The lateral-line system is a unique mechanosensory facility of aquatic animals that enables them not only to localize prey, predator, obstacles, and conspecifics, but also to recognize hydrodynamic objects. Here we present an explicit model explaining how aquatic animals such as fish can distinguish differently shaped submerged moving objects. Our model is based on the hydrodynamic multipole expansion and uses the unambiguous set of multipole components to identify the corresponding object. Furthermore, we show that within the natural range of one fish length the velocity field contains far more information than that due to a dipole. Finally, the model we present is easy to implement both neuronally and technically, and agrees well with available neuronal, physiological, and behavioral data on the lateral-line system.
Two groups of snakes possess an infrared detection system that is used to create a heat image of their environment. In this Letter we present an explicit reconstruction model, the "virtual lens," which explains how a snake can overcome the optical limitations of a wide aperture pinhole camera, and how ensuing properties of the receptive fields on the infrared-sensitive membrane may explain the behavioral performance of this sensory system. Our model explores the optical quality of the infrared system by detailing how a functional representation of the thermal properties of the environment can be created. The model is easy to implement neuronally and agrees well with available neuronal, physiological, and behavioral data on the snake infrared system.
We revisit the method of conformal mapping and apply it to the setting found in mechanosensory detection systems such as the lateral-line system of fish. We derive easy-to-use equations capable of describing analytically the influence of the stimulus shape on the flow field and thus on the input to the lateral line. The present approach shows that the shape of a submerged moving object affects its perception if its distance to a detecting animal does not exceed the object's body length.
In the struggle for survival in a complex and dynamic environment, nature has developed a multitude of sophisticated sensory systems. In order to exploit the information provided by these sensory systems, higher vertebrates reconstruct the spatio-temporal environment from each of the sensory systems they have at their disposal. That is, for each modality the animal computes a neuronal representation of the outside world, a monosensory neuronal map. Here we present a universal framework that allows to calculate the specific layout of the involved neuronal network by means of a general mathematical principle, viz., stochastic optimality. In order to illustrate the use of this theoretical framework, we provide a step-by-step tutorial of how to apply our model. In so doing, we present a spatial and a temporal example of optimal stimulus reconstruction which underline the advantages of our approach. That is, given a known physical signal transmission and rudimental knowledge of the detection process, our approach allows to estimate the possible performance and to predict neuronal properties of biological sensory systems. Finally, information from different sensory modalities has to be integrated so as to gain a unified perception of reality for further processing, e.g., for distinct motor commands. We briefly discuss concepts of multimodal interaction and how a multimodal space can evolve by alignment of monosensory maps.
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