Abstract
Looming-sensitive neurons (LSNs) are motion-sensitive neurons tuned for detecting imminent collision. Their main characteristic is the selectivity to looming (a 2D representation of an object approach), rather than to receding stimuli. We studied a set of LSNs by performing surface extracellular recordings in the optic nerve of Neohelice granulata crabs, and characterized their response against computer-generated visual stimuli with different combinations of moving edges, highlighting different components of the optical flow. In addition to their selectivity to looming stimuli, we characterized other properties of these neurons, such as low directionality; reduced response to sustained excitement; and an inhibition phenomenon in response to visual stimuli with dense optical flow of expansion, contraction, and translation. To analyze the spatio-temporal processing of these LSNs, we proposed a biologically plausible computational model which was inspired by previous computational models of the locust lobula giant motion detector (LGMD) neuron. The videos seen by the animal during electrophysiological experiments were applied as an input to the model which produced a satisfactory fit to the measured responses, suggesting that the computation performed by LSNs in a decapod crustacean appears to be based on similar physiological processing previously described for the LGMD in insects.
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Abbreviations
- DCMD:
-
Descending contralateral movement detector
- FFE:
-
Feed-forward excitation
- FFI:
-
Feed-forward inhibition
- LGMD:
-
Lobula giant motion detector
- LI:
-
Lateral inhibition
- LSN:
-
Looming-sensitive neuron
- MLG:
-
Monostratified lobula giant
- MSN:
-
Motion-sensitive neuron
- OF:
-
Optical flow
- ROF:
-
Rotational optical flow
- TOF:
-
Translational optical flow
References
Berón de Astrada M, Tomsic D (2002) Physiology and morphology of visual movement detector neurons in a crab (Decapoda: Brachyura). J Comp Physiol A 188:539–551. https://doi.org/10.1007/s00359-002-0328-4
Berón de Astrada M, Bengochea M, Medan V, Tomsic D (2012) Regionalization in the eye of the grapsid crab Neohelice granulata (= Chasmagnathus granulatus): variation of resolution and facet diameters. J Comp Physiol A 198:173–180. https://doi.org/10.1007/s00359-011-0697-7
Berón de Astrada M, Bengochea M, Sztarker J, Delorenzi A, Tomsic D (2013) Behaviorally related neural plasticity in the arthropod optic lobes. Curr Biol 23:1389–1398. https://doi.org/10.1016/j.cub.2013.05.061
Borst A (1991) Fly visual interneurones responsive to image expansion. Zool Jahrb Physiol 95:305–313
Borst A, Haag J (2002) Neural networks in the cockpit of the fly. J Comp Physiol A 188:419–437. https://doi.org/10.1007/s00359-002-0316-8
Dunn TW, Gebhardt C, Naumann EA, Riegler C, Ahrens MB, Engert F, Del Bene F (2016) Neural circuits underlying visually evoked escapes in larval zebrafish. Neuron 89:613–628. https://doi.org/10.1016/j.neuron.2015.12.021
Fotowat H, Gabbiani F (2011) Collision detection as a model for sensory-motor integration. Annu Rev Neurosc 4:1–19. https://doi.org/10.1146/annurev-neuro-061010-113632
Fotowat H, Harrison R, Gabbiani F (2011) Multiplexing of motor information in the discharge of a collision detecting neuron during escape behaviors. Neuron 69:147–158. https://doi.org/10.1016/j.neuron.2010.12.007
Gabbiani F, Krapp HG, Laurent G (1999) Computation of object approach by a wide field, motion-sensitive neuron. J Neurosci 19:1122–1141
Gabbiani F, Krapp HG, Koch C, Laurent G (2002) Multiplicative computation in a visual neuron sensitive to looming. Nature 21:320–324. https://doi.org/10.1038/nature01190
Glantz RM (1974) Defense reflex and motion detector responsiveness to approaching targets: the motion detector trigger to the defense reflex pathway. J Comp Physiol 95:297–314
Gray JR, Blincow E, Robertson RM (2010) A pair of motion-sensitive neurons in the locust encode approaches of a looming object. J Comp Physiol A 196:927–938. https://doi.org/10.1007/s00359-010-0576-7
Hemmi JM, Tomsic D (2012) The neuroethology of escape in crabs: from sensory ecology to neurons and back. Curr Opin Neurobiol 22:194–200. https://doi.org/10.1016/j.conb.2011.11.012
Horseman BG, Macauley MW, Barnes WJP (2011) Neuronal processing of translational optic flow in the visual system of the shore crab Carcinus maenas. J Exp Biol 214(9):1586–1598
Jones PW, Gabbiani F (2010) Synchronized neural input shapes stimulus selectivity in a collision-detecting neuron. Curr Biol 20(22):2052–2057
Jones PW, Gabbiani F (2012) Logarithmic compression of sensory signals within the dendritic tree of a collision-sensitive neuron. J Neurosci 32(14):4923–4934
Kostarakos K, Hedwig B (2017) Surface electrodes record and label brain neurons in insects. J Neurophysiol. https://doi.org/10.1152/jn.00490.2017
Krapp HG, Gabbiani F (2005) Spatial distribution of inputs and local receptive field properties of a wide-field, looming sensitive neuron. J Neurophysiol 93:2240–2253. https://doi.org/10.1152/jn.00965.2004
Krapp HG, Hengstenberg R (1996) Estimation of self-motion by optic flow processing in single visual interneurons. Nature 384:463–466. https://doi.org/10.1038/384463a0
Laurent G, Gabbiani F (1998) Collision-avoidance: nature’s many solutions. Nat Neurosci 1:261–263
Medan V, Oliva D, Tomsic D (2007) Characterization of lobula giant neurons responsive to visual stimuli that elicit escape reactions in the crab Chasmagnathus. J Neurophys 98:2414–2428. https://doi.org/10.1152/jn.00803.2007
Nelson RC, Aloimonos J (1988) Finding motion parameters from spherical motion fields (or the advantages of having eyes in the back of your head). Biol Cybern 58:261–273
Nilsson DE, Osorio D (1998) Homology and parallelism in arthropod sensory processing. Arthropod relationships. Springer, Netherlands, pp 333–347
O’Shea M, Rowell CHF (1975) Protection from habituation by lateral inhibition. Nature 254:53–55
Oliva D (2015) Collision avoidance models, visually guided. In: Jaeger D, Jung R (eds) Encyclopedia of computational neuroscience. Springer-Verlag, Berlin Heidelberg, pp 626–645
Oliva D, Tomsic D (2014) Computation of object approach by a system of visual motion-sensitive neurons in the crab Neohelice. J Neurophysiol 112:1477–1490. https://doi.org/10.1152/jn.00921.2013
Oliva D, Tomsic D (2016) Object approach computation by a giant neuron and its relationship with the speed of escape in the crab Neohelice. J Exp Biol 219:3339–3352. https://doi.org/10.1242/jeb.136820
Oliva D, Medan V, Tomsic D (2007) Escape behaviour and neuronal responses to looming stimuli in the crab Chasmagnathus granulatus (Decapoda: Grapsidae). J Exp Biol 210:865–880. https://doi.org/10.1242/jeb.02707
Peron SP, Gabbiani F (2009) Spike frequency adaptation mediates looming stimulus selectivity in a collision detecting neuron. Nat Neurosci 12:318–326. https://doi.org/10.1038/nn.2259
Preuss T, Osei-Bonsu PE, Weiss SA, Wang C, Faber DS (2006) Neural representation of object approach in a decision-making motor circuit. J Neurosci 26:3454–3464. https://doi.org/10.1523/JNEUROSCI.5259-05.2006
Quian Quiroga R (2007) Spike sorting. Scholarpedia 2(12):3583. https://doi.org/10.4249/scholarpedia.3583
Quiroga RQ, Nadasdy Z, Ben-Shaul Y (2004) Unsupervised spike detection and sorting with wavelets and superparamagnetic clustering. Neural Comput 16:1661–1687. https://doi.org/10.1162/089976604774201631
Rind FC, Bramwell DI (1996) Neural network based on the input organization of an identified neuron signaling impending collision. J Neurophysiol 75:967–985
Rind FC, Simmons PJ (1992) Orthopteran DCMD neuron: a reevaluation of responses to moving objects. I. Selective responses to approaching objects. J Neurophysiol 68:1654–1666
Rind FC, Simmons PJ (1999) Seeing what is coming: building collision-sensitive neurones. Trends Neurosci 22:215–20
Rind FC, Wernitznig S, Pölt P, Zankel A, Gütl D, Sztarker J, Leitinger G (2016) Two identified looming detectors in the locust: ubiquitous lateral connections among their inputs contribute to selective responses to looming objects. Sci Rep 6:35525. https://doi.org/10.1038/srep35525
Rosner R, Homberg U (2013) Widespread sensitivity to looming stimuli and small moving objects in the central complex of an insect brain. J Neurosci 33(19):8122–8133
Rowell CF, O’Shea M, Williams JL (1977) The neuronal basis of a sensory analyser, the acridid movement detector system. IV. The preference for small field stimuli. J Exp Biol 68:157–185
Santer RD, Yamawaki Y, Rind FC, Simmons PJ (2008) Preparing for escape: an examination of the role of the DCMD neuron in locust escape jumps. J Comp Physiol A 194:69–77. https://doi.org/10.1007/s00359-007-0289-8
Srinivasan MV, Zhang S (2004) Visual motor computations in insects. Annu Rev Neurosci 27:679–696. https://doi.org/10.1146/annurev.neuro.27.070203.144343
Strausfeld NJ (2005) The evolution of crustacean and insect optic lobes and the origins of chiasmata. Arthropod Struct Dev 34:235–256
Sztarker J, Strausfeld NJ, Tomsic D (2005) Organization of optic lobes that support motion detection in a semiterrestrial crab. J Comp Neurol 493:396–411. https://doi.org/10.1002/cne.20755
Tomsic D, Sztarker J, Berón de Astrada MB, Oliva D, Lanza E (2017) The predator and prey behaviors of crabs: from ecology to neural adaptations. J Exp Biol 220(13):2318–2327
Wang Y, Frost BJ (1992) Time to collision is signalled by neurons in the nucleus rotundus of pigeons. Nature 356:236–238. https://doi.org/10.1038/356236a0
Wasserman L (2004) All of statistics: a concise course in statistical inference. Springer, New York
Wild J, Prekopcsak Z, Sieger T, Novak D, Jech R (2012) Performance comparison of extracellular spike sorting algorithms for single-channel recordings. J Neurosci Methods 203:369–376. https://doi.org/10.1016/j.jneumeth.2011.10.013
Yamawaki Y, Toh Y (2009) Responses of descending neurons to looming stimuli in the praying mantis Tenodera aridifolia. J Comp Physiol A 195:253–264. https://doi.org/10.1007/s00359-008-0403-6
Acknowledgements
We thank D. Tomsic, M. Berón de Astrada and F. Magani for fruitful discussions and corrections to this manuscript. This work describes research partially funded by National Council of Scientific and Technical Research (CONICET), National Agency of Science and Technology (ANPCyT), Grant Number PICT 2012-2765.
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All experiments on animals described above were performed in accordance with applicable national legislation and institutional guidelines for the care and use of animals.
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Carbone, J., Yabo, A. & Oliva, D. Characterization and modelling of looming-sensitive neurons in the crab Neohelice. J Comp Physiol A 204, 487–503 (2018). https://doi.org/10.1007/s00359-018-1257-1
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DOI: https://doi.org/10.1007/s00359-018-1257-1