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Modeling signal and background components of electrosensory scenes

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Abstract

Weakly electric fish are able to detect and localize prey based on microvolt-level perturbations in the fish’s self-generated electric field. In natural environments, weak prey-related signals are embedded in much stronger electrosensory background noise. To better characterize the signal and background components associated with natural electrolocation tasks, we recorded transdermal voltage modulations in restrained Apteronotus albifrons in response to moving spheres, tail bends, and large nonconducting boundaries. Spherical objects give rise to ipsilateral images with center-surround structure and contralateral images that are weak and diffuse. Tail bends and laterally placed nonconducting boundaries induce relatively strong ipsilateral and contralateral modulations of opposite polarity. We present a computational model of electric field generation and electrosensory image formation that is able to reproduce the key features of these empirically measured signal and background components in a unified framework. The model comprises an array of point sources and sinks distributed along the midline of the fish, which can conform to arbitrary body bends. The model is computationally fast and can be used to estimate the spatiotemporal pattern of activation across the entire electroreceptor array of the fish during natural behaviors.

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Abbreviations

ELL:

Electrosensory lateral line lobe

EOD:

Electric organ discharge

FWHM:

Full-width at half-maximum

RMS:

Root mean square

SNR:

Signal-to-noise ratio

References

  • Assad C (1997) Electric field maps and boundary element simulations of electrolocation in weakly electric fish. PhD thesis, California Institute of Technology, Pasadena, University Microfilms Inc, Ann Arbor

  • Assad C, Rasnow B, Stoddard PK (1999) The electric organ discharges and electric images during electrolocation. J Exp Biol 202:1185–1193

    PubMed  Google Scholar 

  • Attneave F (1954) Some informational aspects of visual perception. Psychol Rev 61:183–193

    CAS  PubMed  Google Scholar 

  • Bacher M (1983) A new method for the simulation of electric fields, generated by electric fish, and their distortions by objects. Biol Cybern 47:51–58

    CAS  PubMed  Google Scholar 

  • Barlow HB (1961) Possible principles underlying the transformation of sensory messages. In: Rosenblith WA (ed) Sensory communication. MIT Press, Cambridge, pp 217–234

    Google Scholar 

  • Bass AH (1986) Electric organs revisited: Evolution of a vertebrate communication and orientation organ. In: Bullock TH, Heiligenberg W (eds) Electroreception. Wiley, New York, pp 13–70

    Google Scholar 

  • Bastian J (1981) Electrolocation. I. How electroreceptors of Apteronotus albifrons code for moving objects and other electrical stimuli. J Comp Physiol A 144:465–479

    Google Scholar 

  • Bastian J (1982) Vision and electroreception: integration of sensory information in the optic tectum of the weakly electric fish Apteronotus albifrons. J Comp Physiol A 147:287–297

    Article  Google Scholar 

  • Bastian J (1986) Gain control in the electrosensory system mediated by descending inputs to the electrosensory lateral line lobe. J Neurosci 6:553–562

    Google Scholar 

  • Bastian J (1995) Pyramidal-cell plasticity in weakly electric fish: a mechanism for attenuating responses to reafferent electrosensory inputs. J Comp Physiol A 176:63–78

    CAS  PubMed  Google Scholar 

  • Bastian J (1996) Plasticity in an electrosensory system. I. General features of a dynamic sensory filter. J Neurophysiol 76:2483–2496

    Google Scholar 

  • Bastian J (1999) Plasiticity of feedback inputs in the apteronotid electrosensory system. J Exp Biol 202:1327–1337

    PubMed  Google Scholar 

  • Bastian J (2003) Electrolocation. In: Arbib MA (ed) The handbook of brain theory and neural networks, 2nd edn. MIT Press, Cambridge, pp 391–394

    Google Scholar 

  • Bastian J, Courtright J, Crawford J (1993) Commissural neurons of the electrosensory lateral-line lobe of Apteronotus leptorhynchus: morphological and physiological characteristics. J Comp Physiol A 173:257–274

    CAS  PubMed  Google Scholar 

  • Berman NJ, Maler L (1999) Neural architecture of the electrosensory lateral line lobe: adaptations for coincidence detection, a sensory searchlight and frequency-dependent adaptive filtering. J Exp Biol 202:1243–1253

    PubMed  Google Scholar 

  • Brown BR (2002) Modeling an electrosensory landscape: behavioral and morphological optimization in elasmobranch prey capture. J Exp Biol 205:999–1007

    PubMed  Google Scholar 

  • Caputi A, Budelli R (1995) The electric image in weakly electric fish. I. A data-based model of waveform generation in Gymnotus carapo. J Comput Neurosci 2:131–147

    CAS  PubMed  Google Scholar 

  • Caputi AA, Budelli R, Grant K, Bell CC (1998) The electric image in weakly electric fish: physical images of resistive objects in Gnathonemus petersii. J Exp Biol 201:2115–2128

    PubMed  Google Scholar 

  • Dusenbery DB (1992) Sensory ecology: how organisms acquire and respond to information. WH Freeman, New York

    Google Scholar 

  • Field DJ (1987) Relations between the statistics of natural images and the response properties of cortical cells. J Opt Soc Am A 4:2379–2394

    CAS  PubMed  Google Scholar 

  • Gabbiani F, Metzner W (1999) Encoding and processing of sensory information in neuronal spike trains. J Exp Biol 202:1267–1279

    PubMed  Google Scholar 

  • Gómez L, Budelli R, Grant K, Caputi AA (2004) Pre-receptor profile of sensory images and primary afferent neuronal representation in the mormyrid electrosensory system. J Exp Biol 207:2443–2453

    PubMed  Google Scholar 

  • Granath LP, Erskine FTI, MacCabee BS, Sachs HG (1968) Electric field measurements on a weakly electric fish. Biophysik 4:370–372

    CAS  PubMed  Google Scholar 

  • Haykin S (2001) Communication systems. Wiley, New York

    Google Scholar 

  • Heiligenberg W (1975) Theoretical and experimental approaches to spatial aspects of electrolocation. J Comp Physiol 103:247–272

    Article  Google Scholar 

  • Heiligenberg W (1991) Neural nets in electric fish. MIT Press, Cambridge

    Google Scholar 

  • Hoshimiya N, Shogen K, Matsuo T, Chichibu S (1980) The Apteronotus EOD field: waveform and EOD field simulation. J Comp Physiol A 135:283–290

    Article  Google Scholar 

  • Jackson JD (1975) Classical electrodynamics. Wiley, New York

    Google Scholar 

  • Knudsen EI (1975) Spatial aspects of the electric fields generated by weakly electric fish. J Comp Physiol 99:103–118

    Article  Google Scholar 

  • Lissmann HW (1958) On the function and evolution of electric organs in fish. J Exp Biol 35:156–191

    Google Scholar 

  • MacIver MA (2001) The computational neuroethology of weakly electric fish: body modeling, motion analysis, and sensory signal estimation. PhD Thesis. Dissertation Abstracts International B (DAI-B) 62:2625

  • MacIver MA, Nelson ME (2000) Body modeling and model-based tracking for neuroethology. J Neurosci Meth 95:133–143

    CAS  Google Scholar 

  • MacIver MA, Sharabash N, Nelson ME (2001) Quantitative analysis of prey capture behavior in the weakly electric fish Apteronotus albifrons. J Exp Biol 204:543–557

    CAS  PubMed  Google Scholar 

  • Nelson ME, MacIver MA (1999) Prey capture in the weakly electric fish Apteronotus albifrons: sensory acquisition strategies and electrosensory consequences. J Exp Biol 202:1195–1203

    PubMed  Google Scholar 

  • Nelson ME, Xu Z, Payne JR (1997) Characterization and modeling of P-type electrosensory afferent responses to amplitude modulations in a wave-type electric fish. J Comp Physiol A 181:532–544

    CAS  PubMed  Google Scholar 

  • Nelson ME, MacIver MA, Coombs S (2002) Modeling electrosensory and mechanosensory images during the predatory behavior of weakly electric fish. Brain Behav Evol 59:199–210

    Article  PubMed  Google Scholar 

  • Rasnow B (1996) The effects of simple objects on the electric field of Apteronotus. J Comp Physiol A 178:397–411

    Google Scholar 

  • Rasnow B, Bower JM (1996) The electric organ discharges of the gymnotiform fishes: I. Apteronotus leptorhynchus. J Comp Physiol A 178:383–396

    Google Scholar 

  • Rieke F, Bodnar DA, Bialek W (1995) Naturalistic stimuli increase the rate and efficiency of information transmission by primary auditory afferents. Proc R Soc Lond B 262:259–265

    CAS  PubMed  Google Scholar 

  • Robinson DA (1968) The electrical properties of metal microelectrodes. Proc IEEE 56:1065–1071

    CAS  Google Scholar 

  • Ruderman DL, Bialek W (1994) Statistics of natural images: scaling in the woods. Phys Rev Lett 73:814–817

    PubMed  Google Scholar 

  • Scheich H, Bullock TH (1974) The detection of electric fields from electric organs. In: Fessard F (ed) Electroreceptors and other specialized receptors in lower vertebrates. Springer, Berlin Heidelberg New York, pp 201–256

    Google Scholar 

  • Shumway CA (1989a) Multiple electrosensory maps in the medulla of weakly electric gymnotiform fish. I. Physiological differences. J Neurosci 9:4388–4399

    Google Scholar 

  • Shumway CA (1989b) Multiple electrosensory maps in the medulla of weakly electric gymnotiform fish. II. Anatomical differences. J Neurosci 9:4400–4415

    Google Scholar 

  • Zakon HH (1986) The electroreceptive periphery. In: Bullock TH, Heiligenberg W (eds) Electroreception. Wiley, New York, pp 103–156

    Google Scholar 

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Acknowledgements

We thank C. Assad and B. Rasnow for providing the electric field measurements used in Fig. 3a, and N. Lüdtke for valuable comments and discussion. This work was supported by grants from the National Science Foundation (IBN-0078206) and the National Institute of Mental Health (R01 MH49242). All animal procedures were approved by the Animal Care and Use Committee at the University of Illinois, Urbana-Champaign, USA.

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  1. Rüdiger Krahe

    Present address: Department of Biology, McGill University, Montreal, H3A1B1, Canada

Authors and Affiliations

  1. Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, 405 N. Mathews Ave., Urbana, IL, 61801, USA

    Ling Chen, Jonathan L. House, Rüdiger Krahe & Mark E. Nelson

  2. Department of Molecular and Integrative Physiology, University of Illinois at Urbana-Champaign, Urbana, IL, 61801, USA

    Rüdiger Krahe & Mark E. Nelson

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  1. Ling Chen
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  2. Jonathan L. House
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Correspondence to Mark E. Nelson.

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Chen, L., House, J.L., Krahe, R. et al. Modeling signal and background components of electrosensory scenes. J Comp Physiol A 191, 331–345 (2005). https://doi.org/10.1007/s00359-004-0587-3

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  • DOI: https://doi.org/10.1007/s00359-004-0587-3

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