Abstract
Neural networks started to be developed around the idea of creating models of real neural systems because of the existing need in understanding how biological systems work. New areas of neural computing are trying to make at least one step beyond what digital computing means. The key point is based on learning rather than programming. We designed a mathematical model for information processing. The neuron model is viewed from inside as a feedback system that controls the information flow. The process of learning at ”molecular” level (internal neural learning), under the form of a computational algorithm inspired by a real brain functioning, has been introduced.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Morogan, L.M.: Prion Neural Systems, Why Choosing a Hybrid Approach of Natural Computing? In: Elefterakis, G., et al. (eds.) 4th Annual South-East European Doctoral Student Conference on Infusing Research and Knowledge in South-East Europe, Thessaloniki, vol. 1, pp. 381–391 (2009)
Morogan, L.M.: Coding Information in New Computing Models. DNA Computing and Membrane Computing. In: Analele Universitatii Spiru Haret, vol. 2, pp. 59–73. FRM, Bucharest (2006)
Maass, W.: Networks of Spiking Neurons: The Third Generation of Neural Network Models. Neural Networks 10(9), 1659–1671 (1997)
Maass, W., Schmitt, M.: On the Complexity of Learning for Spiking Neurons with Temporal Coding. Information and Computation 153(1), 26–46 (1999)
Maass, W., Natschlager, T.: Associative Memory with Networks of Spiking Neurons in Temporal Coding. In: Smith, L.S., Hamilton, A. (eds.) Neuromorphic Systems: Engineering Silicon from Neurobiology, pp. 21–32. World Scientific, Singapore (1998)
Natschlaeger, T.: Efficient Computation in Networks of Spiking Neurons - Simulations and Theory. Ph.D. thesis, Institute for Theoretical Computer Science, Technische Universitaet Graz, Austria (1999)
Natschlager, T., Maass, W.: Finding the Key to a Synapse. In: Leen, T.K., et al. (eds.) Advances in Neural Information Processing Systems. NIPS 2000 Conference, vol. 13, pp. 138–145. MIT Press, Cambridge (2001)
Ghazali, R., Mohd Nawi, N., Mohd Salikon, M.Z.: Forecasting the UK/EU and JP/UK Trading Signals Using Polynomial Neural Networks. International Journal of Computer Information Systems and Industrial Management Applications 1, 110–117 (2009)
Ciarelli, P.M., Oliveira, E., Badue, C., De Souza, A.F.: Multi-Label Text Categorization Using a Probabilistic Neural Network. International Journal of Computer Information Systems and Industrial Management Applications 1, 133–144 (2009)
Morogan, L.M., Barza, S.: Algoritmica Grafurilor. FRM, Bucharest (2008)
Alberts, B., Johnson, A., Lewis, J., Raff, M., Roberts, K., Walter, P.: Molecular Biology of the Cell, 4th edn. Garland Science, New York (2002)
Benga, G.: Biologia Moleculara a Membranelor cu Aplicatii Medicale. Dacia, Cluj-Napoca (1979)
Israil, A.M.: Biologie Moleculara. Prezent si Perspective. Humanitas, Bucharest (2000)
Morogan, L.M.: Prion Neural Systems: Synaptic Level. In: Psychogios, A., et al. (eds.) 5th Annual South-East European Doctoral Student Conference on Infusing Research and Knowledge in South-East Europe, Thessaloniki, pp. 436–444 (2010)
Morogan, L.M.: Prion Neural System: Modeling the Binding Affinities Between Neurons of a Network. In: Abraham, A., et al. (eds.) Proceeding of International Conference on Computer Information Systems and Industrial Management Applications with Applications to Ambient Intelligence and Ubiquitous Systems, pp. 143–147. IEEE Press, Krakow (2010)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Morogan, L. (2011). View from Inside One Neuron. In: Snasel, V., Platos, J., El-Qawasmeh, E. (eds) Digital Information Processing and Communications. ICDIPC 2011. Communications in Computer and Information Science, vol 188. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-22389-1_17
Download citation
DOI: https://doi.org/10.1007/978-3-642-22389-1_17
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-22388-4
Online ISBN: 978-3-642-22389-1
eBook Packages: Computer ScienceComputer Science (R0)