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The mammalian brain has evolved specifically to cope with changing environments. Confronted with a novel situation, the mammalian brain can plan appropriate actions based not on instinct, but rather on a general cognitive plan - something we call 'thinking.'
The main goal of our laboratory is to understand, on a cellular level, how the brain represents information, and how single cells and networks can process that information to dynamically perform simple behavioral computations. Many theories exist on how the brain represents and combines information - some suggest that individual cells act as individual storage elements arrayed in hierarchical processing streams, others suggest information is coded in the temporal correlations among sparse, distributed networks of cells. To approach these questions, we must understand (1) how information is represented in single cells, i.e. what is the "neural code" (2) what is physically modified in the brain when something is "learned", and (3) how does changing the firing of single units affect perception, or change behavioral output of the brain.
Following are some of the main specific interests of the laboratory: 
Because many elements of cortical architecture are conserved across the whole cerebral cortex, it is reasonable to assume that the basic principles of cortical function apply equally well throughout the cortex. In many mammals, primary visual cortex provides an excellent place to study cortical circuitry due to (1) the ease of providing patterned input (2) the ease of accessibility for recording and imaging methods. Our method is to study patterns of cortical functioning in either normal or perturbed systems by single-unit recording, or imaging methods, in order to learn how these patterns come about and how they can be altered.
The output of a cortical cell can be thought of as a function of the relative balance of excitatory and inhibitory inputs to that cell. Using visual cortex as a model, we showed that pharmacologically blocking the inhibitory inputs to a single cell altered the rate of firing, but did not alter the basic response properties of that cell. By blocking inhibitory activity across many cells in a small area of cortex, we showed that although the activity of those cells substantially increased, the response properties in neighboring areas were only slightly affected. These results argue for a distinction between types of cortical 'computation' that are hard-wired, and those that are malleable depending on input activity.
Primary visual cortex is the first level of the visual system where information such as color, orientation, etc. is represented by spatially separated groups of cells. Experiments such as these allow us to examine the importance of local connections and the spatial layout of response properties on visual function.
By studying differences between equivalent circuitry across multiple mammalian species (including humans) we can gain insight into the evolutionary origins of cortical circuitry. Two examples of recent comparative projects are the following:
(1) Cat primary visual cortex has the largest hypercolumn dimensions in any species yet studied. In a graph of spacing verses area of V1, the frequency of iso-orientation domains is roughly half what it should be compared with other animals. Why should this be the case? Is it a clue that some other system of organization is different in the cat?
(2) Mice lack many of the functional structures observed in V1 of other mammals. Mice are rodents that have relatively poor eyesight. Carnivores and primates have well developed spatial organization in V1, but also have excellent eyesight. Is the functional organization of V1 a result of difference in the quality of visual input, or of genetics? To answer this question, we examined the organization of V1 in the squirrel - a rodent with relatively good eyesight. Squirrels were found not to have spatially segregated properties suggesting that it is largely a matter of genetics. Interestingly, this result also suggests that cells do not need to be spatially organized for the animal to have a full repertoire of visual behavior.
An important goal of the brain is to choose appropriate behavioral responses based on changing sensory inputs. We are interested in how the brain chooses a behavioral response. To make this problem tractable, we designed an experiment where subjects had to respond either left or right based on a visual cue that was one of two colors and in one of two locations. In half the trials the cue color indicated which was the correct response to make, and in the other half of trials cue location indicated the correct response to make. Areas of the brain were found that coded for differences in cue color only when color was the behaviorally relevant feature but not when location was the relevant feature. Furthermore, some areas of the brain code specifically for which 'rule' is relevant at a given time. These questions can be studied both at a single cell level, and also at a systems level using fMRI. We hope to delineate a behavioral 'circuit' that allows us to understand how the brain is able to map instantaneously and arbitrarily between inputs and behavioral outputs. We hope experiments such as these will help describe the processes behind what is commonly referred to as 'thinking'.
Single-unit recording: By recording from single cells using microelectrodes, the outputs of single cells can be recorded, correlated with a given task or input, and analyzed for what information is contained and how it is represented. Cells can subsequently be localized anatomically using tracer injections visualized microscopically in thin sections.
Optical imaging: Using a sensitive camera, we can image either changes in the reflectance of brain tissue that correlates with activity, or changes in fluorescent tracers applied to the brain. These techniques allow us to map various neuronal properties across the surface of cortex. Using fluorescent dyes, we can map anatomical connectivity, patterns of blood flow, or brain activity using dyes that indicate voltage across cell membranes. We can also measure intrinsic signals that allow us to get spatial maps of neuronal activity.
MRI: We are closely affiliated with the Center for Biological Imaging, which houses a 3-Tesla magnet specifically for research use. Both humans and animals can be imaged noninvasively to obtain maps of function, connectivity, chemical content, anatomy, blood flow, and many other modalities. We are interested in pushing the limits of current techniques in MRI for neuroscience.
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