Literature DB >> 8867110

Neural network based on the input organization of an identified neuron signaling impending collision.

F C Rind1, D I Bramwell.   

Abstract

1. We describe a four-layered neural network (Fig. 1), based on the input organization of a collision signaling neuron in the visual system of the locust, the lobula giant movement detector (LGMD). The 250 photoreceptors ("P" units) in layer 1 are excited by any change in illumination, generated when an image edge passes over them. Layers 2 and 3 incorporate both excitatory and inhibitory interactions, and layer 4 consists of a single output element, equivalent to the locust LGMD. 2. The output element of the neural network, the "LGMD", responds directionally when challenged with approaching versus receding objects, preferring approaching objects (Figs. 2-4). The time course and shape of the "LGMD" response matches that of the LGMD (Fig. 4). Directionality is maintained with objects of various sizes and approach velocities. The network is tuned to direct approach (Fig. 5). The "LGMD" shows no directional selectivity for translatory motion at a constant velocity across the "eye", but its response increases with edge velocity (Figs. 6 and 9). 3. The critical image cues for a selective response to object approach by the "LGMD" are edges that change in extent or in velocity as they move (Fig. 7). Lateral inhibition is crucial to the selectivity of the "LGMD" and the selective response is abolished or else much reduced if lateral inhibition is taken out of the network (Fig. 7). We conclude that lateral inhibition in the neuronal network for the locust LGMD also underlies the experimentally observed critical image cues for its directional response. 4. Lateral inhibition shapes the velocity tuning of the network for objects moving in the X and Y directions without approaching the eye (see Fig. 1). As an edge moves over the eye at a constant velocity, a race occurs between the excitation that is caused by edge movement and which passes down the network and the inhibition that passes laterally. Excitation must win this race for units in layer 3 to reach threshold (Fig. 8). The faster the edge moves over the eye the more units in layer 3 reach threshold and pass excitation on to the "LGMD" (Fig. 9). 5. Lateral inhibition shapes the tuning of the network for objects moving in the Z direction, toward or away from the eye (see Fig. 1). As an object approaches the eye there is a buildup of excitation in the "LGMD" throughout the movement whereas the response to object recession is often brief, particularly for high velocities. During object motion, a critical race occurs between excitation passing down the network and inhibition directed laterally, excitation must win this race for the rapid buildup in excitation in the "LGMD" as seen in the final stages of object approach (Figs. 10-12). The buildup is eliminated if, during object approach, excitation cannot win this race (as happens when the spread of inhibition laterally takes < 1 ms Fig. 13, D and E). Taking all lateral inhibition away increases the "LGMD" response to object approach, but overall directional selectivity is reduced as there is also a lot of residual network excitation following object recession (Fig. 13B). 6. Directional selectivity for rapidly approaching objects is further enhanced at the level of the "LGMD" by the timing of a feed-forward, inhibitory loop onto the "LGMD", activated when a large number of receptor units are excited in a short time. The inhibitory loop is activated at the end of object approach, truncating the excitatory "LGMD" response after approach has ceased, but at the initiation of object recession (*Fig. 2, 3, and 13). Eliminating the feed-forward, inhibitory loop prolongs the "LGMD" response to both receding and approaching objects (Fig. 13F).

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Year:  1996        PMID: 8867110     DOI: 10.1152/jn.1996.75.3.967

Source DB:  PubMed          Journal:  J Neurophysiol        ISSN: 0022-3077            Impact factor:   2.714


  22 in total

1.  A pair of motion-sensitive neurons in the locust encode approaches of a looming object.

Authors:  John R Gray; Eric Blincow; R Meldrum Robertson
Journal:  J Comp Physiol A Neuroethol Sens Neural Behav Physiol       Date:  2010-09-09       Impact factor: 1.836

2.  Time-dependent activation of feed-forward inhibition in a looming-sensitive neuron.

Authors:  Fabrizio Gabbiani; Ivan Cohen; Gilles Laurent
Journal:  J Neurophysiol       Date:  2005-05-31       Impact factor: 2.714

3.  Influence of electrotonic structure and synaptic mapping on the receptive field properties of a collision-detecting neuron.

Authors:  Simon P Peron; Holger G Krapp; Fabrizio Gabbiani
Journal:  J Neurophysiol       Date:  2006-10-04       Impact factor: 2.714

4.  Motion dazzle: a locust's eye view.

Authors:  Roger D Santer
Journal:  Biol Lett       Date:  2013-12-04       Impact factor: 3.703

5.  Computation of object approach by a wide-field, motion-sensitive neuron.

Authors:  F Gabbiani; H G Krapp; G Laurent
Journal:  J Neurosci       Date:  1999-02-01       Impact factor: 6.167

6.  Properties of neuronal facilitation that improve target tracking in natural pursuit simulations.

Authors:  Zahra M Bagheri; Steven D Wiederman; Benjamin S Cazzolato; Steven Grainger; David C O'Carroll
Journal:  J R Soc Interface       Date:  2015-07-06       Impact factor: 4.118

7.  Interaction of compass sensing and object-motion detection in the locust central complex.

Authors:  Tobias Bockhorst; Uwe Homberg
Journal:  J Neurophysiol       Date:  2017-04-12       Impact factor: 2.714

8.  Spatiotemporal receptive field properties of a looming-sensitive neuron in solitarious and gregarious phases of the desert locust.

Authors:  Stephen M Rogers; George W J Harston; Fleur Kilburn-Toppin; Thomas Matheson; Malcolm Burrows; Fabrizio Gabbiani; Holger G Krapp
Journal:  J Neurophysiol       Date:  2009-12-02       Impact factor: 2.714

9.  Non-linear neuronal responses as an emergent property of afferent networks: a case study of the locust lobula giant movement detector.

Authors:  Sergi Bermúdez i Badia; Ulysses Bernardet; Paul F M J Verschure
Journal:  PLoS Comput Biol       Date:  2010-03-12       Impact factor: 4.475

10.  Collision avoidance and a looming sensitive neuron: size matters but biggest is not necessarily best.

Authors:  F Claire Rind; Roger D Santer
Journal:  Proc Biol Sci       Date:  2004-02-07       Impact factor: 5.349

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