Abstract:To analyze the coding properties of neuronal populations sensory stimuli have usually been used that were much simpler than those encountered in real life. It has been possible only recently to stimulate visual interneurons of the blowfly with naturalistic visual stimuli reconstructed from eye movements measured during free flight. Therefore we now investigate with naturalistic optic flow the coding properties of a small neuronal population of identified visual interneurons in the blowfly, the so-called VS and… Show more
“…Previous work suggests that optic flow from natural flight Boeddeker et al, 2005;van Hateren et al, 2005;Karmeier et al, 2006) and higher-order motion stimulus (Quenzer and Zanker, 1991;Lee and Nordström, 2012) contain motion components that can evoke neural response. A strong enough motion along a nonoptimal axis can also induce neural excitation (Karmeier et al, 2003).…”
Motion estimation is crucial for aerial animals such as the fly, which perform fast and complex maneuvers while flying through a 3-D environment. Motion-sensitive neurons in the lobula plate, a part of the visual brain, of the fly have been studied extensively for their specialized role in motion encoding. However, the visual stimuli used in such studies are typically highly simplified, often move in restricted ways, and do not represent the complexities of optic flow generated during actual flight. Here, we use combined rotations about different axes to study how H1, a wide-field motion-sensitive neuron, encodes preferred yaw motion in the presence of stimuli not aligned with its preferred direction. Our approach is an extension of "white noise" methods, providing a framework that is readily adaptable to quantitative studies into the coding of mixed dynamic stimuli in other systems. We find that the presence of a roll or pitch ("distractor") stimulus reduces information transmitted by H1 about yaw, with the amount of this reduction depending on the variance of the distractor. Spike generation is influenced by features of both yaw and the distractor, where the degree of influence is determined by their relative strengths. Certain distractor features may induce bidirectional responses, which are indicative of an imbalance between global excitation and inhibition resulting from complex optic flow. Further, the response is shaped by the dynamics of the combined stimulus. Our results provide intuition for plausible strategies involved in efficient coding of preferred motion from complex stimuli having multiple motion components.
“…Previous work suggests that optic flow from natural flight Boeddeker et al, 2005;van Hateren et al, 2005;Karmeier et al, 2006) and higher-order motion stimulus (Quenzer and Zanker, 1991;Lee and Nordström, 2012) contain motion components that can evoke neural response. A strong enough motion along a nonoptimal axis can also induce neural excitation (Karmeier et al, 2003).…”
Motion estimation is crucial for aerial animals such as the fly, which perform fast and complex maneuvers while flying through a 3-D environment. Motion-sensitive neurons in the lobula plate, a part of the visual brain, of the fly have been studied extensively for their specialized role in motion encoding. However, the visual stimuli used in such studies are typically highly simplified, often move in restricted ways, and do not represent the complexities of optic flow generated during actual flight. Here, we use combined rotations about different axes to study how H1, a wide-field motion-sensitive neuron, encodes preferred yaw motion in the presence of stimuli not aligned with its preferred direction. Our approach is an extension of "white noise" methods, providing a framework that is readily adaptable to quantitative studies into the coding of mixed dynamic stimuli in other systems. We find that the presence of a roll or pitch ("distractor") stimulus reduces information transmitted by H1 about yaw, with the amount of this reduction depending on the variance of the distractor. Spike generation is influenced by features of both yaw and the distractor, where the degree of influence is determined by their relative strengths. Certain distractor features may induce bidirectional responses, which are indicative of an imbalance between global excitation and inhibition resulting from complex optic flow. Further, the response is shaped by the dynamics of the combined stimulus. Our results provide intuition for plausible strategies involved in efficient coding of preferred motion from complex stimuli having multiple motion components.
“…In previous studies we could show that a population of output neurons in the blowfly visual motion pathway extracts information about all self-motion components from the complex optic flow patterns generated on the eyes while the blowfly is flying around in its environment (Boeddeker et al 2005;Karmeier et al 2006;Kern et al 2005;van Hateren et al 2005). In the latter accounts the blowfly's brain is concluded to use a saccadic gaze strategy to obtain information between saccades about the spatial layout of the environment.…”
Section: Discussionmentioning
confidence: 99%
“…Being an output neuron of the system and sensitive to horizontal movement (Hausen 1984), HSE is probably one of the most important neurons involved in controlling horizontal turns. Nonetheless, several additional neurons are likely to be involved in visual flight control, especially if all degrees of freedom of locomotion are taken into account (Karmeier et al 2006). (3) The forward velocity generated by the cyberfly was held constant, although recent observations suggest that blowflies modify their forward velocity depending on the environmental properties (Kern et al, in preparation).…”
Section: Discussionmentioning
confidence: 99%
“…In particular, we analysed the significance of sideward drift after saccadic turns that is one of the most distinguishing features of blowfly flight behaviour. The sideward components of head and body movements were concluded to be encoded in the responses of HSE neuron (Karmeier et al 2006;Kern et al 2005Kern et al , 2006. Sideward movements could be useful in the context of obstacle avoidance, if the blowfly is oriented perpendicular to an obstacle.…”
Section: Discussionmentioning
confidence: 99%
“…On the one hand, large parts of the blowfly visual system are involved in optic flow processing and, on the other hand, experimental analysis can be performed on blowflies by a broad spectrum of methods. Only recently it became possible, thanks to novel technologies, to investigate optic flow processing using visual stimuli which come very close to what flies have seen during their acrobatic flight manoeuvres (Boeddeker et al 2005;Karmeier et al 2006;Kern et al 2005;Lindemann et al 2003;van Hateren et al 2005).…”
Behavioural and electrophysiological experiments suggest that blowflies employ an active saccadic strategy of flight and gaze control to separate the rotational from the translational optic flow components. As a consequence, this allows motion sensitive neurons to encode during translatory intersaccadic phases of locomotion information about the spatial layout of the environment. So far, it has not been clear whether and how a motor controller could decode the responses of these neurons to prevent a blowfly from colliding with obstacles. Here we propose a simple model of the blowfly visual course control system, named cyberfly, and investigate its performance and limitations. The sensory input module of the cyberfly emulates a pair of output neurons subserving the two eyes of the blowfly visual motion pathway. We analyse two sensory-motor interfaces (SMI). An SMI coupling the differential signal of the sensory neurons proportionally to the yaw rotation fails to avoid obstacles. A more plausible SMI is based on a saccadic controller. Even with sideward drift after saccades as is characteristic of real blowflies, the cyberfly is able to successfully avoid collisions with obstacles. The relative distance information contained in the optic flow during translatory movements between saccades is provided to the SMI by the responses of the visual output neurons. An obvious limitation of this simple mechanism is its strong dependence on the textural properties of the environment.
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