 We saw in the previous section how (vector) state spaces provide an interpretation for activity patterns in neural networks, both biological and artificial. A state-space semantics for cognitive representations is a species of a functional role semantics because the individuation of a particular state depends upon the relations obtaining between it and other states. A representation is a point in an appropriate state space, and points (or subvolumes) in a space are individuated by their relations to other points (locations, geometrical proximity). Paul Churchland (1989, 1995) illustrated a state-space semantics for neural states by appealing to sensory systems. One popular theory in sensory neuroscience of how the brain codes for sensory qualities (like color) is the opponent process account (Hardin 1988). Churchland (1995) describes a three-dimensional activation vector state-space in which every color perceivable by humans is represented as a point (or subvolume). Each dimension corresponds to activity rates in one of three classes of photoreceptors present in the human retina and their efferent paths: the red-green opponent pathway, yellow-blue opponent pathway, and black-white (contrast) opponent pathway. Photons striking the retina are transduced by photoreceptors, producing an activity rate in each of the segregated pathways. A represented color is hence a triplet of neuronal activation frequency rates. As an illustration, consider again  Figure 3.  Each dimension in that three-dimensional space will represent average frequency of action potentials in the axons of one class of ganglion cells projecting out of the retina. Each color perceivable by humans will be a region of that space. For example, an orange stimulus produces a relatively low level of activity in both the red-green and yellow-blue opponent pathways (x-axis and y-axis, respectively), and middle-range activity in the black-white (contrast) opponent pathway (z-axis). Pink stimuli, on the other hand, produce low activity in the red-green opponent pathway, middle-range activity in the yellow-blue opponent pathway, and high activity in the black-white (contrast) opponent  pathway.[7]  The location of each color in the space generates a “color solid”. Location on the solid, and geometrical proximity between these locations, reflect structural similarities between the perceived colors. Human gustatory representations are points in a four-dimensional state space, with each dimension coding for activity rates generated by gustatory stimuli in each type of taste receptor (sweet, salty, sour, and bitter) and their segregated efferent pathways. When implemented in a neural network with structural resources, and hence computational resources as vast as the human brain, the state space approach to psychosemantics generates a theory of content for a huge number of cognitive  states.[8]