Notes

Temporal Compression: Engineering The standard video-compression algorithms only send the unexpected information, i.e., the change in pixels within an image, rather than the full matrix of pixels composing an image.

Temporal Compression: Mismatch Negativity (MMN) (Oddball) Paradigm where you show a bunch of images with vertical lines and then one horizontal and viceversa, such image breaks the predictability pattern which elicits a strong response in the EEG.

Temporal Compression: Mismatch in Health Such responses are studied by clinicians to understand diseases.

• Dyslexia: diminished MMN in auditory stimuli.
• Alcoholism: Decreased MMN response, but enhanced after rehab.
• Schizophrenia: Shorter MMN durations before psychotic episodes.
• Autism: Prolonged/reduced MMN latencies.

Compression: Time Sequences Illusion Time can be used to recognize a recurrence, complex images require more time to be processed than straightforward ones. E.g., Flash-Lag Effect cannot be predicted by the brain so it doesn't look collinear, also tennis player cannot be seeing the ball and must be predicting the trajectory.

Compression: Application to RNNs (Deep Predictive Coding: Pred-Net) Training RNNs to predict sequences automatically enforces "good" representations. One of the main problem of DL is that it requires a lot of labelled images, so they trained a network and a component that computes a predictive error. Their models are trained on minimizing such prediction error. They are compressing in time.

This network consists of a series of repeating stacked modules that attempt to make local predictions of the input to the module, which is then subtracted from the actual input and passed along to the next layer. In the figure: Left: Illustration of information flow within two layers. Each layer consists of representation neurons ($R_{l})$, which output a layer-specific prediction at each time step ${(\widehat{A}}_{l})$, which is compared against a target $\left( A_{l} \right)$ to produce an error term ($E_{l})$, which is then propagated laterally and vertically in the network. Right: Module operations for case of video sequences.