Let’s talk about how the cochlea computes!

The tympanic membrane (eardrum) is vibrated by changes in air pressure (sound waves). Bones in the middle ear amplify and send these vibrations to the fluid-filled, snail-shaped cochlea. Vibrations travel through the fluid to the basilar membrane, which remarkably performs frequency separation: the stiffer, lighter base resonates with high frequency components of the signal, and the more flexible, heavier apex resonates with lower frequencies. Between the two ends, the resonant frequencies decrease logarithmically in space.

The hair cells on different parts of the basilar membrane wiggle back and forth at the frequency corresponding to their position on the membrane. But how do wiggling hair cells translate to electrical signals? This mechanoelectrical transduction process feels like it could be from a Dr. Seuss world: springs connected to the ends of hair cells open and close ion channels at the frequency of the vibration, which then cause neurotransmitter release. Bruno calls them “trapdoors”. Here’s a visualization:

It’s clear that the hardware of the ear is well-equipped for frequency analysis. Nerve fibers serve as filters to extract temporal and frequency information about a signal. Below are examples of filters (not necessarily of the ear) shown in the time domain. On the left are filters that are more localized in time, i.e. when a filter is applied to a signal, it is clear when in the signal the corresponding frequency occurred. On the right are filters that have less temporal specificity, but are more uniformly distributed across frequencies compared to the left one.

Wouldn’t it be convenient if the cochlea were doing a Fourier transform, which would fit cleanly into how we often analyze signals in engineering? But no 🙅🏻‍♀️! A Fourier transform has no explicit temporal precision, and resembles something closer to the waveforms on the right; this is not what the filters in the cochlea look like.

We can visualize different filtering schemes, or tiling of the time-frequency domain, in the following figure. In the leftmost box, where each rectangle represents a filter, a signal could be represented at a high temporal resolution (similar to left filters above), but without information about its constituent frequencies. On the other end of the spectrum, the Fourier transform performs precise frequency decomposition, but we cannot tell when in the signal that frequency occurred (similar to right filters). What the cochlea is actually doing is somewhere between a wavelet and Gabor. At high frequencies, frequency resolution is sacrificed for temporal resolution, and vice versa at low frequencies.

Why would this type of frequency-temporal precision tradeoff be a good representation? One theory, explored in Lewicki 2002, is that these filters are a strategy to reduce the redundancy in the representation of natural sounds. Lewicki performed independent component analysis (ICA) to produce filters maximizing statistical independence, comparing environmental sounds, animal vocalizations, and human speech. The tradeoffs look different for each one, and you can kind of map them to somewhere in the above cartoon.

It appears that human speech occupies a distinct time-frequency space. Some speculate that speech evolved to fill a time-frequency space that wasn’t yet occupied by other existing sounds.

To drive the theory home, one that we have been hinting at since the outset: forming ecologically-relevant representations makes sense, as behavior is dependent on the environment. It appears that for audition, as well as other sensory modalities, we are doing this. This is a bit of a teaser for efficient coding, which we will get to soon.

We’ve talked about some incredible mechanisms that occur at the beginning of the sensory coding process, but it’s truly just the tiny tip of the ice burg. We also glossed over how these computations occur. The next lecture will zoom into the biophysics of computation in neurons.