Abstract
Detection of looming obstacles is a vital task for both natural and artificial systems. Locusts possess a visual nervous system with an extensively studied obstacle detection pathway, culminating in the lobula giant movement detector (LGMD) neuron. While numerous models of this system exist, none to date have incorporated recent data on the anatomy and function of feedforward and global inhibitory systems in the input network of the LGMD. Moreover, the possibility that global and lateral inhibition shape the feedforward inhibitory signals to the LGMD has not been investigated. To address these points, a novel model of feedforward inhibitory neurons in the locust optic lobe was developed based on the recent literature. This model also incorporated global and lateral inhibition into the afferent network of these neurons, based on their observed behaviour in existing data and the posited role of these mechanisms in the inputs to the LGMD. Tests with the model showed that it accurately replicates the behaviour of feedforward inhibitory neurons in locusts; the model accurately coded for stimulus angular size in an overall linear fashion, with decreasing response saturation and increasing linearity as stimulus size increased or approach velocity decreased. The model also exhibited only phasic responses to the appearance of a grating, along with sustained movement by it at constant speed. By observing the effects of altering inhibition schemes on these responses, it was determined that global inhibition serves primarily to normalize growing excitation as collision approaches, and keeps coding for subtense angle linear. Lateral inhibition was determined to suppress tonic responses to wide-field stimuli translating at constant speed. Based on these features being shared with characterizations of the LGMD input network, it was hypothesized that the feedforward inhibitory neurons and the LGMD share the same excitatory afferents; this necessitates further investigation.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Bazhenov M, Stopfer M, Rabinovich M, Huerta R, Abarbanel HDI, Sejnowski TJ, Laurent G (2001) Model of transient oscillatory synchronization in the locust antennal lobe. Neuron 30(2):553–567. https://doi.org/10.1016/S0896-6273(01)00284-7
Bermúdez i Badia S, Pyk P, Verschure PFMJ (2007) A fly-locust based neuronal control system applied to an unmanned aerial vehicle: the invertebrate neuronal principles for course stabilization, altitude control and collision avoidance. Int J Robot Res 26(7):759–772. https://doi.org/10.1177/0278364907080253
Bermúdez i Badia S, Bernardet U, Verschure PFMJ (2010) Non-linear neuronal responses as an emergent property of afferent networks: a case study of the locust Lobula giant movement detector. PLoS Comput Biol 6(3):e1000701. https://doi.org/10.1371/journal.pcbi.1000701
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(15):1389–1398. https://doi.org/10.1016/j.cub.2013.05.061
Dayan P, Abbott LF (2001) Theoretical Neuroscience: Computational and Mathematical Modeling of Neural Systems. Computational Neuroscience, Massachusetts Institute of Technology Press, Cambridge
Dewell RB, Gabbiani F (2018) Biophysics of object segmentation in a collision-detecting neuron. eLife 7:e34238. https://doi.org/10.7554/eLife.34238
Dewell RB, Gabbiani F (2018) M current regulates firing mode and spike reliability in a collision-detecting neuron. J Neurophysiol 120(4):1753–1764. https://doi.org/10.1152/jn.00363.2018
Elphick M, Williams L, Shea M (1996) New features of the locust optic lobe: evidence of a role for nitric oxide in insect vision. J Exp Biol 199(11):2395–2407
Fourcaud-Trocmé N, Hansel D, van Vreeswijk C, Brunel N (2003) How spike generation mechanisms determine the neuronal response to fluctuating inputs. J Neurosci 23(37):11628–11640. https://doi.org/10.1523/JNEUROSCI.23-37-11628.2003
Gabbiani F, Krapp HG, Laurent G (1999) Computation of object approach by a wide-field, motion-sensitive neuron. J Neurosci 19(3):1122–1141. https://doi.org/10.1523/JNEUROSCI.19-03-01122.1999
Gabbiani F, Krapp HG, Koch C, Laurent G (2002) Multiplicative computation in a visual neuron sensitive to looming. Nature 420(6913):320. https://doi.org/10.1038/nature01190
Gabbiani F, Cohen I, Laurent G (2005) Time-dependent activation of feed-forward inhibition in a looming-sensitive neuron. J Neurophysiol 94(3):2150–2161. https://doi.org/10.1152/jn.00411.2005
Hatsopoulos N, Gabbiani F, Laurent G (1995) Elementary computation of object approach by a wide-field visual neuron. Science 270(5238):1000–1003. https://doi.org/10.1126/science.270.5238.1000
Homberg U, Würden S (1997) Movement-sensitive, polarization-sensitive, and light-sensitive neurons of the medulla and accessory medulla of the locust, Schistocerca gregaria. Journal of Comparative Neurology 386(3):329–346. https://doi.org/10.1002/(SICI)1096-9861(19970929)386:33c329::AID-CNE13e3.0.CO;2-3
Horridge GA (1978) The separation of visual axes in apposition compound eyes. Philos Trans R Soc Lond B 285(1003):1–59. https://doi.org/10.1098/rstb.1978.0093
Izhikevich E (2003) Simple model of spiking neurons. IEEE Trans Neural Netw 14(6):1569–1572. https://doi.org/10.1109/TNN.2003.820440
Jones PW, Gabbiani F (2010) Synchronized neural input shapes stimulus selectivity in a collision-detecting neuron. Curr Biol 20(22):2052–2057. https://doi.org/10.1016/j.cub.2010.10.025
Jones PW, Gabbiani F (2011) Impact of neural noise on a sensory-motor pathway signaling impending collision. J Neurophysiol 107(4):1067–1079. https://doi.org/10.1152/jn.00607.2011
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. https://doi.org/10.1523/JNEUROSCI.5777-11.2012
Judge S, Rind F (1997) The locust DCMD, a movement-detecting neurone tightly tuned to collision trajectories. J Exp Biol 200(16):2209–2216
Krapp HG, Gabbiani F (2005) Spatial distribution of inputs and local receptive field properties of a wide-field, looming sensitive neuron. J Neurophysiol 93(4):2240–2253. https://doi.org/10.1152/jn.00965.2004
Naud R, Marcille N, Clopath C, Gerstner W (2008) Firing patterns in the adaptive exponential integrate-and-fire model. Biol Cybern 99(4–5):335–347. https://doi.org/10.1007/s00422-008-0264-7
O’Shea M, Rowell CHF (1975) Protection from habituation by lateral inhibition. Nature 254(5495):53–55. https://doi.org/10.1038/254053a0
Peron S, Gabbiani F (2009) Role of spike-frequency adaptation in shaping neuronal response to dynamic stimuli. Biol Cybern 100(6):505–520. https://doi.org/10.1007/s00422-009-0304-y
Peron S, Gabbiani F (2009) Spike frequency adaptation mediates looming stimulus selectivity in a collision-detecting neuron. Nat Neurosci 12(3):318–326. https://doi.org/10.1038/nn.2259
Peron S, Jones PW, Gabbiani F (2009) Precise subcellular input retinotopy and its computational consequences in an identified visual interneuron. Neuron 63(6):830–842. https://doi.org/10.1016/j.neuron.2009.09.010
Peron SP, Krapp HG, Gabbiani F (2007) Influence of electrotonic structure and synaptic mapping on the receptive field properties of a collision-detecting neuron. J Neurophysiol 97(1):159–177. https://doi.org/10.1152/jn.00660.2006
Rind FC (1984) A chemical synapse between two motion detecting neurones in the locust brain. J Exp Biol 110(1):143–167
Rind FC, Bramwell DI (1996) Neural network based on the input organization of an identified neuron signaling impending collision. J Neurophysiol 75(3):967–985
Rind FC, Simmons PJ (1998) Local circuit for the computation of object approach by an identified visual neuron in the locust. J Comp Neurol 395(3):405–415. https://doi.org/10.1002/(SICI)1096-9861(19980808)395:33c405::AID-CNE93e3.0.CO;2-6
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. https://doi.org/10.1038/srep35525
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(1):157–185
Sağlam M, Hayashida Y (2018) A single retinal circuit model for multiple computations. Biol Cybern 112(5):427–444. https://doi.org/10.1007/s00422-018-0767-9
Santer RD, Rind FC, Stafford R, Simmons PJ (2006) Role of an identified looming-sensitive neuron in triggering a flying Locust’s escape. J Neurophysiol 95(6):3391–3400. https://doi.org/10.1152/jn.00024.2006
Santer RD, Rind FC, Simmons PJ (2012) Predator versus prey: locust looming-detector neuron and behavioural responses to stimuli representing attacking bird predators. PLoS ONE 7(11):e50146. https://doi.org/10.1371/journal.pone.0050146
Schlotterer GR (1977) Response of the locust descending movement detector neuron to rapidly approaching and withdrawing visual stimuli. Can J Zool 55(8):1372–1376. https://doi.org/10.1139/z77-179
Simmons PJ, Rind FC (1992) Orthopteran DCMD neuron: a reevaluation of responses to moving objects. II. Critical cues for detecting approaching objects. J Neurophysiol 68(5):1667–1682. https://doi.org/10.1152/jn.1992.68.5.1667
Stein RB, Gossen ER, Jones KE (2005) Neuronal variability: noise or part of the signal? Nat Rev Neurosci 6(5):389–397. https://doi.org/10.1038/nrn1668
Stott TP, Olson EGN, Parkinson RH, Gray JR (2018) Three-dimensional shape and velocity changes affect responses of a locust visual interneuron to approaching objects. J Exp Biol 221:191320. https://doi.org/10.1242/jeb.191320
Wang H, Dewell RB, Zhu Y, Gabbiani F (2018) Feedforward Inhibition Conveys Time-Varying Stimulus Information in a Collision Detection Circuit. Curr Biol 28(10):1509–1521. https://doi.org/10.1016/j.cub.2018.04.007
Zhu Y, Gabbiani F (2016) Fine and distributed subcellular retinotopy of excitatory inputs to the dendritic tree of a collision-detecting neuron. J Neurophysiol 115(6):3101–3112. https://doi.org/10.1152/jn.00044.2016
Zhu Y, Dewell RB, Wang H, Gabbiani F (2018) Pre-synaptic muscarinic excitation enhances the discrimination of looming stimuli in a collision-detection neuron. Cell Rep 23(8):2365–2378. https://doi.org/10.1016/j.celrep.2018.04.079
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of interest
The authors declare that they have no conflict of interest.
Additional information
Communicated by Benjamin Lindner.
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Olson, E.G.N., Wiens, T.K. & Gray, J.R. A model of feedforward, global, and lateral inhibition in the locust visual system predicts responses to looming stimuli. Biol Cybern 115, 245–265 (2021). https://doi.org/10.1007/s00422-021-00876-8
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00422-021-00876-8