Functional consequences of correlated excitatory and inhibitory conductances in cortical networks
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Neurons in the neocortex receive a large number of excitatory and inhibitory synaptic inputs. Excitation and inhibition dynamically balance each other, with inhibition lagging excitation by only few milliseconds. To characterize the functional consequences of such correlated excitation and inhibition, we studied models in which this correlation structure is induced by feedforward inhibition (FFI). Simple circuits show that an effective FFI changes the integrative behavior of neurons such that only synchronous inputs can elicit spikes, causing the responses to be sparse and precise. Further, effective FFI increases the selectivity for propagation of synchrony through a feedforward network, thereby increasing the stability to background activity. Last, we show that recurrent random networks with effective inhibition are more likely to exhibit dynamical network activity states as have been observed in vivo. Thus, when a feedforward signal path is embedded in such recurrent network, the stabilizing effect of effective inhibition creates an suitable substrate for signal propagation. In conclusion, correlated excitation and inhibition support the notion that synchronous spiking may be important for cortical processing.
KeywordsCorrelated conductances Synaptic integration Sparse coding Signal propagation
For helpful discussions we thank Yves Fregnac and Arvind Kumar, the latter also for his careful reading of the manuscript. We thank the reviewers for helpful suggestions. This work was supported by the German Federal Ministry of Education and Research (BMBF grant 01GQ0420 to BCCN Freiburg), by the German Research Council (DFG SFB-780), by the CNRS and the 6th RFP of the EU (grant no. 15879-FACETS).
- Abeles, M. (1991). Corticonics: Neural circuits of the cerebral cortex. Cambridge University Press.Google Scholar
- Brémaud, A., West, D. C., & Thomson, A. M. (2007). Binomial parameters differ across neocortical layers and with different classes of connections in adult rat and cat neocortex. Proceedings of the National Academy of Sciences of the United States of America, 104(35), 14134–14139.CrossRefPubMedGoogle Scholar
- Braitenberg, V., & Schüz, A. (1991). Cortex: Anatomy of the cortex: Statistics and geometry. Springer.Google Scholar
- Kremkow, J., Perrinet, L., Aertsen, A., Masson, G. S. (2008a). Functional properties of feed-forward inhibition. Proc NeuroComp 2008Google Scholar
- Kremkow, J., Perrinet, L., Baudot, P., Levy, M., Marre, O., Monier, C. et al. (2008b). Control of the temporal interplay between excitation and inhibition by the statistics of visual input: A V1 network modelling study. Vol. Soc. Neurosci. Abstr. (p. 366.5/II10).Google Scholar
- Kremkow, J., Perrinet, L., Masson, G. S., & Aertsen, A. (2009). Functional consequences of correlated excitation and inhibition on single neuron integration and signal propagation through synfire chains. Proceedings of the 32nd Göttingen Neurobiology Conference T26-6B.Google Scholar
- Marre, O., Baudot, P., Levy, M., & Frégnac, Y. (2005). High timing precision and reliability, low redundancy and low entropy code in V1 neurons during visual processing of natural scenes. Society for Neuroscience Abstracts, 31, 285.5.Google Scholar
- Povysheva, N. V., Gonzalez-Burgos, G., Zaitsev, A. V., Kröner, S., Barrionuevo, G., Lewis, D. A., & Krimer, L. S. (2006). Properties of excitatory synaptic responses in fast-spiking interneurons and pyramidal cells from monkey and rat prefrontal cortex. Cerebral Cortex, 16(4), 541–552.CrossRefPubMedGoogle Scholar
- Schrader, S., Morrison, A., & Diesmann, M. (2007). A composition machine for complex movements. Proceedings of the 31st Göttingen Neurobiology Conference TS18-1C.Google Scholar
- Thomson, A. M., West, D. C., Wang, Y., & Bannister, A. P. (2002). Synaptic connections and small circuits involving excitatory and inhibitory neurons in layers 2–5 of adult rat and cat neocortex: Triple intracellular recordings and biocytin labelling in vitro. Cerebral Cortex, 12(9), 936–953.CrossRefPubMedGoogle Scholar
- Yger, P., Bruderle, D., Eppler, J., Kremkow J., Pecevski, D., Perrinet, L., et al. (2009). NeuralEnsemble: Towards a meta-environment for network modeling and data analysis. Eight Göttingen Meeting of the German neuroscience society (pp. T26–4C). http://www.incm.cnrs-mrs.fr/LaurentPerrinet/Publications/Yger09gns.