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9 The lag-lead system, i. e., a parallel connection of a low-pass filter and a high-pass filter with k < 1 (see 8a). (a) The amplitude frequency plot is only shown by the three assymptotes. (b) The step responses of the two branches for the case k = 0.5 are shown by light blue lines, and the sum of both functions, i. e. the step response of the total system, by a dark blue line. The input function is shown by green lines. (c) polar plot for k = 0.5
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This third edition essentially compares with the 2nd one, but has been improved by correction of errors and by a rearrangement and minor expansion of the sections referring to recurrent networks. These changes hopefully allow for an easier comprehension of the essential aspects of this important domain that has received growing attention during the...
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Long Short-Term Memory Recurrent Neural Networks (LSTM-RNN) are one of the most powerful dynamic classifiers publicly known. The network itself and the related learning algorithms are reasonably well documented to get an idea how it works. This paper will shed more light into understanding how LSTM-RNNs evolved and why they work impressively well,...
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... In looking at the resulting model itself, an artificial neural network was chosen to illustrate the micro view of cybernetics. H. Cruse's book [21] describes extensively the modeling of neural networks as cybernetic systems. The artificial neural network is presented below to illustrate the micro view of cybernetics within the macro view. ...
In this paper, data science is considered from a cybernetic perspective in two viewpoints. After a brief review of cybernetics, a partial conceptual view of a data science framework is provided. Several layers are identified, working from the software engineering life cycle macro perspective of a data analysis system, through production of a machine learning model to mine knowledge and predict business and product trends, to the micro perspective of a specific analysis, in this case using an artificial neural network. How the layers fit, individually and collectively, into a cybernetic system, identifying feedback loops and their interactions are described. Finally, the advantages and disadvantages of understanding the modern data science life cycle from the cybernetics perspective, and insights to be gained from this perspective are discussed.
... José Rodrigues de Oliveira-Neto, Felipe Duque Belfort, Rafael Cavalcanti-Neto, and João Ranhel S 2014 International Joint Conference on Neural Networks (IJCNN) July 6-11, 2014, Beijing, China 978-1-4799-1484-5/14/$31.00 ©2014 IEEE II. NAC AND MAGNITUDE COMPARATOR We use the term neuroid following Cruse's denomination for artificial computational unities [15], whereas we use neuron for biological cells. As a consequence, action potential is related to neurons whereas the term spike is related to artificial signals generated by neuroids. ...
Spiking neural systems can represent external stimuli and internal states by means of sets of neurons firing together, the so-called cell assemblies. Neural assembly computing (NAC) is an approach that investigates how spiking neural assemblies represent things and states of the world, how interaction among assemblies results in information processing, computation and behavior. Mainly, NAC deals with digital assemblies in which all-or-none cell members are firing. The notion of analog assemblies is introduced, describing sets of neurons that represent something proportionally to their driving stimuli. Interactions among digital and analog cell assemblies create a rich computational environment. In this paper a spiking neural network that compares the magnitude of two analog assemblies is presented.
Biologically inspired control system is necessary to be increased. This paper proposed the new design of multimodal neural network inspired from human learning system which takes different action in different condition. The multimodal neural network consists of some recurrent neural networks (RNNs) those are separated into different condition. There is selector system that decides certain RNN system depending the current condition of the robot. In this paper, we implemented this system in pendulum mobile robot as the basic object of study. Several certain number of RNNs are implemented into certain different condition of tilt robot. RNN works alternately depending on the condition of robot. In order to prove the effectiveness of the proposed model, we simulated in the computer simulation Open Dynamic Engine (ODE) and compared with ordinary RNN. The proposed neural model successfully stabilize the applied robot (2-wheeled robot). This model is developed for implemented into humanoid balancing learning system as the final object of study.
The core ideas of the proposed framework for empirical aesthetics are interpreted as focusing on processes, interaction, and phenomenological experience. This commentary first touches on some methodological impediments to developing theories of processing and interaction, and emphasizes the necessity of computational cognitive modeling using robots to test the empirical adequacy of such theories. Further, the importance of developing and integrating phenomenological methods into current experimental research is stressed, using experimental phenomenology as reference. Situated cognition, affective computing, human-robot interaction research, computational cognitive modeling and social and cultural neuroscience are noted as providing relevant insight into the empirical adequacy of current theories of cognitive and emotional processing. In the near future these fields will have a stimulating impact on empirical aesthetics and research on music and the mind.
SynonymsAfferent feedback to neural oscillators; Afferent input to rhythm generating networks; Proprioceptive feedback to central pattern generators; Sensory drive to rhythmic neuronal networks; Sensory modulation of central pattern generatorsDefinitionA central pattern generator (CPG) is an assembly of neurons (neuronal network) that produces rhythmic activity without requiring phasic input signals and often drives the motor system and rhythmic muscle movements. Sensory feedback to a CPG circuit is the return signal from the sensory system in response to this rhythmic muscle movement, which conveys a continuous measurement of the output behavior to the CPG.Detailed DescriptionIndividual neural networks, such as CPGs, can generate rhythmic output patterns even in the absence of any phasic input. They drive vital behaviors such as breathing, swallowing, and chewing, as well as locomotion and saccadic eye movements, and often continue to function even in isolated nervous system (i.e., in ...
