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The neural basis of prey detection, recognition, and orientation was studied in depth by Jörg-Peter Ewert in a series of experiments that made the toad visual system a model system in neuroethology (neural basis of natural behavior). He began by observing the natural prey catching behavior of the common European toad (Bufo bufo).
Experiments on blue jays suggest they form a search image for certain prey.. Visual predators may form what is termed a search image of certain prey.. Predators need not locate their host directly: Kestrels, for instance, are able to detect the faeces and urine of their prey (which reflect ultraviolet), allowing them to identify areas where there are large numbers of voles, for example.
Thus, prey feature detection is not an all-or-nothing condition, but rather a matter of degree: the greater an object's releasing value as a prey stimulus, the stronger is prey-selective T5.2 neuron's discharge frequency, the shorter is toad's prey-catching response latency, and the higher is the number of prey-catching responses during a ...
This is a behavioral form of detection avoidance called crypsis used by animals to either avoid predation or to enhance prey hunting. Predation risk has long been recognized as critical in shaping behavioral decisions. For example, this predation risk is of prime importance in determining the time of evening emergence in echolocating bats.
Detection theory or signal detection theory is a means to measure the ability to differentiate between information-bearing patterns (called stimulus in living organisms, signal in machines) and random patterns that distract from the information (called noise, consisting of background stimuli and random activity of the detection machine and of the nervous system of the operator).
[9] [10] The last two examples form the subtopic image analysis of pattern recognition that deals with digital images as input to pattern recognition systems. [11] [12] Optical character recognition is an example of the application of a pattern classifier. The method of signing one's name was captured with stylus and overlay starting in 1990.
Feature learning is intended to result in faster training or better performance in task-specific settings than if the data was input directly (compare transfer learning). [1] In machine learning (ML), feature learning or representation learning [2] is a set of techniques that allow a system to automatically discover the representations needed ...
This is done using machine learning techniques that process different modalities, such as speech recognition, natural language processing, or facial expression detection. The goal of most of these techniques is to produce labels that would match the labels a human perceiver would give in the same situation: For example, if a person makes a ...