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  • Review Article
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Contrast coding in the electrosensory system: parallels with visual computation

Key Points

  • First- and second-order contrast stimuli can be described for the visual, auditory and tactile senses. Similarly, electrosensory contrast patterns vary over large spatial and temporal scales.

  • Fast exponential adaptation and power law adaptation in electrosensory afferent neurons partition the range of natural electrosensory stimulus frequencies and enable different signal processing goals.

  • Comparison of the electrosensory and retinal adaptation algorithms reveals many similarities.

  • The electrosensory of ON and OFF ganglion cells can also be compared to retinal ON and OFF ganglion cells. Despite different biophysics and network architectures, these cells share the same algorithmic role for the electrosense and vision. Envelope encoding and decoding mechanisms in the electrosense are related to both locomotion and social behaviours. Both the electrosense and vision combine ON and OFF cell responses to improve the coding efficiency of second-order contrast stimuli.

  • Motion reversal triggers switches in electrosensory ON and OFF cell preferences for spatial contrast (polarity), which has also been noted to occur in salamander and mouse retina. Individual ON and OFF cell firing rates encode scalar quantities of motion such as object distance and speed, whereas sequences of population activity encode vector information (motion direction).

  • There are many benefits of these flexible coding paradigms for spatiotemporal contrast using ON and OFF cells. It will be important to understand how downstream decoding neurons interpret patterns and sequences of activity of ON and OFF cell populations.

Abstract

To identify and interact with moving objects, including other members of the same species, an animal's nervous system must correctly interpret patterns of contrast in the physical signals (such as light or sound) that it receives from the environment. In weakly electric fish, the motion of objects in the environment and social interactions with other fish create complex patterns of contrast in the electric fields that they produce and detect. These contrast patterns can extend widely over space and time and represent a multitude of relevant features, as is also true for other sensory systems. Mounting evidence suggests that the computational principles underlying contrast coding in electrosensory neural networks are conserved elements of spatiotemporal processing that show strong parallels with the vertebrate visual system.

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Figure 1: Natural electrosensory signals.
Figure 2: Role of adaptation in the electroreceptor afferent response to object motion.
Figure 3: Electrosensory lobe circuitry.
Figure 4: Encoding and decoding conspecific motion using envelopes.
Figure 5: Motion reversal and distributed contrast coding.

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References

  1. Purves, D., Wojtach, W. T. & Lotto, R. B. Understanding vision in wholly empirical terms. Proc. Natl Acad. Sci. USA 108, S15588–S15595 (2011).

    Article  Google Scholar 

  2. Marr, D. Vision: A Computational Investigation into the Human Representation and Processing of Visual Information (MIT Press, 2010).

    Book  Google Scholar 

  3. Krahe, R. & Maler, L. Neural maps in the electrosensory system of weakly electric fish. Curr. Opin. Neurobiol. 24, 13–21 (2014). This is an important review of the electrosense and the maps of the ELL, each of which contains ON and OFF cell types.

    Article  CAS  Google Scholar 

  4. Babineau, D., Lewis, J. E. & Longtin, A. Spatial acuity and prey detection in weakly electric fish. PLoS Comput. Biol. 3, e38 (2007).

    Article  Google Scholar 

  5. Chen, L., House, J. L., Krahe, R. & Nelson, M. E. Modeling signal and background components of electrosensory scenes. J. Comp. Physiol. A Neuroethol. Sens. Neural Behav. Physiol. 191, 331–345 (2005).

    Article  Google Scholar 

  6. Nelson, M. E. & MacIver, M. A. Prey capture in the weakly electric fish Apteronotus leptorhynchus: sensory acquisition strategies and electrosensory consequences. J. Exp. Biol. 202, 1195–1203 (1999).

    CAS  PubMed  Google Scholar 

  7. Fotowat, H., Harrison, R. R. & Krahe, R. Statistics of the electrosensory input in the freely swimming weakly electric fish Apteronotus leptorhynchus. J. Neurosci. 33, 13758–13772 (2013).

    Article  CAS  Google Scholar 

  8. Stamper, S. A. et al. Species differences in group size and electrosensory interference in weakly electric fishes: implications for electrosensory processing. Behav. Brain Res. 207, 368–376 (2010).

    Article  Google Scholar 

  9. Baker, C. L. Jr Central neural mechanisms for detecting second-order motion. Curr. Opin. Neurobiol. 9, 461–466 (1999).

    Article  CAS  Google Scholar 

  10. Gussin, D., Benda, J. & Maler, L. Limits of linear rate coding of dynamic stimuli by electroreceptor afferents. J. Neurophysiol. 97, 2917–2929 (2007).

    Article  Google Scholar 

  11. Xu, Z., Payne, J. R. & Nelson, M. E. Logarithmic time course of sensory adaptation in electrosensory afferent nerve fibers in a weakly electric fish. J. Neurophysiol. 76, 2020–2032 (1996).

    Article  CAS  Google Scholar 

  12. Ratnam, R. & Nelson, M. E. Non-renewal statistics of electrosensory afferent spike trains: implications for the detection of weak sensory signals. J. Neurosci. 20, 6672–6683 (2000).

    Article  CAS  Google Scholar 

  13. Drew, P. J. & Abbott, L. F. Models and properties of power-law adaptation in neural systems. J. Neurophysiol. 96, 826–833 (2006).

    Article  Google Scholar 

  14. Benda, J., Longtin, A. & Maler, L. Spike-frequency adaptation separates transient communication signals from background oscillations. J. Neurosci. 25, 2312–2321 (2005).

    Article  CAS  Google Scholar 

  15. Clarke, S. E., Naud, R., Longtin, A. & Maler, L. Speed-invariant encoding of looming object distance requires power law spike rate adaptation. Proc. Natl Acad. Sci. USA 110, 13624–13629 (2013). This paper describes the role of power law adaptation in generating a timescale-free code for looming motion, providing a velocity-independent estimate of looming object distance. This form of adaptation is one of two important types in primary EAs.

    Article  CAS  Google Scholar 

  16. Nelson, M. E., Xu, Z. & Payne, J. R. 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 (1997).

    Article  CAS  Google Scholar 

  17. Benda, J., Longtin, A. & Maler, L. A synchronization-desynchronization code for natural communication signals. Neuron 52, 347–358 (2006). This reference provides details about the fast exponential form of spike rate adaptation that operates in primary electrosensory neurons to encode high-frequency social signals. By selecting for specific stimulus timescales, adaptation can generate a synchronous or an asynchronous population state.

    Article  CAS  Google Scholar 

  18. Chacron, M. J., Maler, L. & Bastian, J. Electroreceptor neuron dynamics shape information transmission. Nat. Neurosci. 8, 673–678 (2005).

    Article  CAS  Google Scholar 

  19. Chacron, M. J., Longtin, A. & Maler, L. Efficient computation via sparse coding in electrosensory neural networks. Curr. Opin. Neurobiol. 21, 752–760 (2011).

    Article  CAS  Google Scholar 

  20. Marsat, G., Longtin, A. & Maler, L. Cellular and circuit properties supporting different sensory coding strategies in electric fish and other systems. Curr. Opin. Neurobiol. 22, 1–7 (2012).

    Article  Google Scholar 

  21. Clarke, S. E., Longtin, A. & Maler, L. A neural code for looming and receding motion is distributed over a population of electrosensory ON and OFF contrast cells. J. Neurosci. 34, 5583–5594 (2014). Motion reversal evokes switches in electrosensory ON and OFF cell preferences for spatial contrast (polarity). We conclude that ON and OFF cells encode positive and negative derivatives of sensory contrast, respectively; that is, they are selective for the sign of temporal contrast changes.

    Article  CAS  Google Scholar 

  22. Wark, B., Fairhall, A. & Rieke, F. Timescales of inference in visual adaptation. Neuron 61, 750–761 (2009).

    Article  CAS  Google Scholar 

  23. Wark, B., Lundstrom, B. N. & Fairhall, A. Sensory adaptation. Curr. Opin. Neurobiol. 17, 423–429 (2007).

    Article  CAS  Google Scholar 

  24. French, A. S. & Torkkeli, P. H. The power law of sensory adaptation: simulation by a model of excitability in spider mechanoreceptor neurons. Ann. Biomed. Eng. 36, 153–161 (2008).

    Article  Google Scholar 

  25. Pozzorini, C., Naud, R., Mensi, S. & Gerstner, W. Temporal whitening by power-law adaptation in neocortical neurons. Nat. Neurosci. 16, 942–948 (2013).

    Article  CAS  Google Scholar 

  26. Trenholm, S., Schwab, D. J., Balasubramanian, V. & Awatramani, G. B. Lag normalization in an electrically coupled neural network. Nat. Neurosci. 16, 154–156 (2013). An electrically coupled network of motion-coding neurons in mouse retina functions to correct for spatial lag. The biophysical mechanisms and algorithms are completely different to those in the electrosense but the speed-invariant computation is equivalent to that of EA afferents.

    Article  CAS  Google Scholar 

  27. Berman, N. J. & Maler, L. 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 (1999).

    CAS  PubMed  Google Scholar 

  28. Saunders, J. & Bastian, J. The physiology and morphology of two classes of electrosensory neurons in the weakly electric fish Apteronotus Leptorhynchus. J. Comp. Physiol. A 154, 199–209 (1984).

    Article  Google Scholar 

  29. Maler, L. Receptive field organization across multiple electrosensory maps: I. Columnar organization and estimation of receptive field size. J. Comp. Neurol. 516, 376–393 (2009).

    Article  Google Scholar 

  30. Schiller, P. H. The ON and OFF channels of the visual system. Res. Publ. Assoc. Res. Nerv. Ment. Dis. 67, 35–41 (1990).

    CAS  PubMed  Google Scholar 

  31. Lee, C. H. Neuroscience: the split view of motion. Nature 468, 178–179 (2010).

    Article  CAS  Google Scholar 

  32. Bastian, J., Chacron, M. J. & Maler, L. Receptive field organization determines pyramidal cell stimulus-encoding capability and spatial stimulus selectivity. J. Neurosci. 22, 4577–4590 (2002).

    Article  CAS  Google Scholar 

  33. Middleton, J. W., Longtin, A., Benda, J. & Maler, L. The cellular basis for parallel neural transmission of a high-frequency stimulus and its low-frequency envelope. Proc. Natl Acad. Sci. USA 103, 14596–14601 (2006).

    Article  CAS  Google Scholar 

  34. Stamper, S. A., Fortune, E. S. & Chacron, M. J. Perception and coding of envelopes in weakly electric fishes. J. Exp. Biol. 216, 2393–2402 (2013).

    Article  Google Scholar 

  35. Yu, N., Hupe, G., Garfinkle, C., Lewis, J. E. & Longtin, A. Coding conspecific identity and motion in the electric sense. PLoS Comput. Biol. 8, e1002564 (2012).

    Article  CAS  Google Scholar 

  36. Metzen, M. G. & Chacron, M. J. Neural heterogeneities determine response characteristics to second-, but not first-order stimulus features. J. Neurosci. 35, 3124–3138 (2015).

    Article  CAS  Google Scholar 

  37. Stamper, S. A., Madhav, M. S., Cowan, N. J. & Fortune, E. S. Beyond the jamming avoidance response: weakly electric fish respond to the envelope of social electrosensory signals. J. Exp. Biol. 215, 4196–4207 (2012).

    Article  Google Scholar 

  38. Middleton, J. W., Harvey-Girard, E., Maler, L. & Longtin, A. Envelope gating and noise shaping in populations of noisy neurons. Phys. Rev. E Stat. Nonlin. Soft Matter Phys. 75, 021918 (2007).

    Article  CAS  Google Scholar 

  39. Metzen, M. G. & Chacron, M. J. Weakly electric fish display behavioral responses to envelopes naturally occurring during movement: implications for neural processing. J. Exp. Biol. 217, 1381–1391 (2014).

    Article  Google Scholar 

  40. Savard, M., Krahe, R. & Chacron, M. J. Neural heterogeneities influence envelope and temporal coding at the sensory periphery. Neuroscience 172, 270–284 (2011).

    Article  CAS  Google Scholar 

  41. Metzen, M. G. et al. Coding of envelopes by correlated but not single-neuron activity requires neural variability. Proc. Natl Acad. Sci. USA 112, 4791–4796 (2015). This work describes a correlation coding mechanism for populations of neurons, demonstrating its involvement in processing envelopes in the electrosense and mammalian vestibular system.

    Article  CAS  Google Scholar 

  42. McGillivray, P., Vonderschen, K., Fortune, E. S. & Chacron, M. J. Parallel coding of first- and second-order stimulus attributes by midbrain electrosensory neurons. J. Neurosci. 32, 5510–5524 (2012). This important work describes how estimates of AM and envelope can be distilled in midbrain from ON and OFF cell population responses.

    Article  CAS  Google Scholar 

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

    Article  CAS  Google Scholar 

  44. Middleton, J. W., Longtin, A., Benda, J. & Maler, L. Postsynaptic receptive field size and spike threshold determine encoding of high frequency information via sensitivity to synchronous presynaptic activity. J. Neurophysiol. 101, 1160–1170 (2009).

    Article  Google Scholar 

  45. Longtin, A., Middleton, J. W., Cieniak, J. & Maler, L. Neural dynamics of envelope coding. Math. Biosci. 214, 87–99 (2008).

    Article  Google Scholar 

  46. Orger, M. B., Smear, M. C., Anstis, S. M. & Baier, H. Perception of Fourier and non-Fourier motion by larval zebrafish. Nat. Neurosci. 3, 1128–1133 (2000).

    Article  CAS  Google Scholar 

  47. Mareschal, I. & Baker, C. L. Jr Cortical processing of second-order motion. Vis. Neurosci. 16, 527–540 (1999).

    Article  CAS  Google Scholar 

  48. Ramachandran, V. S., Rao, V. M. & Vidyasagar, T. R. Apparent movement with subjective contours. Vision Res. 13, 1399–1401 (1973).

    Article  CAS  Google Scholar 

  49. Hallum, L. E. & Movshon, J. A. Second-order selectivity of single units in macaque primary visual cortex (V1) and V2. J. Vision 11, 1198–1198 (2011).

    Article  Google Scholar 

  50. Tanaka, H. & Ohzawa, I. Surround suppression of V1 neurons mediates orientation-based representation of high-order visual features. J. Neurophysiol. 101, 1444–1462 (2009).

    Article  Google Scholar 

  51. Lewis, J. E. & Maler, L. Neuronal population codes and the perception of distance in weakly electric fish. J. Neurosci. 21, 2842–2850 (2001).

    Article  CAS  Google Scholar 

  52. Peron, S. & Gabbiani, F. Spike frequency adaptation mediates looming stimulus selectivity in a collision-detecting neuron. Nat. Neurosci. 12, 318–326 (2009).

    Article  CAS  Google Scholar 

  53. Geffen, M. N., de Vries, S. E. J. & Meister, M. Retinal ganglion cells can rapidly change polarity from off to on. PLoS Biol. 5, e65 (2007).

    Article  Google Scholar 

  54. Chen, E. Y., Chou, J., Park, J., Schwartz, G. & Berry, M. J. The neural circuit mechanisms underlying the retinal response to motion reversal. J. Neurosci. 34, 15557–15575 (2014). Confirmation of polarity switches in response to motion reversal is provided for mouse and salamander retinal ganglion cells. This paper helps establish the generality of the mechanisms discussed in our Review.

    Article  CAS  Google Scholar 

  55. Tikidji-Hamburyan, A. et al. Retinal output changes qualitatively with every change in ambient illuminance. Nat. Neurosci. 18, 66–74 (2015).

    Article  CAS  Google Scholar 

  56. Zhang, Y., Kim, I. J., Sanes, J. R. & Meister, M. The most numerous ganglion cell type of the mouse retina is a selective feature detector. Proc. Natl Acad. Sci. USA 109, E2391–E2398 (2012).

    Article  CAS  Google Scholar 

  57. Gjorgjieva, J., Sompolinsky, H. & Meister, M. Benefits of pathway splitting in sensory coding. J. Neurosci. 34, 12127–12144 (2014).

    Article  CAS  Google Scholar 

  58. Aumentado-Armstrong, T., Metzen, M. G., Sproule, M. K. J. & Chacron, M. J. Electrosensory midbrain neurons display feature invariant responses to natural communication stimuli. PLoS Comput. Biol. 11, e1004430 (2015). Describes how midbrain neurons with Hodgkin–Huxley dynamics can pool ON and OFF responses to encode an important social signal, regardless of the context in which it occurs.

    Article  Google Scholar 

  59. Tian, B., Kusmierek, P. & Rauschecker, J. P. Analogues of simple and complex cells in rhesus monkey auditory cortex. Proc. Natl Acad. Sci. USA 110, 7892–7897 (2013).

    Article  CAS  Google Scholar 

  60. Robin, D. A. & Royer, F. L. Auditory temporal processing: two-tone flutter fusion and a model of temporal integration. J. Acoust. Soc. Am. 82, 1207–1217 (1987).

    Article  CAS  Google Scholar 

  61. Szwed, M., Bagdasarian, K. & Ahissar, E. Encoding of vibrissal active touch. Neuron 40, 621–630 (2003).

    Article  CAS  Google Scholar 

  62. Clarke, S. E., Longtin, A. & Maler, L. The neural dynamics of sensory focus. Nat. Commun. 6, 8764 (2015).

    Article  CAS  Google Scholar 

  63. Chacron, M. J. Nonlinear information processing in a model sensory system. J. Neurophysiol. 95, 2933–2946 (2006).

    Article  Google Scholar 

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Acknowledgements

S.E.C. is supported by a Frederick Banting and Charles Best Canada Graduate Scholarship from the Canadian Institutes of Health Research. A.L. and L.M. are supported by the Natural Sciences and Engineering Research Council of Canada and the Canadian Institutes of Health Research.

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PowerPoint slides

Glossary

Marr's tri-level hypothesis

Marr proposed that neural function can be analysed at three levels. The computational level addresses what cognitive problems are solved by a neural process (for example, why should visual scenes be segregated on the basis of local contrast). The algorithmic level describes how computational level elements (such as contours) are represented and manipulated to achieve a computational goal. The physical level describes the biophysical and network implementation of these algorithms.

Carrier wave

A high-frequency waveform (often sinusoidal) the amplitude or frequency of which are modulated by an input signa l for the purpose of conveying information to the receiver.

Envelopes

Smooth curves outlining the extremes (such as the peaks) of an oscillating carrier signal.

Sinusoidal AM

An amplitude modulation in the form of a sine wave.

High-pass filters

Filters that pass the components of a signal that are of a higher frequency than a certain cutoff and attenuate signals with frequencies lower than the cutoff frequency.

Looming

The situation in which an object moves towards an animal's body, perpendicular to the sensory surface (skin or retina). In visual neuroscience, it specifically refers to the expansion of the retinal image as an object approaches the body.

Power law relationship

A functional relationship between two quantities, where one quantity varies as a power (or powers) of another. It can be written as y = tc.

Receptive field

A property of sensory neurons that encode spatial information, referring to a particular point on the sensory surface where stimulation alters the rate at which the neuron fires action potentials.

White noise

A signal the samples of which can be described as a sequence of serially uncorrelated random values, often drawn from a normal (bell shaped) distribution; the resulting signal contains equal power at all frequencies.

Half-wave rectification

A process by which either the positive or negative components of a signal are transmitted. In neurons, the positive (depolarizing) component of an input signal triggers spiking, whereas the hyperpolarizing current does not drive spiking and is thus poorly encoded.

Low-pass filtering

A low-pass filter will transmit the low frequencies present in a complex signal, while attenuating its high-frequency components.

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Clarke, S., Longtin, A. & Maler, L. Contrast coding in the electrosensory system: parallels with visual computation. Nat Rev Neurosci 16, 733–744 (2015). https://doi.org/10.1038/nrn4037

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