[From MK (2015.07.19.2315 CET)]
The authors use 'trivial' computations to construct an "extrapolated
target position" perception. Watch ~35:00-47:00 of Meister's Heller
lecture titled "Neural computations in the retina", given on 26th May,
2009 at ELSC (Israel), for some background on the work:
https://www.youtube.com/watch?v=PEgaczSpJus
···
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Nonlinear dynamics support a linear population code in a retinal
target-tracking circuit.
Leonardo A, Meister M.
J Neurosci. 2013 Oct 23;33(43):16971-82. doi: 10.1523/JNEUROSCI.2257-13.2013.
A basic task faced by the visual system of many organisms is to
accurately track the position of moving prey. The retina is the first
stage in the processing of such stimuli; the nature of the
transformation here, from photons to spike trains, constrains not only
the ultimate fidelity of the tracking signal but also the ease with
which it can be extracted by other brain regions. Here we demonstrate
that a population of fast-OFF ganglion cells in the salamander retina,
whose dynamics are governed by a nonlinear circuit, serve to compute
the future position of the target over hundreds of milliseconds. The
extrapolated position of the target is not found by stimulus
reconstruction but is instead computed by a weighted sum of ganglion
cell outputs, the population vector average (PVA). The magnitude of
PVA extrapolation varies systematically with target size, speed, and
acceleration, such that large targets are tracked most accurately at
high speeds, and small targets at low speeds, just as is seen in the
motion of real prey. Tracking precision reaches the resolution of
single photoreceptors, and the PVA algorithm performs more robustly
than several alternative algorithms. If the salamander brain uses the
fast-OFF cell circuit for target extrapolation as we suggest, the
circuit dynamics should leave a microstructure on the behavior that
may be measured in future experiments. Our analysis highlights the
utility of simple computations that, while not globally optimal, are
efficiently implemented and have close to optimal performance over a
limited but ethologically relevant range of stimuli.