Decorrelation is a fundamental computation that optimizes the format of neuronal activity patterns. Channel decorrelation by adaptive mechanisms results in efficient coding, whereas pattern decorrelation facilitates the readout and storage of information. Mechanisms achieving pattern decorrelation, however, remain unclear. We developed a theoretical framework that relates high-dimensional pattern decorrelation to neuronal and circuit properties in a mathematically stringent fashion. For a generic class of random neuronal networks, we proved that pattern decorrelation emerges from neuronal nonlinearities and is amplified by recurrent connectivity. This mechanism does not require adaptation of the network, is enhanced by sparse connectivity, depends on the baseline membrane potential and is robust. Connectivity measurements and computational modeling suggest that this mechanism is involved in pattern decorrelation in the zebrafish olfactory bulb. These results reveal a generic relationship between the structure and function of neuronal circuits that is probably relevant for pattern processing in various brain areas.
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We thank Y.-P. Zhang and O. Fajardo for help with histological procedures, T. Oertner, B. Roska and J.M. Stix for comments on the manuscript, and members of the Friedrich laboratory for discussions. This work was supported by the Novartis Research Foundation, the Max-Planck-Society, the Alexander-von-Humboldt Foundation, the National Science Foundation (DMS-0719944 to H.R.), the European Union (IST-507610 to R.W.F.) and the Deutsche Forschungsgemeinschaft (SFB 488; FOR 643 to R.W.F.).
The authors declare no competing financial interests.
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Wiechert, M., Judkewitz, B., Riecke, H. et al. Mechanisms of pattern decorrelation by recurrent neuronal circuits. Nat Neurosci 13, 1003–1010 (2010). https://doi.org/10.1038/nn.2591
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