Abstract
Dynamic model of a recurrent neuron with a sigmoidal activation function is considered. It is shown that with the presence of a modulation parameter its activation characteristic (dependence between input pattern and output signal) varies from a smooth sigmoid-like function to the form of a quasi-rectangular hysteresis loop. We demonstrate how a gateway element can be build using a structure with two recognizing neurons and one output neuron. It is shown how its functional properties change due to changes in the value of the modulation parameter. Such gateway element can take the output value based on a weighted sum of signals from the recognizing neurons. On the other hand it can perform a complex binary-like calculation with the input patterns. We demonstrate that in this case it can be used as a coincidence detector even for disjoint-in-time patterns. Futhermore, under certain extreme conditions it can be triggered even if only the one input pattern was recognized. Also the results of numerical simulations presented and some directions for further development suggested.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Ditzler, G., Roveri, M., Alippi, C., Polikar, R.: Learning in nonstationary environments: a survey. IEEE Comput. Intell. Mag. 10(4), 12 (2015). doi:10.1109/MCI.2015.2471196
Gepperth, A., Hammer, B.: Incremental learning algorithms and applications. In: European Symposium on Artificial Neural Networks (ESANN) (2016)
Prostov, Y.S., Tiumentsev, Y.V.: Multimodal associative neural network with context-dependent adaptation. In: Abstracts of the 12th International Conference “Aviation and Cosmonautics - 2013”, MAI (NRU), Moscow, pp. 619–620 (2013). (in Russian)
Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9(8), 1735 (1997). doi:10.1162/neco.1997.9.8.1735
Krasnosel’skii, M.A., Pokrovskii, A.V.: Systems with Hysteresis. Springer Science & Business Media, Heidelberg (2012)
Prostov, Y.S., Tiumentsev, Y.V.: A study of neural network model composed of hysteresis microensembles. In: Proceedings of XVII All-Russian Scientific Engineering and Technical Conference “Neuroinformatics-2015”, vol. 1, NRNU MEPhI, Moscow, pp. 116–126. (2015). (in Russian)
Prostov, Y.S., Tiumentsev, Y.V.: A hysteresis micro ensemble as a basic element of an adaptive neural net. Opt. Mem. Neural Netw. 24(2), 116 (2015). doi:10.3103/S1060992X15020113
Koulakov, A.A., Raghavachari, S., Kepecs, A., Lisman, J.E.: Model for a robust neural integrator. Nat. Neurosci. 5(8), 775–782 (2002). doi:10.1038/nn893
Brody, C.D., Romo, R., Kepecs, A.: Basic mechanisms for graded persistent activity: discrete attractors, continuous attractors, and dynamic representations. Curr. Opin. Neurobiol. 13(2), 204–211 (2003). doi:10.1016/S0959-4388(03)00050-3
Zeeman, E.C.: Catastrophe Theory: Selected Papers, 1972–1977. Addison-Wesley, Reading (1977)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer International Publishing AG
About this paper
Cite this paper
Prostov, Y.S., Tiumentsev, Y.V. (2018). Adaptive Gateway Element Based on a Recurrent Neurodynamical Model. In: Kryzhanovsky, B., Dunin-Barkowski, W., Redko, V. (eds) Advances in Neural Computation, Machine Learning, and Cognitive Research. NEUROINFORMATICS 2017. Studies in Computational Intelligence, vol 736. Springer, Cham. https://doi.org/10.1007/978-3-319-66604-4_5
Download citation
DOI: https://doi.org/10.1007/978-3-319-66604-4_5
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-66603-7
Online ISBN: 978-3-319-66604-4
eBook Packages: EngineeringEngineering (R0)