Object approach computation by a giant neuron and its relation with the speed of escape in the crab Neohelice

Upon detection of an approaching object, the crab Neohelice granulata continuously regulates the direction and speed of escape according to ongoing visual information. These visuomotor transformations are thought to be largely accounted for by a small number of motion-sensitive giant neurons projecting from the lobula (third optic neuropil) towards the supraesophageal ganglion. One of these elements, the monostratified lobula giant neuron of type 2 (MLG2), proved to be highly sensitive to looming stimuli (a 2D representation of an object approach). By performing in vivo intracellular recordings, we assessed the response of the MLG2 neuron to a variety of looming stimuli representing objects of different sizes and velocities of approach. This allowed us to: (1) identify some of the physiological mechanisms involved in the regulation of the MLG2 activity and test a simplified biophysical model of its response to looming stimuli; (2) identify the stimulus optical parameters encoded by the MLG2 and formulate a phenomenological model able to predict the temporal course of the neural firing responses to all looming stimuli; and (3) incorporate the MLG2-encoded information of the stimulus (in terms of firing rate) into a mathematical model able to fit the speed of the escape run of the animal. The agreement between the model predictions and the actual escape speed measured on a treadmill for all tested stimuli strengthens our interpretation of the computations performed by the MLG2 and of the involvement of this neuron in the regulation of the animal's speed of run while escaping from objects approaching with constant speed.Fil: Oliva, Damian Ernesto. Universidad Nacional de Quilmes. Departamento de Ciencia y Tecnología; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Tomsic, Daniel. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Fisiología, Biología Molecular y Neurociencias. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Fisiología, Biología Molecular y Neurociencias; Argentin

A modified model for the Lobula Giant Movement Detector and its FPGA implementation

The Lobula Giant Movement Detector (LGMD) is a wide-field visual neuron located in the Lobula layer of the Locust nervous system. The LGMD increases its firing rate in response to both the velocity of an approaching object and the proximity of this object. It has been found that it can respond to looming stimuli very quickly and trigger avoidance reactions. It has been successfully applied in visual collision avoidance systems for vehicles and robots. This paper introduces a modified neural model for LGMD that provides additional depth direction information for the movement. The proposed model retains the simplicity of the previous model by adding only a few new cells. It has been simplified and implemented on a Field Programmable Gate Array (FPGA), taking advantage of the inherent parallelism exhibited by the LGMD, and tested on real-time video streams. Experimental results demonstrate the effectiveness as a fast motion detector

Neural networks application to divergence-based passive ranging

The purpose of this report is to summarize the state of knowledge and outline the planned work in divergence-based/neural networks approach to the problem of passive ranging derived from optical flow. Work in this and closely related areas is reviewed in order to provide the necessary background for further developments. New ideas about devising a monocular passive-ranging system are then introduced. It is shown that image-plan divergence is independent of image-plan location with respect to the focus of expansion and of camera maneuvers because it directly measures the object's expansion which, in turn, is related to the time-to-collision. Thus, a divergence-based method has the potential of providing a reliable range complementing other monocular passive-ranging methods which encounter difficulties in image areas close to the focus of expansion. Image-plan divergence can be thought of as some spatial/temporal pattern. A neural network realization was chosen for this task because neural networks have generally performed well in various other pattern recognition applications. The main goal of this work is to teach a neural network to derive the divergence from the imagery

A modified neural network model for Lobula Giant Movement Detector with additional depth movement feature

The Lobula Giant Movement Detector (LGMD) is a wide-field visual neuron that is located in the Lobula layer of the Locust nervous system. The LGMD increases its firing rate in response to both the velocity of the approaching object and its proximity. It has been found that it can respond to looming stimuli very quickly and can trigger avoidance reactions whenever a rapidly approaching object is detected. It has been successfully applied in visual collision avoidance systems for vehicles and robots. This paper proposes a modified LGMD model that provides additional movement depth direction information. The proposed model retains the simplicity of the previous neural network model, adding only a few new cells. It has been tested on both simulated and recorded video data sets. The experimental results shows that the modified model can very efficiently provide stable information on the depth direction of movement

Spatial vision in insects is facilitated by shaping the dynamics of visual input through behavioral action

Egelhaaf M, Boeddeker N, Kern R, Kurtz R, Lindemann JP. Spatial vision in insects is facilitated by shaping the dynamics of visual input through behavioral action. Frontiers in Neural Circuits. 2012;6:108.Insects such as flies or bees, with their miniature brains, are able to control highly aerobatic flight maneuvres and to solve spatial vision tasks, such as avoiding collisions with obstacles, landing on objects, or even localizing a previously learnt inconspicuous goal on the basis of environmental cues. With regard to solving such spatial tasks, these insects still outperform man-made autonomous flying systems. To accomplish their extraordinary performance, flies and bees have been shown by their characteristic behavioral actions to actively shape the dynamics of the image flow on their eyes ("optic flow"). The neural processing of information about the spatial layout of the environment is greatly facilitated by segregating the rotational from the translational optic flow component through a saccadic flight and gaze strategy. This active vision strategy thus enables the nervous system to solve apparently complex spatial vision tasks in a particularly efficient and parsimonious way. The key idea of this review is that biological agents, such as flies or bees, acquire at least part of their strength as autonomous systems through active interactions with their environment and not by simply processing passively gained information about the world. These agent-environment interactions lead to adaptive behavior in surroundings of a wide range of complexity. Animals with even tiny brains, such as insects, are capable of performing extraordinarily well in their behavioral contexts by making optimal use of the closed action-perception loop. Model simulations and robotic implementations show that the smart biological mechanisms of motion computation and visually-guided flight control might be helpful to find technical solutions, for example, when designing micro air vehicles carrying a miniaturized, low-weight on-board processor

Bio-inspired collision detector with enhanced selectivity for ground robotic vision system

There are many ways of building collision-detecting systems. In this paper, we propose a novel collision selective visual neural network inspired by LGMD2 neurons in the juvenile locusts. Such collision-sensitive neuron matures early in the first-aged or even hatching locusts, and is only selective to detect looming dark objects against bright background in depth, represents swooping predators, a situation which is similar to ground robots or vehicles. However, little has been done on modeling LGMD2, let alone its potential applications in robotics and other vision-based areas. Compared to other collision detectors, our major contributions are first, enhancing the collision selectivity in a bio-inspired way, via constructing a computing efficient visual sensor, and realizing the revealed specific characteristic sofLGMD2. Second, we applied the neural network to help rearrange path navigation of an autonomous ground miniature robot in an arena. We also examined its neural properties through systematic experiments challenged against image streams from a visual sensor of the micro-robot

Modelling LGMD2 visual neuron system

Two Lobula Giant Movement Detectors (LGMDs) have been identified in the lobula region of the locust visual system: LGMD1 and LGMD2. LGMD1 had been successfully used in robot navigation to avoid impending collision. LGMD2 also responds to looming stimuli in depth, and shares most the same properties with LGMD1; however, LGMD2 has its specific collision selective responds when dealing with different visual stimulus. Therefore, in this paper, we propose a novel way to model LGMD2, in order to emulate its predicted bio-functions, moreover, to solve some defects of previous LGMD1 computational models. The mechanism of ON and OFF cells, as well as bioinspired nonlinear functions, are introduced in our model, to achieve LGMD2’s collision selectivity. Our model has been tested by a miniature mobile robot in real time. The results suggested this model has an ideal performance in both software and hardware for collision recognition

Sensory coding of complex visual motion in the locust (Locusta migratoria)

The visual environment of any animal is a complex amalgamation of sensory information (Lochmann and Deneve, 2011); however, it is adaptive for an animal to only react to salient cues (Zupanc, 2010). For many organisms, the detection of an approaching object, such as an oncoming conspecific or a predator, is particularly important. An approaching object with constant velocity is called looming, and has been widely studied for evoking avoidance behaviours in a number of animal species (Gibson, 1958). The migratory locust, Locusta migratoria, has been used extensively as a model system for visually guided behaviour, due to its robust collision-avoidance behaviours and its tractable nervous system (Schlotterer, 1977). The Lobula Giant Movement Detector (LGMD) and the Descending Contralateral Movement Detector (DCMD) constitute one pathway in the locust visual system that integrates the entire field of view that has been implicated in coordinating these types of behaviours (Santer et al., 2006). Previous studies have found that the LGMD/DCMD pathway responds to many visual stimuli, including complex scenes (Rind and Simmons, 1992), approaching paired objects (Guest and Gray, 2006), objects with compound shapes (Guest and Gray, 2006), and objects that follow compound trajectories (McMillan and Gray, 2012). These findings suggest that this pathway is capable of encoding complex motion such as exists in the locust’s natural environment. In my first objective (Chapter 2), I tested the response of the locust DCMD to increasingly complex motion. Using computer generated disks that followed compound trajectories with different velocities, I demonstrate that the DCMD is capable of encoding the location, trajectory, and velocity of an approaching object through aspects of the response profile over time. The motor systems of invertebrates are often controlled by ensembles of neurons working together (Dubuc et al., 2008; Hedrich et al., 2011; Gonzalez-Bellido et al., 2013). The locust visual system has at least five identified descending neurons, beyond the DCMD, that respond to visual motion (Rowell, 1971; Griss and Rowell, 1986; Gray et al., 2010). Due to the tractability of extracellular recordings of the DCMD, these neurons remain relatively little studied. Furthermore, their responses to stimuli have not been investigated concurrently. With recent advancements in multichannel recordings and spike sorting algorithms, it is now possible to explore the responses of multiple neurons in the locust system together. In my second objective (Chapter 3), I recorded from the connective of the locust using multichannel electrodes while challenging it with a wide array of visual stimuli. Preliminary results of these experiments identified as many as five neuronal units with distinctive firing patterns, some which appear to be novel. Together, these results illustrate that the locust visual system is more complex than previously thought, through both the abilities of a single neuron to encode many aspects of visual motion and the presence of multiple unique, visually-sensitive neurons