  To compensate for sensory processing delays, the visual system must make
predictions to ensure timely and appropriate behaviors. Recent work has found
predictive information about the stimulus in neural populations early in vision
processing, starting in the retina. However, to utilize this information, cells
downstream must in turn be able to read out the predictive information from the
spiking activity of retinal ganglion cells. Here we investigate whether a
downstream cell could learn efficient encoding of predictive information in its
inputs in the absence of other instructive signals, from the correlations in
the inputs themselves. We simulate learning driven by spiking activity recorded
in salamander retina. We model a downstream cell as a binary neuron receiving a
small group of weighted inputs and quantify the predictive information between
activity in the binary neuron and future input. Input weights change according
to spike timing-dependent learning rules during a training period. We
characterize the readouts learned under spike timing-dependent learning rules,
finding that although the fixed points of learning dynamics are not associated
with absolute optimal readouts, they convey nearly all the information conveyed
by the optimal readout. Moreover, we find that learned perceptrons transmit
position and velocity information of a moving bar stimulus nearly as
efficiently as optimal perceptrons. We conclude that predictive information is,
in principle, readable from the perspective of downstream neurons in the
absence of other inputs, and consequently suggests that bottom-up prediction
may play an important role in sensory processing.
