Abstract
Implementing a deadlock-breaking neural computing scheme that can flexibly search for reasonable solutions without any resource allocation program.
Supplemental Material
Available for Download
Requires Asian Language Support in Adobe Reader and Japanese Language Support in Your Browser.
- Aihara, K., Takabe, T., and Toyoda, M. Chaotic neural networks. Physics Letters A 144, 6/7 (1990), 333--340.Google ScholarCross Ref
- Aono, M. and Gunji, Y.-P. Beyond input-output computings: Error-driven emergence with parallel non-distributed slime mold computer. BioSystems 71 (2003), 257--287.Google ScholarCross Ref
- Aono, M. and Hara, M. Amoeba-based nonequilibrium neurocomputer utilizing fluctuations and instability. In S.G. Aki et al, Eds., UC 2007, LNCS 4618, Springer-Verlag, Berlin (2007), 41--54. Google ScholarDigital Library
- Arbib, M.A., Ed. The Handbook of Brain Theory and Neural Networks (2nd Edition). The MIT Press, Cambridge, MA, 2003. Google ScholarDigital Library
- Hasegawa, M., Ikeguchi, T., and Aihara, K. Combination of chaotic neurodynamics with the 2-opt algorithm to solve traveling salesman problems. Physics Review Letters 79, 12 (1997), 2344--2347.Google ScholarCross Ref
- Hopfield, J.J. and Tank, D.W. Computing with neural circuits: A model. Science 233, (1986), 625--633.Google ScholarCross Ref
- Nakagaki, T., Yamada, H., and Toth, A. Maze-solving by an amoeboid organism. Nature 407 (2000), 470.Google ScholarCross Ref
- Takamatsu, A. Spontaneous switching among multiple spatio-temporal patterns in three-oscillator systems constructed with oscillatory cells of true slime mold. Physica D 223 (2006), 180--188.Google ScholarCross Ref
Index Terms
- Amoeba-based neurocomputing with chaotic dynamics
Recommendations
Amoeba-based Chaotic Neurocomputing: Combinatorial Optimization by Coupled Biological Oscillators
AbstractWe demonstrate a neurocomputing system incorporating an amoeboid unicellular organism, the true slime mold Physarum, known to exhibit rich spatiotemporal oscillatory behavior and sophisticated computational capabilities. Introducing optical ...
Neural learning of chaotic dynamics
AbstractIn recent years, considerable progress has been made in modeling chaotic time series with neural networks. Most of the work concentrates on the development of architectures and learning paradigms that minimize the prediction error. A more detailed ...
On the dynamics of fractional q-deformation chaotic map
Highlights- A novel fractional q-deformation chaotic map is constructed.
- Chaotic attractor ...
AbstractIn this paper, the dynamical behaviors of fractional q-deformation chaotic map are analyzed. Firstly, the fractional q-deformation chaotic map is proposed by employing the Caputo delta difference operator. Secondly, the rich dynamical ...
Comments