Predictive coding: a fresh view of inhibition in the retina

Abstract

Interneurons exhibiting centre-surround antagonism within their recep­tive fields are commonly found in peripheral visual pathways. We propose that this organization enables the visual system to encode spatial detail in a manner that minimizes the deleterious effects of intrinsic noise, by exploiting the spatial correlation that exists within natural scenes. The antagonistic surround takes a weighted mean of the signals in neighbouring receptors to generate a statistical prediction of the signal at the centre. The predicted value is subtracted from the actual centre signal, thus minimizing the range of outputs transmitted by the centre. In this way the entire dynamic range of the interneuron can be devoted to encoding a small range of intensities, thus rendering fine detail detectable against intrinsic noise injected at later stages in processing. This predictive encoding scheme also reduces spatial redundancy, thereby enabling the array of interneurons to transmit a larger number of distinguishable images, taking into account the expected structure of the visual world. The profile of the required inhibitory field is derived from statistical estimation theory. This profile depends strongly upon the signal: noise ratio and weakly upon the extent of lateral spatial correlation. The receptive fields that are quantitatively predicted by the theory resemble those of X-type retinal ganglion cells and show that the inhibitory surround should become weaker and more diffuse at low intensities. The latter property is unequivocally demonstrated in the first-order interneurons of the fly’s compound eye. The theory is extended to the time domain to account for the phasic responses of fly interneurons. These comparisons suggest that, in the early stages of processing, the visual system is concerned primarily with coding the visual image to protect against subsequent intrinsic noise, rather than with reconstructing the scene or extracting specific features from it. The treatment emphasizes that a neuron’s dynamic range should be matched to both its receptive field and the statistical properties of the visual pattern expected within this field. Finally, the analysis is synthetic because it is an extension of the background suppression hypothesis (Barlow & Levick 1976), satisfies the redundancy reduction hypothesis (Barlow 1961 a, b) and is equivalent to deblurring under certain conditions (Ratliff 1965).

Velocity constancy and models for wide-field visual motion detection in insects

“…In the first optical ganglion, the lamina monopolar cells (LMCs) are thought to reside in the visual motion detection pathway, and they display a bandpass temporal characteristic relative to front-end luminance signals (James, 1992;Srinivasan et al, 1982), with low-frequency rolloff below a few Hz.…”

“…The on-center, off-surround spatial opponency that implements highpass filtering is ubiquitous in neurobiology, and prior studies of predictive coding in the visual system (Srinivasan et al, 1982) and on LMCs (van Hateren, 1992) find evidence of suppression of low spatial frequencies in LMC responses. We have also found some evidence for such filtering in the response characteristics of wide-field motion-sensitive neurons in diurnal sphingid moths (O'Carroll, unpublished observations), although as yet not in tangential neurons in flies.…”

Adaptive encoding of motion information in the fly visual system

“…2 A). In this way the system can take advantage of the fact that in most natural input signals characteris c temporal correla ons are present, and use a strategy of "predic ve coding" (see Srinivasan et al 1982 for similar consideraons concerning spa al visual coding). For ex ample, the probability that luminance changes abruptly from bright sunlight to pitch dark is usually very low during natural visual s mula on.…”

The correlational structure of natural images and the calibration of spatial representations

“…Theories using estimates of image statistics (such as the correlation) have previously allowed successful predictions of the form of inhibition in fly retina (Srinivasan et al, 1982), the form of the compressive nonlinearity in fly retina (Laughlin,198 I), color sensitivity , contrast sensitivity (Atick 8~ Redlich, 1992, van Hateren, 1993, and short time orientation discrimination (Baddeley & Hancock, 1991).…”

“…The correlations present in one natural scene were analysed by Srinivasan, Laughlin, and Dubs (1982). The scene was of a reed bed, and the measure of correlation took no account of the mean level of luminance.'…”