Do We Know What the Early Visual System Does?

Abstract

We can claim that we know what the visual system does once we can predict neural responses to arbitrary stimuli, including those seen in nature. In the early visual system, models based on one or more linear receptive fields hold promise to achieve this goal as long as the models include nonlinear mechanisms that control responsiveness, based on stimulus context and history, and take into account the nonlinearity of spike generation. These linear and nonlinear mechanisms might be the only essential determinants of the response, or alternatively, there may be additional fundamental determinants yet to be identified. Research is progressing with the goals of defining a single "standard model" for each stage of the visual pathway and testing the predictive power of these models on the responses to movies of natural scenes. These predictive models represent, at a given stage of the visual pathway, a compact description of visual computation. They would be an invaluable guide for understanding the underlying biophysical and anatomical mechanisms and relating neural responses to visual perception.Key words: contrast; lateral geniculate nucleus; luminance; primary visual cortex; receptive field; retina; visual system; natural imagesThe ultimate test of our knowledge of the visual system is prediction: we can say that we know what the visual system does when we can predict its response to arbitrary stimuli. How far are we from this end result? Do we have a "standard model" that can predict the responses of at least some early part of the visual system, such as the retina, the lateral geniculate nucleus (LGN), or primary visual cortex (V1)? Does such a model predict responses to stimuli encountered in the real world?A standard model existed in the early decades of visual neuroscience, until the 1990s: it was given by the linear receptive field. The linear receptive field specifies a set of weights to apply to images to yield a predicted response. A weighted sum is a linear operation, so it is simple and intuitive. Moreover, linearity made the receptive field mathematically tractable, allowing the fruitful marriage of visual neuroscience with image processing (Robson, 1975) and with linear systems analysis (De Valois and De Valois, 1988). It also provided a promising parallel with research in visual perception (Graham, 1989). Because it served as a standard model, the receptive field could be used to decide which findings were surprising and which were not: if a phenomenon was not predictable from the linear receptive field, it was particularly worthy of publication.Research aimed at testing the linear receptive field led to the discovery of important nonlinear phenomena, which cannot be explained by a linear receptive field alone. These phenomena have been discovered at all stages of the early visual system, including the retina (for review, see Shapley and Enroth-Cugell, 1984;Demb, 2002), the LGN (for review, see Carandini, 2004), and area V1 (for review, see Carandini et al., 1999;Fitzpatrick, 2000;Albright and Stoner,...

Functional organization of excitatory synaptic strength in primary visual cortex

“…Nonetheless, pairwise response correlation was the best predictor of connection strength (Fig. 2k); this is not surprising as pairwise response correlation includes additional information about shared response properties not captured by the linear RF 16 .…”

Virtual Retina: A biological retina model and simulator, with contrast gain control

“…To conclude, equation (13) simply describes a synaptic rectification, which is a very common feature in neural modeling and in retinal models [4,5,12]. A smooth rectification is needed here to account for the observed 'low-input' range of ganglion cells, where ganglion cells clearly act underlinearly, but are not totally silent either (see cell simulations in Section 3.3).…”

“…The easiest way to implement a sub-linearity of retinal responses with respect to contrast, is that of standard models [4], also known as LNP (Linear, Nonlinear, Poisson) models. These models allow to choose, or experimentally determine (Chichilnisky [5]) a static non-linear function through which the filtered signal is passed before spike generation.…”

“…This general architecture has been termed the retinal standard model by Carandini et al [4]. Virtual Retina is inspired by, and extends, such models.…”

Self-Invertible 2D Log-Gabor Wavelets

“…(b)-(c) appears close to the independent components learned by sparse coding or ICA techniques on natural images (Olshausen and Field, 1996;Doi and Lewicki, 2005), confirming the adequacy of the filters to match natural image features. By its parts the modulus operation on real and imaginary parts which will be exploited for denoising in Section 3 and the resulting independency toward phase are characteristic of the complex cortical cells (Carandini et al, 2005). The thresholding operation used in the denoising method has also a biological justification ).…”