Pattern separation by adaptive networks: neurogenesis in olfaction
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1
Northwestern University, United States
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2
Northwestern University, Applied Mathematics, United States
A characteristic aspect of early processing of sensory information by neuronal circuits is a reshaping of activity patterns that may facilitate further processing in the brain. For instance, in the olfactory system the activity patterns that related odors evoke at the input of the olfactory bulb can be highly similar; nevertheless, the corresponding activity patterns of the mitral cells, which represent the output of the olfactory bulb, can differ significantly from each other due to strong inhibition by granule cells and peri-glomerular cells [1]. Due to the high dimensionality of ‘odor space’ the activation patterns that need to be separated are very complex. This presumably requires bulbar network connectivities that are more complex than those generating the center-surround receptive fields in the retina. We therefore investigate to what extent adaptive inhibitory networks can learn to perform pattern separation, i.e. to enhance the difference between similar patterns. Considering simple firing-rate models, we first present general biophysical considerations that can constrain the ability of networks to learn pattern separation. Then we investigate to what extent adult neurogenesis, as it is observed in the olfactory bulb, can provide a learning mechanism for this task. The stimuli that an animal needs to discriminate are typically sensed at quite different times. Without access to substantial memory it is therefore difficult for the network to learn the connectivity based on the similarity of different stimuli; biologically it is more plausible that learning is driven by simultaneous correlations between the input channels. We investigate the connection between pattern separation and channel decorrelation and demonstrate that networks can achieve effective pattern separation through channel decorrelation if they simultaneously equalize their output levels. In feedforward networks biophysically plausible learning mechanisms fail, however, for even moderately similar input patterns. Recurrent networks do not have that limitation. Even when the connectivity of the recurrent networks is optimized for linear neuronal dynamics they perform very well when the dynamics are nonlinear [2]. Even in adult animals new inhibitory interneurons are persistently incorporated into the bulbar network; less than 50%of them survive, however, in the long term. Since their survival rate depends on the odor exposure of the animal and on behavioral tasks it may perform, this adult neurogenesis may provide an efficient mechanism to restructure the bulbar network to adapt it to the challenges presented to the animal by the olfactory environment. Consistent with this, adult neurogenesis has been found to be correlated with the animal’s performance in odor discrimination tasks. In our model new interneurons are integrated persistently into the network and subsequently removed depending on their activity. The networks resulting from this training procedure are able to separate even quite similar stimuli. As observed experimentally, we find that young neurons are more responsive to novel odors.
References
1. R.W. Friedrich, G. Laurent, "Dynamic Optimization of Odor Representations by Slow Temporal Patterning of Mitral Cell Activity", Science 291 (2001) 889.
2. S.D.Wick, M.T. Wiechert, R.W. Friedrich, H. Riecke, "Pattern orthogonalization via channel decorrelation by adaptive networks", J. Comp. Neurosci. (2009).
Conference:
Computational and Systems Neuroscience 2010, Salt Lake City, UT, United States, 25 Feb - 2 Mar, 2010.
Presentation Type:
Poster Presentation
Topic:
Poster session II
Citation:
Chow
SF,
Wick
SD and
Riecke
H
(2010). Pattern separation by adaptive networks: neurogenesis in olfaction.
Front. Neurosci.
Conference Abstract:
Computational and Systems Neuroscience 2010.
doi: 10.3389/conf.fnins.2010.03.00188
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Received:
03 Mar 2010;
Published Online:
03 Mar 2010.
*
Correspondence:
Hermann Riecke, Northwestern University, Applied Mathematics, Evanston, IL, United States, h-riecke@northwestern.edu