By Claire Gerrard, John McCall, George M. Coghill, Christopher Macleod (auth.), Alessandro E. P. Villa, Włodzisław Duch, Péter Érdi, Francesco Masulli, Günther Palm (eds.)
The two-volume set LNCS 7552 + 7553 constitutes the complaints of the twenty second foreign convention on synthetic Neural Networks, ICANN 2012, held in Lausanne, Switzerland, in September 2012. The 162 papers incorporated within the lawsuits have been rigorously reviewed and chosen from 247 submissions. they're geared up in topical sections named: theoretical neural computation; details and optimization; from neurons to neuromorphism; spiking dynamics; from unmarried neurons to networks; complicated firing styles; flow and movement; from sensation to notion; item and face popularity; reinforcement studying; bayesian and echo nation networks; recurrent neural networks and reservoir computing; coding architectures; interacting with the mind; swarm intelligence and decision-making; mulitlayer perceptrons and kernel networks; education and studying; inference and popularity; aid vector machines; self-organizing maps and clustering; clustering, mining and exploratory research; bioinformatics; and time weries and forecasting.
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Extra info for Artificial Neural Networks and Machine Learning – ICANN 2012: 22nd International Conference on Artificial Neural Networks, Lausanne, Switzerland, September 11-14, 2012, Proceedings, Part I
As an example the case of the standard Hopfield model ( rμ ≡ 1 ) is examined in details. In Section 3 we present the algorithm of solving of the main equation in the general case. Some concluding remarks are given in Section 4. For the first time the quasi-Hebbian connection matrix (2) was discussed many years ago. For this matrix the implicit form of the main equation (4) was obtained in . However, the authors of  examined the case of the standard Hopfield model only. Our contribution is the solution of the main equation in the general form.
For example, by initializing a C pool, a walk gait will be generated, where the C pool chosen will determine the starting leg. Similarly, a trot gait is achieved by initializing 2 C pools within alternate modules. In this particular design, the value to which the C pool(s) are initialized determines the DOF angle and were set specifically for the physicality of the particular robot, although it can be freely varied. 4 Results The ARN controller was considered to generate a specific gait if the relative phases of the respective oscillatory signals were within 2% of the standard gait cycle described previously.
References 1. : 6. The Adaptive Self-organization of Serial Order in Behavior: Speech, Language, and Motor Control. In: Pattern Recognition by Humans and Machines: Speech Perception, pp. 187–396 (1986) 2. : The problem of serial order: a neural network model of sequence learning and recall. In: Current Research in Natural Language Generation, pp. 287–319. , San Diego (1990) 3. : Contention scheduling and the control of routine activities. Cognitive Neuropsychology 17(4), 297–338 (2000) 4. : An embodied account of serial order: How instabilities drive sequence generation.