Literature DB >> 15800793

Modeling signal and background components of electrosensory scenes.

Ling Chen1, Jonathan L House, Rüdiger Krahe, Mark E Nelson.   

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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Year:  2004        PMID: 15800793     DOI: 10.1007/s00359-004-0587-3

Source DB:  PubMed          Journal:  J Comp Physiol A Neuroethol Sens Neural Behav Physiol        ISSN: 0340-7594            Impact factor:   1.836


  25 in total

1.  Multiple electrosensory maps in the medulla of weakly electric gymnotiform fish. II. Anatomical differences.

Authors:  C A Shumway
Journal:  J Neurosci       Date:  1989-12       Impact factor: 6.167

2.  Multiple electrosensory maps in the medulla of weakly electric gymnotiform fish. I. Physiological differences.

Authors:  C A Shumway
Journal:  J Neurosci       Date:  1989-12       Impact factor: 6.167

3.  Relations between the statistics of natural images and the response properties of cortical cells.

Authors:  D J Field
Journal:  J Opt Soc Am A       Date:  1987-12       Impact factor: 2.129

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Authors:  J Bastian; J Courtright; J Crawford
Journal:  J Comp Physiol A       Date:  1993-09       Impact factor: 1.836

5.  A new method for the simulation of electric fields, generated by electric fish, and their distorsions by objects.

Authors:  M Bacher
Journal:  Biol Cybern       Date:  1983       Impact factor: 2.086

6.  Body modeling and model-based tracking for neuroethology.

Authors:  M A MacIver; M E Nelson
Journal:  J Neurosci Methods       Date:  2000-02-15       Impact factor: 2.390

7.  Neural architecture of the electrosensory lateral line lobe: adaptations for coincidence detection, a sensory searchlight and frequency-dependent adaptive filtering

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Journal:  J Exp Biol       Date:  1999-05       Impact factor: 3.312

Review 8.  Plasticity of feedback inputs in the apteronotid electrosensory system.

Authors:  J Bastian
Journal:  J Exp Biol       Date:  1999-05       Impact factor: 3.312

9.  Modeling an electrosensory landscape: behavioral and morphological optimization in elasmobranch prey capture.

Authors:  Brandon R Brown
Journal:  J Exp Biol       Date:  2002-04       Impact factor: 3.312

10.  The electric image in weakly electric fish: physical images of resistive objects in Gnathonemus petersii.

Authors:  A A Caputi; R Budelli; K Grant; C C Bell
Journal:  J Exp Biol       Date:  1998-07       Impact factor: 3.312

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  26 in total

1.  Balanced ionotropic receptor dynamics support signal estimation via voltage-dependent membrane noise.

Authors:  Curtis M Marcoux; Stephen E Clarke; William H Nesse; Andre Longtin; Leonard Maler
Journal:  J Neurophysiol       Date:  2015-11-11       Impact factor: 2.714

Review 2.  Contrast coding in the electrosensory system: parallels with visual computation.

Authors:  Stephen E Clarke; André Longtin; Leonard Maler
Journal:  Nat Rev Neurosci       Date:  2015-11-12       Impact factor: 34.870

Review 3.  Sensory acquisition in active sensing systems.

Authors:  M E Nelson; M A MacIver
Journal:  J Comp Physiol A Neuroethol Sens Neural Behav Physiol       Date:  2006-01-28       Impact factor: 1.836

Review 4.  Perception and coding of envelopes in weakly electric fishes.

Authors:  Sarah A Stamper; Eric S Fortune; Maurice J Chacron
Journal:  J Exp Biol       Date:  2013-07-01       Impact factor: 3.312

5.  Speed-invariant encoding of looming object distance requires power law spike rate adaptation.

Authors:  Stephen E Clarke; Richard Naud; André Longtin; Leonard Maler
Journal:  Proc Natl Acad Sci U S A       Date:  2013-07-29       Impact factor: 11.205

6.  Optimal movement in the prey strikes of weakly electric fish: a case study of the interplay of body plan and movement capability.

Authors:  Claire M Postlethwaite; Tiffany M Psemeneki; Jangir Selimkhanov; Mary Silber; Malcolm A MacIver
Journal:  J R Soc Interface       Date:  2008-10-08       Impact factor: 4.118

7.  Temporal processing across multiple topographic maps in the electrosensory system.

Authors:  Rüdiger Krahe; Joseph Bastian; Maurice J Chacron
Journal:  J Neurophysiol       Date:  2008-05-28       Impact factor: 2.714

Review 8.  Efficient computation via sparse coding in electrosensory neural networks.

Authors:  Maurice J Chacron; André Longtin; Leonard Maler
Journal:  Curr Opin Neurobiol       Date:  2011-06-16       Impact factor: 6.627

9.  A role for mixed corollary discharge and proprioceptive signals in predicting the sensory consequences of movements.

Authors:  Tim Requarth; Patrick Kaifosh; Nathaniel B Sawtell
Journal:  J Neurosci       Date:  2014-11-26       Impact factor: 6.167

10.  Shape recognition and classification in electro-sensing.

Authors:  Habib Ammari; Thomas Boulier; Josselin Garnier; Han Wang
Journal:  Proc Natl Acad Sci U S A       Date:  2014-07-28       Impact factor: 11.205

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