Dynamics and plasticity of responses in V1

Dynamic effects induced by the spatial context of visual stimuli allow V1 neurons to integrate information from different parts of the visual scene. These effects are manifested when both the classical receptive field (i.e., the receptive field center), and the extraclassical receptive field (i.e., the surround), are stimulated together. The way in which surround stimulation modulates responses elicited by a center stimulus is highly nonlinear and often nonintuitive. Thus, stimuli in the surround can either facilitate or suppress cortical responses, depending on the relative orientation and contrast between the center and the surround. The presence of a surround stimulus with an orientation similar to that of the cell's preferred orientation suppresses the response to a high-contrast optimal stimulus within the receptive field center and facilitates the response to a low-contrast optimal stimulus within the receptive field center (Polat et al., 1998; Somers et al., 1998; Toth et al., 1996). On the other hand, stimulating the surround with a stimulus whose orientation differs significantly from the cell's preferred orientation facilitates responses to optimal stimulation within the center (Levitt and Lund, 1997; Sillito et al., 1995). In this case, the cell responds “supraoptimally,” that is, beyond the level expected after stimulation with the optimal orientation.

In addition to integrating inputs from outside their classical receptive fields, V1 responses are sensitive to the history of visual stimulation or short-term experience. For instance, masking a portion of the visual field for several minutes (a situation akin to creating an artificial scotoma) while placing a patterned stimulus around the mask demonstrates the capacity of V1 neurons with receptive fields inside the artificial scotoma to alter their responses. Specifically, after a few minutes of conditioning with the artificial scotoma, the receptive fields of neurons close to the scotoma borders expand beyond their original limits and show an overall increase in responsiveness (Das and Gilbert, 1995; DeAngelis et al., 1995; Pettet and Gilbert, 1992). This type of receptive field plasticity has also been demonstrated at shorter time scales. De Weerd et al. (1995) have shown that after exposure to a static stimulus consisting of a similar artificial scotoma pattern, neurons with receptive fields inside the scotoma borders begin to respond despite the absence of retinal stimulation, a phenomenon associated with the perceptual filling-in effect (Ramachandran and Gregory, 1991).

The dependence of V1 neuron responses on short-term or recent experience is evident in the phenomenon of pattern adaptation: selective exposure for a period of time to patterned stimulation induces transient changes in the selectivity of V1 responses. Pattern adaptation has been characterized with respect to many stimulus dimensions, such as orientation (Blakemore and Campbell, 1969; Carandini et al., 1998; Dragoi et al., 2000; Hammond et al., 1986; Muller et al., 1999; Nelson, 1991), contrast (Carandini and Ferster, 1997; Carandini et al., 1998; Movshon and Lennie, 1979; Ohzawa et al., 1982), spatial frequency (Movshon and Lennie, 1979; Saul and Cynader, 1989), direction of motion (Hammond et al., 1985, 1986; Maffei et al., 1973), and velocity (Hammond et al., 1985).

Cortical neurons also have the adaptive capacity to change their responses with perceptual learning (Gilbert et al., 2001). Perceptual learning in vision is a particular form of plasticity that begins during postnatal life, continues throughout adulthood, and allows us to improve visual performance after active exposure to a structured visual environment. There are many examples of situations in which training has been shown to improve discrimination along a variety of visual stimulus dimensions. For instance, training can improve spatial resolution of the visual system (Fahle and Edelman, 1993; Poggio et al., 1992), the ability to discriminate orientations (Schoups et al., 2001; Vogels and Orban, 1985), the direction of motion (Ball and Sekuler, 1985, 1987), or the depth of visual targets (Fendick and Westheimer, 1983). However, importantly, unlike other forms of learning, in which enhanced performance in one task improves performance in related tasks, perceptual learning is highly specific for the stimulus dimension used in the training task, such as retinal position (e.g., Karni and Sagi, 1991b) or orientation (e.g., McKee and Westheimer, 1978; Ramachandran and Braddick, 1973). This high degree of specificity has important implications for the neuronal mechanisms underlying perceptual learning, for it argues that plasticity must be a phenomenon present in the early visual cortical areas. Indeed, recent work has demonstrated task-specific, learning-induced plasticity of V1 neurons in animals trained to perform either a three-line bisection task (Crist et al., 2001) or an orientation discrimination task (Schoups et al., 2001).