jump to navigation

Domain specificity follows from interactions between overlapping maps March 10, 2008

Posted by Johan in Face Perception, Neuroscience, Sensation and Perception, Theory.
1 comment so far
Object-face by Multitude

ResearchBlogging.orgI can’t simplify the title beyond that, but don’t run away yet, the idea itself is straight forward once the terminology is explained. Skip ahead two paragraphs if you know what domain specificity means.

Recognition of objects in the visual scene is thought to arise in inferior temporal and occipital cortex, along the ventral stream (see also this planned Scholarpedia article on the topic by Ungerleider and Pessoa – might be worth waiting for). That general notion is pretty much where consensus ends, with the issue of how different object categories are represented remaining controversial. Currently, the dominant paradigm is that of Nancy Kanwisher and colleagues, who hold that a number of domain-specific (that is, modular) areas exist, which each deal with the recognition of one particular object category. The most widely accepted among these are the fusiform face area (FFA), the parahippocampal place area (PPA), the occipital face area (OFA), the extrastriate body area (EBA), and the lateral occipital complex (LO), which is a bit of a catch-all region for the recognition of any object category that doesn’t fall into one of the domains with their own area. Usually, the face-selective part of the superior temporal sulcus (STS) is also included.

Typical locations of object areas by category. B is an upside down down, C is flattened

This modular view of the visual recognition has received a lot of criticism. However, the undeniable success of the functional localiser approach to fMRI analysis, in which responses are averaged across all voxels in each of the previously-mentioned areas, has led to widespread acceptance of the approach. Essentially, then, the domain specific account seems to be accepted because recording from a functionally-defined FFA, for instance, seems to yield results that make a lot of sense for face perception.

When you think about it, the domain specific account in itself is a pretty lousy theory of object recognition. It does map object categories onto cortex, but it is considerably more difficult to explain how such a specific representation might be built on input from earlier, non-object specific visual areas. This brings us to today’s paper, which proposes a possible solution (op de Beeck et al, 2008). The bulk of the paper is a review of previous research in this area, so give it a read for that reason if you want to get up to speed. The focus of this post is on the theoretical proposal that op de Beeck et al (2008) make towards the end of the paper, which goes something like this:

Ventral stream areas contain a number of overlapped and aligned topographical maps, where each maps encodes one functional property of the stimulus. Op de Beeck et al (2008) suggest that properties might include shape, functional connectivity, process, and eccentricity. Let’s go through each of those suggestions in turn (the following is based on my own ideas – op de Beeck et al don’t really specify how the topography of these featural maps might work):

A shape map might encode continuous variations of for instance angularity and orientation of parts of the stimulus. So one imaginary neuron in this map might be tuned to a sharp corner presented at an upright orientation (see Pasupathy & Connor, 2002 for an example of such tuning in V4), and topographically, the map might be laid out with angularity and curvature as the x and y dimensions in the simplest case.

Functional connectivity is hard to explain – read the article I just linked if you’re curious, but let’s just call it brain connectivity here. A map of brain connectivity is a topographical layout of connections to other areas – for instance, one part of the map might be more connected to earlier visual areas (such as V4), while another part of the map might connect more with higher-order areas that deal with memory or emotion (e.g., hippocampus, amygdala).

The process map is a tip of the hat to some of Kanwisher’s strongest critics, such as Tarr & Gauthier (2000), who argued that the ventral stream isn’t divided by object category, but by the visual processing that is used. So for example, the FFA is actually an area specialised for expert within-category discrimination of objects (faces or otherwise), which happens to appear face-specific because we have more experience with faces than with other categories. Some parts of the map might deal with such expertise discriminations, while others might deal with more general between-category classification.

Eccentricity is a fancy term for distance from the fixation point (ie, the fovea) in retinal coordinates. If you hold your finger slightly left of your fixation point and continue to move it left, you are increasing the eccentricity of the stimulus. Eccentricity and its complicated partner polarity (visual angle) reflect the two basic large-scale topographical principles in early visual areas, but such maps can be found throughout the visual system.

Incidentally, the eccentricity map is the only of these proposed maps for which there is currently good evidence in this part of the brain (Levy et al, 2001). The part that corresponds to the FFA has a foveal (or central) representation of the visual field, which makes sense considering that we tend to look directly at faces. Conversely, the PPA has a peripheral representation, as might be expected since most of us don’t spend much time fixating on the scenery.

The central proposal is that in an area such as the FFA, the face-specific response is actually the combination of the concurrent, aligned activation of a number of different maps. For example, the FFA might correspond to responses tuned to rounded shapes in the shape map, to input from earlier visual areas in the functional connectivity map, to expert within-category discrimination in the process map, and to a foveal (central) representation in the eccentricity map.

To really get the kind of strong domain-specificity that is observed, these maps must display multiplicative interactions – op de Beeck et al (2008) suggest that if their simultaneous activations were just added to make up the fMRI response, you wouldn’t get the strong selectivity that is observed (so by implication, less strict modularists could do away with the multiplicative bit and get a map that corresponds better to their view of ventral areas).

This is a pretty interesting idea, although wildly speculative. Note that with the exception of eccentricity, there really is very little evidence for this form of organisation. In other words, this theory is a theory not just in the scientific sense, but also in the creationist sense of the word. It definitely is an inspiring source of possible future experiments, however.

References
Levy, I., Hasson, U., Avidan, G., Hendler, T., & Malach, R. (2001). Center-periphery organization of human object areas. Nature Neuroscience, 4, 533-539. DOI: 10.1038/87490

Op de Beeck, H.P., Haushofer, J., Kanwisher, N.G. (2008). Interpreting fMRI data: maps, modules and dimensions. Nature Reviews Neuroscience, 9, 123-135. DOI: 10.1038/nrn2314

Pasupathy, A., & Connor, C.E. (2002) Population coding of shape in area V4. Nature Neuroscience, 5, 1332-1338. Link

Tarr, M.J., & Gauthier, I. (2000). FFA: a flexible fusiform area for subordinate-level visual processing automated by expertise. Nature Neuroscience, 3, 764-769. DOI: 10.1038/77666

Know your neuron: Grid cells March 3, 2008

Posted by Johan in Animals, Know your neuron, Navigation, Neuroscience.
2 comments

Know your neuron is a mini-series here at the Fan Club where we look at some of the curious response characteristics of neurons discovered through electrophysiology. We will work our way through the simple properties of cells that respond to spatial locations or head direction and on to increasingly abstract cells, which encode the identity of individuals or perhaps even the relation between actions of others and those of the self.

ResearchBlogging.orgGiven how straight-forward their response properties are, it is curious that grid cells were only discovered relatively recently (Hafting et al, 2005). According to one of my professors, this is simply because researchers didn’t put their rats in sufficiently large boxes to see the pattern previously, but who knows… The response properties of grid cells are perhaps best illustrated in a figure (this is from the excellent Scholarpedia article on Grid cells, written by two of their discoverers).

In this figure, the x and y coordinates correspond to the spatial location of a rat, which is running around freely inside a large box. The black lines in the left figure shows how this particular rat explored the box in a fairly haphazard manner. However, an electrode inserted in the rat’s subcortex picks up a signal that is anything but chaotic: the responses of said neuron are given as red dots in the left figure, while the right figure gives the firing rate distribution (ranging from blue for silent and red for the peak rate of responding). Although the rat is running about randomly, this neuron is responding in a grid, seemingly coming on an off in response to the animal’s spatial location.

This response pattern is similar regardless of the exact box, so the pattern does not reflect learning of the spatial layout of this box as such. Indeed, when the light is turned out, the grid cell continues to respond as before while the rat runs around in the dark (Hafting et al, 2005 – this is not terribly impressive when you consider how little rats use vision, however). Yet, at least some grid cells showed a response pattern that rotated 90 degrees with a corresponding shift in an external visual cue, so although the grid cells do not depend on the external environment, they do seem to take cues from it when available.

Grid cells are found in Entorhinal cortex of the rat, a region which provides the main input to the Hippocampus, long known as a centre for memory consolidation and spatial navigation. Thus, these cells are a prime candidate as building blocks for more complex spatial cells that are observed in the Hippocampus proper. Different grid cells in Entorhinal cortex appear to have partially overlapping response fields, so that a given location might produce a response in some grid cells but not others. There is also a topographical structure, where the size of the grid increases as one moves from the dorsomedial to the ventrolateral side of the area. Incidentally, this pattern matches a spatial scale increase of more complex cells in the Hippocampus proper.

The obvious implication is that by combining the output of a number of partially-overlapping grid cells, perhaps a cell with more complex spatial properties could be built. The next issue of Know your neuron is about one category of such neurons, which appear to respond only to a specific spatial location.

References
Hafting, T., Fyhn, M., Molden, S., Moser, M., & Moser, E.I. (2005). Microstructure of a spatial map in the entorhinal cortex. Nature, 436, 801-806. DOI: 10.1038/nature03721

Moser, E., & Moser, M.-B. (2007). Grid Cells. Scholarpedia, 2, 3394.

Follow

Get every new post delivered to your Inbox.