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ABSTRACT: Cortical function is regulated by a strikingly diverse array of local-circuit inhibitory neurons. We evaluated how optogenetically activating somatostatin- and parvalbumin-positive interneurons subtractively or divisively suppressed auditory cortical cells' responses to tones. In both awake and anesthetized animals, we found that activating either family of interneurons produced mixtures of divisive and subtractive effects and that simultaneously recorded neurons were often suppressed in qualitatively different ways. A simple network model shows that threshold nonlinearities can interact with network activity to transform subtractive inhibition of neurons into divisive inhibition of networks, or vice versa. Varying threshold and the strength of suppression of a model neuron could determine whether the effect of inhibition appeared divisive, subtractive, or both. We conclude that the characteristics of response inhibition specific to a single interneuron type can be "masked" by the network configuration and cellular properties of the network in which they are embedded.

SUBMITTER: Seybold BA

PROVIDER: S-EPMC4635400 | biostudies-literature | 2015 Sep

REPOSITORIES: biostudies-literature

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Inhibitory Actions Unified by Network Integration.

Seybold Bryan A BA Phillips Elizabeth A K EAK Schreiner Christoph E CE Hasenstaub Andrea R AR

Neuron 20150901 6

Cortical function is regulated by a strikingly diverse array of local-circuit inhibitory neurons. We evaluated how optogenetically activating somatostatin- and parvalbumin-positive interneurons subtractively or divisively suppressed auditory cortical cells' responses to tones. In both awake and anesthetized animals, we found that activating either family of interneurons produced mixtures of divisive and subtractive effects and that simultaneously recorded neurons were often suppressed in qualita ...[more]

PMID: 26402602

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Project description:High-throughput multi-omics studies and corresponding network analyses of multi-omic data have rapidly expanded their impact over the last 10 years. As biological features of different types (e.g. transcripts, proteins, metabolites) interact within cellular systems, the greatest amount of knowledge can be gained from networks that incorporate multiple types of -omic data. However, biological and technical sources of variation diminish the ability to detect cross-type associations, yielding networks dominated by communities comprised of nodes of the same type. We describe here network building methods that can maximize edges between nodes of different data types leading to integrated networks, networks that have a large number of edges that link nodes of different-omic types (transcripts, proteins, lipids etc). We systematically rank several network inference methods and demonstrate that, in many cases, using a random forest method, GENIE3, produces the most integrated networks. This increase in integration does not come at the cost of accuracy as GENIE3 produces networks of approximately the same quality as the other network inference methods tested here. Using GENIE3, we also infer networks representing antibody-mediated Dengue virus cell invasion and receptor-mediated Dengue virus invasion. A number of functional pathways showed centrality differences between the two networks including genes responding to both GM-CSF and IL-4, which had a higher centrality value in an antibody-mediated vs. receptor-mediated Dengue network. Because a biological system involves the interplay of many different types of molecules, incorporating multiple data types into networks will improve their use as models of biological systems. The methods explored here are some of the first to specifically highlight and address the challenges associated with how such multi-omic networks can be assembled and how the greatest number of interactions can be inferred from different data types. The resulting networks can lead to the discovery of new host response patterns and interactions during viral infection, generate new hypotheses of pathogenic mechanisms and confirm mechanisms of disease.

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Inhibitory Actions Unified by Network Integration.

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Inhibitory Actions Unified by Network Integration.

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