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 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 from the correlations in the inputs themselves, in the absence of other instructive signals. 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 synaptic update rules, finding that although the fixed points of learning dynamics are not associated with absolute optimal readouts they convey nearly all of 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. This suggests an important role for feedforward prediction in sensory encoding.
|Original language||English (US)|
|Number of pages||6|
|Journal||Proceedings of the National Academy of Sciences of the United States of America|
|State||Published - Jan 30 2018|
Bibliographical noteFunding Information:
This work was supported by a Mary-Rita Angelo Fellowship (A.J.S.), the Alfred P. Sloan Foundation and NSF CAREER Grant 1652617 (to S.E.P.), and NSF CAREER Grant 0952686 (to J.N.M.).
ACKNOWLEDGMENTS. This work was supported by a Mary-Rita Angelo Fellowship (A.J.S.), the Alfred P. Sloan Foundation and NSF CAREER Grant 1652617 (to S.E.P.), and NSF CAREER Grant 0952686 (to J.N.M.).
- Information theory