The Evolution of Signals

2014 ◽  
pp. 79-90
Author(s):  
Daniel Cloud
Keyword(s):  
Evolution Of Signals

Evolution of signals in long-haul fiber links

1993 ◽  
Vol 6 (12) ◽  
pp. 675-677
Author(s):  
F. Matera ◽  
A. Mecozzi ◽  
M. Romagnoli ◽  
M. Settembre
Keyword(s):  
Evolution Of Signals ◽  
Fiber Links

Hidden preferences and the evolution of signals

1993 ◽  
Vol 340 (1292) ◽  
pp. 207-213 ◽  
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Genetic Drift ◽  
Evolutionary History ◽  
Biological Signals ◽  
Evolution Of Signals ◽  
Efficient Alternative ◽  
Artificial Neural ◽  
Simple Models

Mechanisms concerned with the recognition of biological signals act as important agents of selection on the appearance or ‘form’ of signals. Recognition of a given signal form can be achieved by many equally efficient alternative mechanisms. These alternative mechanisms will be selectively neutral and subject to change by genetic drift, thus preventing the fixation of a signal form that is optimal in releasing the receiver’s behaviour. Because signal form is a multidimensional trait with an almost infinite potential to vary, it is expected that some novel forms of signals always exist that elicit responses more readily than any of those signals that the receiver has experienced during its evolutionary history. The existence of such ‘hidden preferences’ is illustrated in simple models of recognition mechanisms based on artificial neural networks. The fundamental evolutionary instability of recognition mechanisms perhaps explains why biological signals are so variable in form, and why, in experiments, animals sometimes show greater responses to novel forms of signals than they do to familiar forms.

scholarly journals Development of Communication Behaviour: Receiver Ontogeny in Túngara Frogs and a Prospectus for a Behavioural Evolutionary Development

2012 ◽  
Vol 2012 ◽  
pp. 1-10 ◽  
Author(s):  
Alexander T. Baugh ◽  
Kim L. Hoke ◽  
Michael J. Ryan
Keyword(s):  
Communication Systems ◽  
Animal Communication ◽  
Mate Recognition ◽  
Evolutionary Development ◽  
Communication Signals ◽  
Tungara Frogs ◽  
Signal Production ◽  
Evolution Of Signals ◽  
Evolution Of Communication ◽  
Behaviour Recognition

Most studies addressing the development of animal communication have focused on signal production rather than receiver decoding, and similar emphasis has been given to learning over nonlearning. But receivers are an integral part of a communication network, and nonlearned mechanisms appear to be more ubiquitous than learned ones in the communication systems of most animals. Here we review the results of recent experiments and outline future directions for integrative studies on the development of a primarily nonlearned behaviour—recognition of communication signals during ontogeny in a tropical frog. The results suggest that antecedents to adult behaviours might be a common feature of developing organisms. Given the essential role that acoustic communication serves in reproduction for many organisms and that receivers can exert strong influence on the evolution of signals, understanding the evolutionary developmental basis of mate recognition will provide new insights into the evolution of communication systems.

Visual Acuity and the Evolution of Signals

2018 ◽  
Vol 33 (5) ◽  
pp. 358-372 ◽  
Author(s):  
Eleanor M. Caves ◽  
Nicholas C. Brandley ◽  
Sönke Johnsen
Keyword(s):  
Visual Acuity ◽  
Evolution Of Signals

scholarly journals A Chemometric Survey about the Ability of Voltammetry to Discriminate Pharmaceutical Products from the Evolution of Signals as a Function of pH

Chemosensors ◽  
2020 ◽  
Vol 8 (3) ◽  
pp. 46
Author(s):  
Javier Marín ◽  
Núria Serrano ◽  
Cristina Ariño ◽  
José Manuel Díaz-Cruz
Keyword(s):  
Principal Component Analysis ◽  
Discriminant Analysis ◽  
Principal Component ◽  
Differential Pulse ◽  
Pharmaceutical Products ◽  
Support Vector ◽  
Peak Potential ◽  
Evolution Of Signals ◽  
Chemometric Approach ◽  
Chemometric Tools

Many pharmaceutical products are electroactive and, therefore, can be determined by voltammetry. However, most of these substances produce signals in the same region of oxidative potentials, which makes it difficult to identify them. In this work, chemometric tools are applied to extract characteristic information not only from the peak potential of differential pulse voltammograms (DPV), but also from their evolution as a function of pH. The chemometric approach is based on principal component analysis (PCA), partial least squares discriminant analysis (PLS-DA) and support vector machine discriminant analysis (SVM-DA) yielding promising results for the future discrimination of pharmaceutical products in water samples.

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