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      Coding Conspecific Identity and Motion in the Electric Sense

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          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          Interactions among animals can result in complex sensory signals containing a variety of socially relevant information, including the number, identity, and relative motion of conspecifics. How the spatiotemporal properties of such evolving naturalistic signals are encoded is a key question in sensory neuroscience. Here, we present results from experiments and modeling that address this issue in the context of the electric sense, which combines the spatial aspects of vision and touch, with the temporal aspects of audition. Wave-type electric fish, such as the brown ghost knifefish, Apteronotus leptorhynchus, used in this study, are uniquely identified by the frequency of their electric organ discharge (EOD). Multiple beat frequencies arise from the superposition of the EODs of each fish. We record the natural electrical signals near the skin of a “receiving” fish that are produced by stationary and freely swimming conspecifics. Using spectral analysis, we find that the primary beats, and the secondary beats between them (“beats of beats”), can be greatly influenced by fish swimming; the resulting motion produces low-frequency envelopes that broaden all the beat peaks and reshape the “noise floor”. We assess the consequences of this motion on sensory coding using a model electroreceptor. We show that the primary and secondary beats are encoded in the afferent spike train, but that motion acts to degrade this encoding. We also simulate the response of a realistic population of receptors, and find that it can encode the motion envelope well, primarily due to the receptors with lower firing rates. We discuss the implications of our results for the identification of conspecifics through specific beat frequencies and its possible hindrance by active swimming.

          Author Summary

          Effectively processing information from a sensory scene is essential for animal survival. Motion in a sensory scene complicates this task by dynamically modifying signal properties. To address this general issue, we focus on weakly electric fish. Each fish produces a weak electrical carrier signal with a characteristic frequency. Electroreceptors on its skin encode the modulations of this carrier caused by nearby objects and other animals, enabling this fish to thrive in its nocturnal environment. Little is known about how swimming movements influence natural electrosensory scenes, specifically in the context of detection and identification of, and communication with conspecifics. Using recordings involving free-swimming fish, we characterize the amplitude modulations of the carrier signal arising from small groups of fish. The differences between individual frequencies (beats) are prominent features of these signals, with the number of beats reflecting the number of neighbours. We also find that the distance and motion of a free-swimming fish are represented in a slow modulation of the beat at the receiving fish. Modeling shows that these stimulus features can be effectively encoded in the activity of the electroreceptors, but that encoding quality of some features can be degraded by motion, suggesting that active swimming could hinder conspecific identification.

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          Most cited references48

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          Information theory and neural coding.

          Information theory quantifies how much information a neural response carries about the stimulus. This can be compared to the information transferred in particular models of the stimulus-response function and to maximum possible information transfer. Such comparisons are crucial because they validate assumptions present in any neurophysiological analysis. Here we review information-theory basics before demonstrating its use in neural coding. We show how to use information theory to validate simple stimulus-response models of neural coding of dynamic stimuli. Because these models require specification of spike timing precision, they can reveal which time scales contain information in neural coding. This approach shows that dynamic stimuli can be encoded efficiently by single neurons and that each spike contributes to information transmission. We argue, however, that the data obtained so far do not suggest a temporal code, in which the placement of spikes relative to each other yields additional information.
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            Estimating and interpreting the instantaneous frequency of a signal. I. Fundamentals

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              Sensory acquisition in active sensing systems.

              A defining feature of active sensing is the use of self-generated energy to probe the environment. Familiar biological examples include echolocation in bats and dolphins and active electrolocation in weakly electric fish. Organisms that utilize active sensing systems can potentially exert control over the characteristics of the probe energy, such as its intensity, direction, timing, and spectral characteristics. This is in contrast to passive sensing systems, which rely on extrinsic energy sources that are not directly controllable by the organism. The ability to control the probe energy adds a new dimension to the task of acquiring relevant information about the environment. Physical and ecological constraints confronted by active sensing systems include issues of signal propagation, attenuation, speed, energetics, and conspicuousness. These constraints influence the type of energy that organisms use to probe the environment, the amount of energy devoted to the process, and the way in which the nervous system integrates sensory and motor functions for optimizing sensory acquisition performance.
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                Author and article information

                Contributors
                Role: Editor
                Journal
                PLoS Comput Biol
                PLoS Comput. Biol
                plos
                ploscomp
                PLoS Computational Biology
                Public Library of Science (San Francisco, USA )
                1553-734X
                1553-7358
                July 2012
                July 2012
                12 July 2012
                : 8
                : 7
                : e1002564
                Affiliations
                [1 ]Department of Physics, University of Ottawa, Ottawa, Ontario, Canada
                [2 ]Department of Biology, University of Ottawa, Ottawa, Ontario, Canada
                [3 ]Department of Cellular and Molecular Medicine, University of Ottawa, Ottawa, Ontario, Canada
                Rice University, United States of America
                Author notes

                Conceived and designed the experiments: NY GH JEL AL. Performed the experiments: GH NY. Analyzed the data: NY JEL AL GH. Contributed reagents/materials/analysis tools: NY GH CG JEL AL. Wrote the paper: NY JEL AL GH.

                Article
                PCOMPBIOL-D-11-01595
                10.1371/journal.pcbi.1002564
                3395610
                22807662
                0c4e65eb-de90-4f7e-82d8-bda8ebae3436
                Yu et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
                History
                : 27 October 2011
                : 3 May 2012
                Page count
                Pages: 16
                Categories
                Research Article
                Biology
                Computational Biology
                Computational Neuroscience
                Neuroscience
                Sensory Systems

                Quantitative & Systems biology
                Quantitative & Systems biology

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