Distributed processing of color and form in the visual cortex

Abstract

To what extent does the visual system process color and form separately? Proponents of the segregation view claim that distinct regions of the cortex are dedicated to each of these two dimensions separately. However, evidence is accumulating that color and form processing may, at least to some extent, be intertwined in the brain. In this perspective, we review psychophysical and neurophysiological studies on color and form perception and evaluate their results in light of recent developments in population coding.

Keywords: color; complex selectivity; distributed processing; form; high dimensional code; integration; mixed selective cells; segregation.

FIGURE 1

Schematic representation of an early segregation model of visual information pathways from the retina to V2. Parasol cells in the retina are linked to the magnocellular pathway. They project to layers 1 and 2 of LGN, continue to layer 4Cα of V1, and then from layer 4B of V1 they project to the thick stripes of V2. This pathway conveys information about motion and stereo. Midget cells in the retina are part of the parvocellular pathway; they project to layers 3–6 of LGN and on to layer 4Cβ of V1. From then on they split into two streams. The stream that conveys information about color projects to the blobs in layers 2/3 of V1 and then to the thin stripes in V2. The stream that conveys information about form projects to the interblob area in layers 2/3 of V1, and then to the interstripes in V2 (drawn by Anastasia Lavdaniti; anastasialavdaniti@gmail.com).

FIGURE 2

Selectivity of V2 neurons in different CO compartments (taken from Gegenfurtner, 2003). The graph shows selectivities of cells for color, orientation, direction and size in thick, thin and inter-stripes in V2 from six different studies. The black lines are the average selectivities from the six studies.

FIGURE 3

Color induction in primary visual cortex (taken from Wachtler et al., 2003). (A) The color patches on the rows marked by an asterisk are physically identical, but are shown on different backgrounds; thus they appear different. For example color patch (b) looks more similar to physically different color patch (a) compared to physically identical color patch (c). (B) Estimated responses of four neurons to patches (a)–(c). (see “Estimating Stimulus Color from Population Responses” in the Results section of Wachtler et al. (2003) for a detailed description of the analysis). The responses are normalized relative to the maximum firing rate for each neuron. The pattern of responses of the four neurons for patch (b) are more similar with the responses for patch (a) than for patch (c), even though patches (b) and (c) are physically identical. Similar results were found for a population of 94 neurons.

FIGURE 4

Dimensionality of neural representations (taken from Rigotti et al., 2013). (A) Contour plots of the firing rate of four neurons (spikes/sec). Their firing rate is shown as a function of conditions a and b which vary from 0–1. Neurons 1 and 2 are pure selective: they respond only to condition a and b, respectively. Neuron 3 is linearly mixed selective: its response is a linear combination of its firing rate to single parameters. Neuron 4 is non-linearly mixed: its response cannot be expressed as a linear combination of its firing rate to single parameters. The circles indicate the responses of the neurons for three different combinations of a and b. (B) The space of activities of the pure and linearly mixed neurons. (C), as in (B), with the only difference being that the axis where the linearly mixed neuron’s response was represented is replaced by the axis that represents the response of the non-linearly mixed neuron. The circles represent the response of the neurons for the same combinations of conditions a and b as in (A). In (B) we see that the response of the neurons lie in low dimensional space (a line). This low dimensional space limits the possible input output relationships that a linear classifier can implement. For example a linear decoder (a two dimensional plane in this case) cannot separate the black dot from the green dots. In (C) where the activity of the non-linearly mixed neuron is represented, a plane not only can separate the black dot from the green dots, but it can also separate any possible combination of the three dots. This is because the activity of the neurons lies in a higher dimensional space (a plane).