (3 weeks) Other topics. In past years, we've looked at: Multi-rate and multi-scale processing. Scale-space, pyramids, wavelets, steerable filters. Biological representations of sound and light. Multi-scale, multi-orientation image analysis. Perceptual distortion measures. Brain imaging techniques. PET, (f)MRI, MEG.