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Comment by radomir_cernoch | Hacker News Reader

From a different point of view... The cochlea is a "real" "implementation" of Fourier transform (https://www.britannica.com/science/sound-physics/The-ear-as-...)

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The cochlea actually supports the point the article makes, as while it does transform to the frequency domain it doesn't do (or even approximate) a Fourier transform. The time->frequency domain transform it "implements" is more like a wavelet transform.

Edit: To expand on this, to interpret the cochlea as a fourier transform is to make the same mistake as thinking eyes have cone cells that respond only to red, green or blue light. The reality is that each cell has a varying reponse to a range of frequencies. Cone cells have a range that peaks in the low, medium or high frequency area and tails off at the sides. Cochlear hair cells have a more wavelet-like response curve with secondary peaks at harmonics of their peak response frequency.

Caveat: I'm not an expert in this, only an enthusiastic amateur, so I eagerly await someone well-akshuallying my well-akshually.

Sure but the article asks the question about the frequency domain generally then constrains itself to Fourier transforms. Fourier has a lot of baggage from making large assumptions. Transforms like wavelet and laplace are closer to "real world" because of fewer non-physical assumptions and have actual physical implementations. It doesn't get much more real than seeing it with your own eyes.

> Transforms like wavelet and laplace are closer to "real world" because of fewer non-physical assumptions and have actual physical implementations.

Could you expand on this a bit please? Especially as it relates to the Laplace transform.

For the cone cells we have excellent empirical data about the response curves. Do ypu know if there is public data for the chochlea hair cells?

Any kind of discrete Fourier transform, and also any device that generates the Fourier series of a periodic signal, even when done in an ideal way, must have outputs that are generated by a set of filters that have "a varying response to a range of frequencies".

Only a full Fourier transform, which has an infinity of outputs, could have (an infinite number of) filters with an infinitely narrow bandwidth, but which would also need an infinite time until producing their output.

So what you have said does not show that the eye cone cells do not perform a Fourier transform (more correctly a partial expansion in Fourier series of the light, which is periodic in time at the time scales comparable to its period).

The right explanation is that the sensitivity curves of the eye cone cells are a rather poor approximation of the optimal sensitivity curves of a set of filters for analyzing the spectral distribution of the incoming light (other animals except mammals have better sensitivity curves, but mammals have lost some of them and the ancestors of humans have re-developed 2 filters for red and green from a single inherited filter and there has not been enough time to do a job as good as in our distant ancestors).

I'm not certain the secondary peaks would matter very much though? It seems to me that maybe the most useful model would be not a wavelet transform but some form of DCT?

At any rate, the point is that the frequency domain matters a lot, since our brain essentially receives sound data converted to the frequency domain in the first place...

It's easy to forget how grounded in physics biology is. When I was in college, we had an issue where our stem cell lines were differentiating into bone. Turns out, the hardness of the environment is a signal stem cells can transduce, and the hard dish was telling them they were supposed to be bone cells.

Hmm, this mechanism can only add more bone to existing bone, right?

So how do the first bone cells know to start becoming bone?

probably a transient signal (in the rna-protein soup) that happens during embryonic development.

Which came first, the chicken or the egg?

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