Lecture 7

This week we really swung for the fences! We read Sections 4.2 and 4.3 from Spikes (by Rieke) which cover neural coding of hyper-acuity in the bat and the fly. The fly visual system is pretty amazing - the fly can detect very small changes in its visual field far faster than can be explained by the raw speed of the visual system. It does this by integrating over a large number of broad receptive fields, thereby obviating the need for spatial oversampling. Pretty neat stuff!

The experiment described in the book goes like this: a fly is shown a random pattern and then that pattern is shifted by some small amount. The fly's H1 neuron is then monitored by dividing time from t=15-40ms post stimulus into 2ms bins and assigning a "one" (spike) or "zero" (no spike) to each of the 13 bins. (it takes 15ms for the signal to reach H1, and after 40ms, the fly has already reacted to the stimulus, meaning the signal has already been interpreted). There are therefore 2^13 possible outcomes of this experiment. The trick is to examine the contents of those 13 bits and determine whether the fly observed a pattern shift of x degrees or y degrees.

Spiking is, of course, a stochastic event - spike trains are probabilistic, not deterministic. We learned how to use signal-to-noise ratio to quantify the separability between two populations of spiking patterns, spiking patterns contributed to signal to noise ratio, and, most interestingly, that most of the information separating two populations comes in the timing of the first spike.

In general, this was a good conversation and a very tough piece of reading. I think next year this lecture could stand to be better developed, perhaps with some external readings and some simulated Matlab code.