neural networks

CFP: IEEE ALIFE 2017

IEEE ALIFE 2017

2017 IEEE Symposium on Artificial Life
http://www.ele.uri.edu/ieee-ssci2017/ALIFE.htm

as part of

2017 IEEE Symposium Series on Computational Intelligence (IEEE SSCI 2017)
Hilton Hawaiian Village Resort, Honolulu, Hawaii
Nov. 27-Dec. 1, 2017
http://www.ieee-ssci.org

Call for papers

Artificial Life is the study of the simulation and synthesis of living systems. In particular, this science of generalized living and life-like systems provides engineering with billions of years of design expertise to learn from and exploit through the example of the evolution of organic life on earth. Increased understanding of the massively successful design diversity, complexity, and adaptability of life is rapidly making inroads into all areas of engineering and the Sciences of the Artificial. Numerous applications of ideas from nature and their generalizations from life-as-we-know-it to life-as-it-could-be continually find their way into engineering and science.

IEEE ALIFE 2017 brings together researchers working on the emerging areas of Artificial Life and Complex Adaptive Systems, aiming to understand and synthesize life-like systems and applying bio-inspired synthetic methods to other science/engineering disciplines, including Biology, Robotics, Social Sciences, among others.
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ALIFE XV Late Breaking Abstract

Can we incorporate sleep-like interruptions into evolutionary robotics?

Mario A. Zarco-Lopez and Tom Froese

Traditional use of Hopfield networks can be divided into two main categories: (1) constraint satisfaction based on predefined a weight space, and (2) model induction based on a training set of patterns. Recently, Watson et al. (2011) have demonstrated that combining these two aspects, i.e. by inducing a model of the network’s attractors by applying Hebbian learning after constraint satisfaction, can lead to self-optimization of network connectivity. A key element of their approach is a repeated randomized reset and relaxation of network state, which has been interpreted as similar to the function of sleep (Woodward, Froese, & Ikegami, 2015). This perspective might give rise to an alternative “wake-sleep” algorithm (Hinton, Dayan, Frey, & Neal, 1995). All of this research, however, has taken place with isolated artificial neural networks, which goes against decades of work on situated robotics (Cliff, 1991). We consider the challenges involved in extending this work on sleep-like self-optimization to the dynamical approach to cognition, in which behavior is seen as emerging from the interactions of brain, body and environment (Beer, 2000).

Beer, R. D. (2000). Dynamical approaches to cognitive science. Trends in Cognitive Sciences, 4(3), 91-99.

Cliff, D. (1991). Computational neuroethology: A provisional manifesto. In J.-A. Meyer & S. W. Wilson (Eds.), From Animals to Animats (pp. 29-39). MIT Press.

Hinton, G. E., Dayan, P., Frey, B. J., & Neal, R. M. (1995). The “wake-sleep” algorithm for unsupervised neural networks. Science, 268, 1158-1161.

Watson, R. A., Buckley, C. L., & Mills, R. (2011). Optimization in “self-modeling” complex adaptive systems. Complexity, 16(5), 17-26.

Woodward, A., Froese, T., & Ikegami, T. (2015). Neural coordination can be enhanced by occasional interruption of normal firing patterns: A self-optimizing spiking neural network model. Neural Networks, 62, 39-46.