Spatial vision in V1

Spatial vision in V1

In the lateral geniculate nucleus (or LGN), we saw the beginnings of what we’ll call spatial vision, or the measurement of patterns of light by the visual system. Compared to what the photoreceptors do, LGN cells (and their predecessors in the visual system, the retinal ganglion cells) respond best when light is in specific arrangements. Within the receptive field of an LGN cell, there are both excitatory and inhibitory regions arranged into a center-surround structure. This ends up meaning that LGN cells respond most vigorously when there is either a spot of light surrounded by a darker region (an on-center cell) or a darker spot surrounded by light (an off-center cell). In the magnocellular layer, these cells only measure light/dark contrast. In the parvocellular layer, the excitatory and inhibitory regions are also wavelength-specific: An LGN cell may respond best when there is red light in the surround and green light in the center, for example. These receptive field structures enable these cells to measure basic luminance and chromatic contrast  - parts of an image that differ from what is around them. If this is our first set of measurements that help us measure something about the patterns of light in an image, what comes next? How does your visual system build on these measurements to encode more complex aspects of an image?

To answer this question, we’re going to continue following the anatomical connections from your retina onward to your brain. Our next stop on this route is at the back of your occipital lobe, in a region called primary visual cortex or V1. There is a great deal that we could say about the way this region is laid out (which is pretty cool), but we’re going to focus on trying to understand what the cells in this part of the brain are measuring using the same tools we introduced to study what LGN cells responded to best: single-unit recordings. Remember, this is a handy way to identify the receptive field of a particular cell and also describe the layout of excitatory and inhibitory regions within that receptive field. If we listen to single-units in V1, what do we learn about the kinds of stimuli that they like? What we’ll find are patterns of excitatory/inhibitory regions that might look something like what you see below:

Figure 1 - A typical pattern of excitatory and inhibitory regions in a V1 cell's receptive field.

Never mind the hexagons here – I’m just using them as a handy way to divide up a 2D region with cells that can be closer to circular. The real question is what this pattern of pluses and minuses means for the kinds of stimulus that this cell likes best. What I hope is fairly clear from looking at this pattern is that this V1 cell is going to like something like a line that is tilted at a particular angle. Another way to say that is to say that this cells has a preference for a specific orientation of a line, or that it is tuned to that orientation. Other cells in V1 will have receptive fields like this, too, meaning that there are cells in V1 that respond to lines, edges, and bars tilted at different orientations. Beyond the spots of lights that are being measured in the LGN, V1 appears to be measuring something more complex: Boundaries that can bend to follow the contours of objects and surfaces. These cells, unlike those that we found in the LGN, are capable of not just telling us that they’ve found a place where the image changes but also telling us about the specific shape of that border between light and dark parts of a picture.

I hope it’s fairly clear that all this is just a by-product of having a different pattern of excitatory and inhibitory regions inside of a receptive field. So far, we haven’t introduced any kind of new computation: To predict what a V1 cell does in response to a picture, we’d use the same tools that we established for the LGN – we’d need a description of the image in terms of pixel intensities, a description of the receptive field in terms of excitatory and inhibitory regions, and we’d need to take a dot product between those two things to calculate a response. Doing this for a particular V1 cell, we can easily see how a cell with a preferred orientation responds to edges or lines that are oriented at different angles. The graph below shows you what we’d calculate in terms of a response for such a cell as we rotate a line that it likes a lot away from its preferred orientation (Figure 2). We call this graph the tuning curve for a cell.

Figure 2 - An orientation tuning curve for a V1 cell. This cell prefers vertical orientations, but has intermediate responses to lines near that degree of tilt.

Because all of this is stuff we’ve seen before, we’re going to concentrate on something new: How do we put information from multiple V1 cells together? We’re going to think about this in two different ways using two different kinds of computation to do so. In the first case, we’re going to explore how to use population coding to use a relatively small number of different kinds of cell to measure line/edge orientation precisely. In the second case, we’re going to see how to use logic gates to combine the outputs of small groups of V1 cells to achieve something called invariance to certain transformations of the image. Both of these are important ways to measure things we’d like to know about images, and both of them involve using a set of simple measurements to do something more complex. This is going to be a continuing theme as we move through the visual system: How can we keep elaborating on the measurements we’re making to achieve goals that are more and more useful/interesting? We’ll begin by talking about listening to populations of cells in V1.