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Sensory Processing by Neuronal Networks

The brain receives information from the sensory periphery in the form of activation patterns across many parallel input channels, such as the photoreceptors in vision. An essential task of the neuronal circuits receiving this information is to extract from these input patterns information that is relevant to the animal. Thus, even at the very first processing stages the aim is not to transmit activation patterns - like the optical image on the retina - unchanged to subsequent circuits, but rather to reshape the inputs into output patterns that enable the downstream circuits to access, store, and process the relevant information more easily and effectively.

A familiar example of early reshaping of sensory information is the processing of luminance information in the retina. Rather than transmitting faithfully luminance information to the brain the retina extracts in particular luminance differences: spatial gradients and temporal changes in luminance. While in most situations absolute luminance values are of little significance for the behavior of an animal, large gradients typically indicate edges delineating objects, which could be predators or preys, and temporal changes likely signifiy motion. Figure 1 illustrates spatial contrast enhancement in the retina.

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Figure 1: Contrast enhancement in the retina illustrated with adjacent rectangles of homogeneous luminosity. a) When the rectangles are separated by black bars it is clear that the each rectangle has spatially homogeneous gray level. Given that the gray levels of adjacent rectangls are so similar one could imagine that it is difficult to recognize the edge separating them. b) Without the black bars, the edges are clearly visible because each rectangle appears to become brighter towards the left where it contacts a darker rectangle. This enhancement of the contrast between the rectangles is generated in the retina, which transmits the illusion of sharp edges to the brain.

Visual objects are typically delineated by edges; they are identified by comparing the luminance of neighboring pixels. This can be achieved by neuronal networks in which neurons that transmit information about neighboring pixels inhibit each other mutually. Since these neurons are physically close to each other corresponding network connectivity is the relatively simple topology of nearest-neighbor interaction. In fact, this contrast enhancement is somewhat akin to backward diffusion, which sharpens gradients.

We are interested in the processing of olfactory information. There the first processing is performed by the network of the olfactory bulb. It collects the information provided by the olfactory sensory neurons, which function as chemical sensors. Animal species differ in their number of different types of chemical sensors: flies have ~60, humans ~350, and mice and rats have ~1000 different receptor types. Thus, the input to the olfactory bulb consists of activation patterns across 60 to 1000 different channels. In analogy to the visual system one may expect that the olfactory bulb enhances the difference (contrast) between the activation patterns corresponding to similar odorants. Indeed, this is what is found in experiments on zebrafish (Fig.3B in [2]); while the input patterns for two odors can be very similar the corresponding output patterns of the olfactory bulb can be quite different. Thus, one function of the olfactory bulb appears to be the decorrelation of odor representations.

A drastic difference between the visual and the olfactory system is the character of the stimuli. The high dimension of ‘odor space’ results in very complex activation patterns (Fig.4 in [1]). To achieve contrast enhancement in these patterns it is not sufficient to compare neighboring ‘odor pixels’ (glomeruli). Thus, the olfactory network exhibits inhibitory connection across large distances, extending across a third of the whole olfactory bulb. How is the connectivity in this complex network organized? How does it arise? Given the propensity of the brain to adapt to the tasks at hand, it is quite natural to assume that the bulbar network can evolve and learn to separate similar odor stimuli that have significantly different meaning for the animal. In a general analysis [4] we have identified biophysical constraints for the ability of neuronal networks to learn to separate (orthogonalize) representations of similar stimuli.

A fascinating aspect of the olfactory bulb is that even in adult animals its network connectivity is persistently being restructured through neurogenesis. New neurons migrate into the bulb continuously. But more than half of them die within 2 weeks of their arrival. Significantly, it is observed that this survival depends on the odor environment that the animals are exposed to and it is found that if neurogenesis is suppressed the animals are less able to learn certain odor discrimination tasks[3]. Thus, neurogenesis appears to play an important role in the animals’ ability to adapt to their changing environment.

To investigate how neurogenesis can provide a learning mechanism for the animal we are developing very simple computational models of the olfactory bulb that incorporate what we think are the essential features of this process. In their simplest form these models consist of a fixed population of excitatory relay neurons (mitral cells) and an ever changing population of inhibitory interneurons (granule cells). New granule cells are added to the network randomly. Since their survival depends on how active they are in response to an ensemble of training odor stimuli (the ‘natural environment’), the network evolves toward a connectivity that is better able to decorrelate the stimuli in this training ensemble.

So far it is quite a mystery why in the olfactory bulb learning invokes the rather ‘expensive’ process of making new neurons only to discard most of them later; in almost all other brain areas learning results from a change in the connection strengths between persistent neurons. With our modeling efforts we hope to shed light on this and other fascinating aspects of olfactory processing.

References

[1] R. W. Friedrich and S. I. Korsching. Combinatorial and chemotopic odorant coding in the zebrafish olfactory bulb visualized by optical imaging. Neuron, 18:737, 1997.

[2] R. W. Friedrich and G. Laurent. Dynamic optimization of odor representations by slow temporal patterning of mitral cell activity. Science, 291:889, 2001.

[3] M. M. Moreno, Christiane Linster, Olga Escanilla, Joëlle Sacquet, Anne Didier, and Nathalie Mandairon. Olfactory perceptual learning requires adult neurogenesis. Proc Natl Acad Sci U S A, Oct 2009.

[4] S.D. Wick, M.T. Wiechert, R.W. Friedrich, and H. Riecke, Pattern orthogonalization via channel decorrelation by adaptive networks, J. Comp. Neurosci. (2009).




Last modified: December, 2010
Hermann Riecke (h-riecke at northwestern.edu)