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Packets of Sequential Neural Activity in Sensory Cortex

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Analysis and Modeling of Coordinated Multi-neuronal Activity

Part of the book series: Springer Series in Computational Neuroscience ((NEUROSCI,volume 12))

Abstract

Either spontaneously or in response to stimuli, neurons are active in a coordinated fashion. For example, an onset response to sensory stimuli usually evokes a 50–200 ms long burst of population activity. In this chapter, we summarize recent papers of the author showing that such bursts of neuronal activity are not randomly organized, but rather composed of stereotypical sequential spiking patterns. To underline this fine-scale internal organization of such population bursts, we will refer to them as “packets.” It has been shown that packets are ubiquitous feature of spontaneous and stimulus-evoked network activity and are present across different brain states. Although these packets have a generally conserved sequential spiking structure, the exact timing and number of spikes fired by each neuron within a packet can be modified depending on the stimuli. In this chapter, we provide a detailed description of packets, and we discuss how the packet-like organization of neuronal activity may provide an explanation for multiple puzzling observations about neuronal coding. It is interesting to note that organizing population activity into packets resembles how engineers designed information transfer over Internet, where information is divided in small, formatted network packets to increase communication efficiency and reliability.

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Authors and Affiliations

  1. Department of Neuroscience, University of Lethbridge, 4401 University Dr., Lethbridge, AB, Canada, T1K 3M4

    Artur Luczak Ph.D.

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  1. Artur Luczak Ph.D.
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Correspondence to Artur Luczak Ph.D. .

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  1. The University of Lethbridge, Lethbridge, Alberta, Canada

    Masami Tatsuno

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Luczak, A. (2015). Packets of Sequential Neural Activity in Sensory Cortex. In: Tatsuno, M. (eds) Analysis and Modeling of Coordinated Multi-neuronal Activity. Springer Series in Computational Neuroscience, vol 12. Springer, New York, NY. https://doi.org/10.1007/978-1-4939-1969-7_8

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