First, we present a simplified model, our Basic Model. This model detects moving contrast edges, binds these edges to objects, and selects one object. We derive analytical results concerning the performance level of the network. The results are similar to those known from amphibians.
Second, we extend this model to our Amphibian Model where a number of recent biological data is taken into account, especially data about the retina and the tectum opticum which is an important brain region with respect to prey-capture behaviour and which is homologous to the mammalian superior colliculus, and data about the nucleus isthmi which is a relatively small brain region connected reciprocally with the tectum opticum and which is homologous to the mammalian nucleus parabigeminalis. This model is a complete neural network for object segmentation from retinal photoreceptors to the segmentation of the selected object. The model yields possible explanations for many biological findings: e.~g., for the width distribution of the so-called receptive fields of neurons of the tectum opticum, or for the range of velocities of objects for which Hydromantes italicus shows prey-capture behaviour.
The model contains a spotlight network which enhances the retinotectal transmission in a certain spotlight region to further neurons, which we call the double-synapse coarse-coding neurons. The spotlight network should segment the selected object from the rest of the visual scene, and the double-synapse coarse-coding neurons should encode its place with high accuracy. We describe in detail how this network can be realized in amphibians.
Third, we present the computer model Simulander III with which we simulate the described double-dummy experiment. Indeed the model shows the high object-selection ability and the high accuracy known from Hydromantes. We give an outlook to various model extensions, especially to a model for recognition of the absolute width of the selected object and for recognition of a stepwise prey movement. These abilities are also known from Hydromantes. Additionally, we discuss a model extension which includes a neural network of synchronizing neurons.
In the second part of the text, we consider the classification of neurons of the tectum opticum of Hydromantes italicus according to their responses to different prey-dummies. We are particularly interested in the question whether the response types form distinct classes or a continuum. We classify the data with an algorithm that we have developed using known classification methods. The classification result suggests that the data forms a continuum with some accumulations. Finally, we discuss the possible biological reasons for a continuum and possible advantages of a continuum.
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