In cognitive neuroscience, (stimulus-dependent) (phase-)synchronous oscillations of neuron populations serve to solve the general "[[binding problem]]". According to the so-called "Binding-By-Synchrony (BBS) Hypothesis"<ref>Singer, W. (1999). Neuronal synchrony: A versatile code for the definition of relations. Neuron, 24, 49-65.</ref><ref>Singer, W. (1999a). Binding by neural synchrony. In R. A. Wilson & F. C. Keil (eds.): The MIT encyclopedia of the cognitive sciences (pp. 81-84). Cambridge, MA, London: The MIT Press.</ref><ref>Singer, W. (2009a). Consciousness and neuronal synchronization. In S. Laureys & G. Tononi: The neurology of consciousness: Cognitive neuroscience and neuropathology (pp. 43-52). Amsterdam: Elsevier.</ref><ref>Singer, W. (2009b). Neural synchrony and feature binding. In L.R. Squire (Ed.) Encyclopedia of Neuroscience. Vol. 6 (pp. 253-259). Oxford: Academic Press.</ref><ref>Singer, W. (2013a). The neuronal correlate of consciousness: Unity in time rather than space? Neurosciences and the Human Person: New Perspectives on Human Activities Pontifical Academy of Sciences. Scripta Varia. Vol. 121. Vatican City. 2013. From: www.casinapioiv.va/content/dam/accademia/pdf/sv121/sv121-singer.pdf</ref><ref>Singer, W. (2013b). Cortical dynamics revisited. Trends in Cognitive Sciences 17, 616-626.</ref><ref>Singer, W. (2018). Neuronal oscillations: unavoidable and useful? European Journal of Neuroscience 48, 2389-2399.</ref> a precise temporal correlation between the impulses of neurons ("cross-correlation analysis"<ref>Engel, A. K., König, P., Gray, C. M. & Singer, W. (1990). Stimulus-dependent neuronal oscillations in cat visual cortex: Intercolumnar interaction as determined by cross-correlation analysis. European Journal of Neuroscience, 2, 588-606.</ref>) and thus a stimulus-dependent temporal synchronization of the coherent activity of subpopulations of neurons emerges. Moreover, this synchronization mechanism circumvents the "''superposition problem"''<ref>Malsburg, C. von der (1999). The what and why of binding: The modeler's perspective. Neuron, 24, 95-104.</ref> by more effectively identifying the signature of synchronous neuronal signals as belonging together for subsequent (sub-)cortical information processing areas.
