Volume 14 Supplement 1

Abstracts from the Twenty Second Annual Computational Neuroscience Meeting: CNS*2013

Open Access

Information transmission efficiency in neuronal communication systems

  • Bartosz Paprocki1,
  • Janusz Szczepanski1, 2 and
  • Dorota Kolbuk2
BMC Neuroscience201314(Suppl 1):P217

https://doi.org/10.1186/1471-2202-14-S1-P217

Published: 8 July 2013

The nature and efficiency of brain transmission processes, its high reliability and efficiency is one of the most elusive area of contemporary science [1]. We study information transmission efficiency by considering a neuronal communication as a Shannon-type channel. Thus, using high quality entropy estimators, we evaluate the mutual information between input and output signals. We assume model of neuron proposed by Levy and Baxter [2], which incorporates all essential qualitative mechanisms participating in neural transmission process. We analyze how the synaptic failure, activation threshold and characteristics of the input source affect the efficiency. Two types of network architectures are considered. We start by a single-layer feedforward network and next we study brain-like networks which contains components such as excitatory and inhibitory neurons or long-range connections. It turned out that, especially for lower activation thresholds, significant synaptic noise can lead even to twofold [Figure 1] increase of the transmission efficiency [3]. Moreover, the more amplifying the amplitude fluctuation is, the more positive is the role of synaptic noise [4]. Our research also shows that all brain-like network components, in broad range of conditions, significantly improve the information-energetic efficiency. It turned out that inhibitory neurons can improve the information-energetic transmission efficiency by 50 percent, while long-range connections can improve the efficiency even by 70 percent. The knowledge of the effects of the long-range connections could be particulary useful when we consider possible reconstruction or support of them applying biomaterials [5, 6]. We also showed that the most effective is the network with the smallest size: we found that two times increase of the size can cause even three times decrease of the information-energetic efficiency [7].
Figure 1

Mutual information dependency on synaptic success, s , in single-layer neural network. Maximal mutual information values (dotted line) and these achieved at s = 1 (solid). Size of a given dot is proportional to 1−s, indicating the bigger the dot, the corresponding mutual information value is achieved at lower s [3].

Declarations

Acknowledgements

This paper has been supported by Polish National Science Centre Grant N N519 646540.

Authors’ Affiliations

(1)
Institute of Mechanics and Applied Computer Sciences, Kazimierz Wielki University
(2)
Institute of Fundamental Technological Research, Polish Academy of Sciences

References

  1. van Hemmen JL, Sejnowski T: 23 problems in Systems Neurosciences. Oxford University. 2006Google Scholar
  2. Levy W, Baxter R: Energy-efficient neuronal computation via quantal synaptic failures. Journal of Neuroscience. 2002, 22: 4746-4755.PubMedGoogle Scholar
  3. Paprocki B, Szczepanski J: Efficiency of neural transmission as a function of synaptic noise, threshold, and source characteristics. Biosystems. 2011, 105: 62-72. 10.1016/j.biosystems.2011.03.005.View ArticlePubMedGoogle Scholar
  4. Paprocki B, Szczepanski J: How do the amplitude fluctuations affect the neuronal transmission efficiency. Neurocomputing. 2013, 104: 50-56.View ArticleGoogle Scholar
  5. Kołbuk D, Sajkiewicz P, Kowalewski TA: Optical birefringence and molecular orientation of electrospun polycaprolactone fibers by polarizing-interference microscopy. European Polymer Journal. 2012, 48: 275-283. 10.1016/j.eurpolymj.2011.11.012.View ArticleGoogle Scholar
  6. Hu A, Zuo B, Zhang F, Lan Q, Zhang H: Electrospun silk fibroin nanofibers promote Schwann cell adhesion, growth and proliferation. Neural Regeneration Research. 2012, 7 (15): 1171-1178.PubMed CentralPubMedGoogle Scholar
  7. Paprocki B, Szczepanski J: Transmission efficiency in the brain-like neuronal networks. Information and energetic aspects. Neural Coding Workshop. 2012, PragueGoogle Scholar

Copyright

© Paprocki et al; licensee BioMed Central Ltd. 2013

This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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