Heterogeneous response dynamics in retinal ganglion cells: the interplay of predictive coding and adaptation.
To make efficient use of their limited signaling capacity, sensory systems often use predictive coding. Predictive coding works by exploiting the statistical regularities of the environment--specifically, by filtering the sensory input to remove its predictable elements, thus enabling the neural signal to focus on what cannot be guessed. To do this, the neural filters must remove the environmental correlations. If predictive coding is to work well in multiple environments, sensory systems must adapt their filtering properties to fit each environment's statistics. Using the visual system as a model, we determine whether this happens. We compare retinal ganglion cell dynamics in two very different environments: white noise and natural. Because natural environments have more power than that of white noise at low temporal frequencies, predictive coding is expected to produce a suppression of low frequencies and an enhancement of high frequencies, compared with the behavior in a white-noise environment. We find that this holds, but only in part. First, predictive coding behavior is not uniform: most on cells manifest it, whereas off cells, on average, do not. Overlaid on this nonuniformity between cell classes is further nonuniformity within both cell classes. These findings indicate that functional considerations beyond predictive coding play an important role in shaping the dynamics of sensory adaptation. Moreover, the differences in behavior between on and off cell classes add to the growing evidence that these classes are not merely homogeneous mirror images of each other and suggest that their roles in visual processing are more complex than expected from the classic view.
Nirenberg
Sheila
S
Department of Physiology and Biophysics, Weill Cornell Medical College, New York, NY 10065, USA.
Bomash
Illya
I
Pillow
Jonathan W
JW
Victor
Jonathan D
JD
D000818:Animals; D003627:Data Interpretation, Statistical; D005583:Fourier Analysis; D066298:In Vitro Techniques; D051379:Mice; D008959:Models, Neurological; D017711:Nonlinear Dynamics; D011237:Predictive Value of Tests; D025341:Principal Component Analysis; D012160:Retina / Q000166:cytology*; D012165:Retinal Ganglion Cells / Q000502:physiology*