Abstract
We proposed a neural segmentation model that is suitable for implementation in analog VLSIs using conventional CMOS technology. The model consists of neural oscillators mutually couple through synaptic connections. The model performs segmentation in temporal domain, which is equivalent to segmentation according to the spike timing difference of each neuron. Thus, the learning is governed by symmetric spike-timing dependent plasticity (STDP). We numerically demonstrate basic operations of the proposed model as well as fundamental circuit operations using a simulation program with integrated circuit emphasis (SPICE).
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.
References
Han, S.K., Kim, W.S., Kook, H.: Temporal segmentation of the stochastic oscillator neural network. Physical Review E 58, 2325–2334 (1998)
von der Malsburg, Ch., Buhmann, J.: Sensory segmentation with coupled neural oscillators. Biological Cybernetics 67, 233–242 (1992)
von der Malsburg, Ch., Schneider, W.: A neural cocktail-party processor. Biological Cybernetics 54, 29–40 (1986)
Wang, D.L., Terman, D.: Locally excitatory globally inhibitory oscillator networks. IEEE Trans. on Neural Networks 6(1), 283–286 (1995)
Ando, H., Morie, T., Nagata, M., Iwata, A.: An Image Region Extraction LSI Based on a Merged/Mixed-Signal Nonlinear Oscillator Network Circuit. In: European Solid-State Circuits Conference (ESSCIRC 2002), Italy, September 2002, pp. 703–706 (2002)
Reichardt, W.: Principles of Sensory Communication, p. 303. Wiley, New York (1961)
Asai, T., Kanazawa, Y., Hirose, T., Amemiya, Y.: Analog reaction-diffusion chip imitating the Belousov-Zhabotinsky reaction with Hardware Oregonator Model. International Journal of Unconventional Computing 1(2), 123–147 (2005)
Tovar, G.M., Fukuda, S.E., Asai, T., Hirose, T., Amemiya, Y.: Neuromorphic CMOS circuits implementing a novel neural segmentation model based on symmetric STDP learning. In: 2007 International Joint Conference on Neural Networks, Florida, USA, August 12-17 (2007)
Asai, T., Ohtani, M., Yonezu, H.: Analog MOS circuits for motion detection based on correlation neural networks. Japanese Journal of Applied Physics 38(4B), 2256–2261 (1999)
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 2008 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Tovar, G.M., Fukuda, E.S., Asai, T., Hirose, T., Amemiya, Y. (2008). Analog CMOS Circuits Implementing Neural Segmentation Model Based on Symmetric STDP Learning. In: Ishikawa, M., Doya, K., Miyamoto, H., Yamakawa, T. (eds) Neural Information Processing. ICONIP 2007. Lecture Notes in Computer Science, vol 4985. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-69162-4_13
Download citation
DOI: https://doi.org/10.1007/978-3-540-69162-4_13
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-69159-4
Online ISBN: 978-3-540-69162-4
eBook Packages: Computer ScienceComputer Science (R0)