Living retina achieves sensitivity and efficiency engineers can only
dream about
Nature's ultra low-power, ultra-sensitive detector array should be the
envy of smartphones everywhere

Date:
September 28, 2021
Source:
Duke University
Summary:
'Efficient coding theory' describes the most perfect, low-energy
way to design a light-detecting device for a future camera or
prosthetic retina.

Or you could just look at a mammalian retina that's already
organized this way. In a pair of papers on retinal structure,
a team of neurobiologists has shown that the rigors of natural
selection and evolution shaped our retinas to capture noisy data
just as this theory of optimization would prescribe.



FULL STORY ==========================================================================
If you wanted to design the most perfect, low-energy, light-detecting
device for a future camera or a prosthetic retina, you'd reach for
something called 'efficient coding theory,' to set out the array of
sensors.


==========================================================================
Or you could just look at a mammalian retina.

In a pair of papers on retinal structure, Duke University neurobiologists
have shown that the rigors of natural selection and evolution have
shaped the retinas in our eyes just as this theory of optimization would predict. And that puts retinas miles ahead of anything human engineering
can achieve at this point.

In a previous paper published last March in Nature, the researchers showed
that rat and monkey retinas are laid out in patterns of sensitivity
that mimic what efficient coding theory would predict. Different sets
of retinal neurons are sensitive to individual stimuli: bright, dark,
moving, and so on, and they're arranged in a three-dimensional mosaic
of cells that works to add up the image.

Now, in a paper appearing this week in theProceedings of the National
Academy of Sciences, "we set out to understand that, through a lot of simulation and a little bit of pencil and paper math," said John Pearson,
an assistant professor of biostatistics & bioinformatics in the School
of Medicine. "The mosaics don't just randomly overlap, but they don't
overlap in a highly ordered way." "We're making a prediction about
how literally thousands of cells of multiple different types arrange
themselves across space," said Greg Field, an assistant professor of neurobiology in the Duke School of Medicine. "The monkey retina and
our retinas are nearly indistinguishable," he said. "The fact that we
observed this in the monkey retina gives us incredible confidence that our retinas are laid out in the same way." In a cross-section of the retina,
the bodies of the ganglion cells, round orbs that contain the nucleus,
line up in a layer together, but they extend their tree-like, branching dendrites into a thick layer that looks like the tangled roots of a
pot-bound houseplant. It's in this thicker, spectacularly complex layer
that mosaics of different sensitivities are laid out in ordered patterns.



==========================================================================
The ganglion cells below the dendrite layer just output ones and zeros, essentially. The sensitivity comes from the mosaic itself. And that
mosaic is not only laid out optimally, it adapts to current conditions.

"The retina is not one mosaic. It's a whole bunch of stacked mosaics. And
each of these mosaics encodes something different about the visual field," Field said. The mammalian retina parses some 40 different visual features.

"The depth that the dendrites reach in the retina is kind of like
an addressing scheme, where if you're deeper, you get one kind of
information," Field said.

"If it's more shallow, it gets a different kind of information. In fact,
the deeper ones get the 'off' signals, and the more shallow ones get the
'on' signals. So you can have many detectors sampling the same place in
the visual world, because they're using depth to convey different kinds
of signals," Field said.

One reason the array is so efficient is that the cells conserve energy by
not responding to some stimuli. In a very dark room, the environment is
'noisy' for the receptors, so they tune out most of the static and only
respond to something that's quite bright.

"The more noise there is in the world, the pickier the cell can be about
what it will respond to," Pearson said. "And when they get pickier, it
turns out that there's less redundancy in them. And so you can deploy
them in ways that don't have to overlap anymore." If there were never
any noise in the visual environment, the mosaics of detectors would
be aligned on top of each other, explained graduate student Na Young
Jun, who is the first author one of the papers and a co-author on the
other. But she computationally modeled 168 different noise conditions and
found that the higher the noise, the greater the offset between detectors.



==========================================================================
In a living mammalian retina, the team found the mosaics are offset
just as the theory would predict, meaning the retina is optimized to
deal with higher noise conditions.

If you're a small, delicious woodland creature like a mouse, "your
survival doesn't hinge so much on the things that are easy to see," Field
said. "It hinges on the things that are hard to see. And so the retina
is really geared toward being optimized to detect those things that are
hard to see." "This is an important design feature to incorporate in
any kind of retinal prosthetic that you'd want to build," Field said. But getting this idea into a smart phone may take a while. For one thing, the retina is alive and self- assembled, and it adapts and changes with time.

The energy consumption of the human retina is also orders of magnitude
less than even the best smartphone sensor at the moment, Jun said. For
example, the 5-megapixel, 1/5th of an inch OmniVision OV5675 smartphone
image sensor consumes 1.92x10-10 Watts. The human retina is conservatively estimated to consume about six percent of that (1.27x10-11 Watts in bright light). In dim conditions, the eye's energy consumption goes up to about 5.08x10-11, but it also captures single photons that no smartphone camera
ever could.

The next feature of the system the team would like to tackle is the
element of time -- differences in the response times of retinal cells
that add up to form a sense of motion, or an interpretation of moving
images. Some of it, Jun said, will be dependent on the speed at which individual detectors fire.

This research was funded by the National Eye Institute of the
U.S. National Institutes of Health (R01 EY031396), a Ruth K. Broad
postdoctoral fellowship and the Whitehead Scholars Program.

========================================================================== Story Source: Materials provided by Duke_University. Original written
by Karl Leif Bates.

Note: Content may be edited for style and length.


========================================================================== Journal References:
1. Na Young Jun, Greg D. Field, John Pearson. Scene statistics
and noise
determine the relative arrangement of receptive field mosaics.

Proceedings of the National Academy of Sciences, 2021; 118 (39):
e2105115118 DOI: 10.1073/pnas.2105115118
2. Suva Roy, Na Young Jun, Emily L. Davis, John Pearson, Greg D. Field.

Inter-mosaic coordination of retinal receptive fields. Nature,
2021; 592 (7854): 409 DOI: 10.1038/s41586-021-03317-5 ==========================================================================

Link to news story: https://www.sciencedaily.com/releases/2021/09/210928130825.htm

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