Abstract
The responses of visual neurons, as well as visual perception phenomena in general, are highly nonlinear functions of the visual input, while most vision models are grounded on the notion of a linear receptive field (RF). The linear RF has a number of inherent problems: it changes with the input, it presupposes a set of basis functions for the visual system, and it conflicts with recent studies on dendritic computations. Here we propose to model the RF in a nonlinear manner, introducing the intrinsically nonlinear receptive field (INRF). Apart from being more physiologically plausible and embodying the efficient representation principle, the INRF has a key property of wide-ranging implications: for several vision science phenomena where a linear RF must vary with the input in order to predict responses, the INRF can remain constant under different stimuli. We also prove that Artificial Neural Networks with INRF modules instead of linear filters have a remarkably improved performance and better emulate basic human perception. Our results suggest a change of paradigm for vision science as well as for artificial intelligence.
Highlights
The responses of visual neurons, as well as visual perception phenomena in general, are highly nonlinear functions of the visual input, while most vision models are grounded on the notion of a linear receptive field (RF)
Adaptation allows to encode neural signals with less redundancy, and is an embodiment of the efficient representation p rinciple[9,10], an ecological approach for vision science that has proven to be extremely successful across mammalian, amphibian and insect species[11,12,13,14] and that states that the organization of the visual system in general and neural responses in particular are tailored to the statistics of the images that the individual typically encounters, so that visual information can be encoded in the most efficient way, optimizing the limited biological resources
We have presented the intrinsically nonlinear receptive field (INRF) and demonstrated that it has a number of remarkable advantages with respect to the classical linear RF
Summary
The responses of visual neurons, as well as visual perception phenomena in general, are highly nonlinear functions of the visual input, while most vision models are grounded on the notion of a linear receptive field (RF). Adaptation constantly adjusts the sensitivity of the visual system to the properties of the stimulus, bringing the survival advantage of making perception approximately independent from lighting conditions while quite sensitive to small differences among neighboring regions[6,7]; this happens at very different timescales, from days and hours down to the 100ms interval between rapid eye movements, when retinal neurons adapt to the local mean and variance of the signal, approximating histogram equalization[8] In this way, adaptation allows to encode neural signals with less redundancy, and is an embodiment of the efficient representation p rinciple[9,10], an ecological approach for vision science that has proven to be extremely successful across mammalian, amphibian and insect species[11,12,13,14] and that states that the organization of the visual system in general and neural responses in particular are tailored to the statistics of the images that the individual typically encounters, so that visual information can be encoded in the most efficient way, optimizing the limited biological resources. It is visual adaptation which modifies the spatial receptive field and temporal
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