The computational basis of an identified neuronal circuit for elementary motion detection in dipterous insects

Abstract

Based on comparative anatomical studies and electrophysiological experiments, we have identified a conserved subset of neurons in the lamina, medulla, and lobula of dipterous insects that are involved in retinotopic visual motion direction selectivity. Working from the photoreceptors inward, this neuronal subset includes lamina amacrine (alpha) cells, lamina monopolar (L2) cells, the basket T-cell (T1 or beta), the transmedullary cell Tm1, and the T5 bushy T-cell. Two GABA-immunoreactive neurons, the transmedullary cell Tm9 and a local interneuron at the level of T5 dendrites, are also implicated in the motion computation. We suggest that these neurons comprise the small-field elementary motion detector circuits the outputs of which are integrated by wide-field lobula plate tangential cells. We show that a computational model based on the available data about these neurons is consistent with existing models of biological elementary motion detection, and present a comparable version of the Hassenstein-Reichardt (HR) correlation model. Further, by using the model to synthesize a generic tangential cell, we show that it can account for the responses of lobula plate tangential cells to a wide range of transient stimuli, including responses which cannot be predicted using the HR model. This computational model of elementary motion detection is the first which derives specifically from the functional organization of a subset of retinotopic neurons supplying the lobula plate. A key prediction of this model is that elementary motion detector circuits respond quite differently to small-field transient stimulation than do spatially integrated motion processing neurons as observed in the lobula plate. In addition, this model suggests that the retinotopic motion information provided to wide-field motion-sensitive cells in the lobula is derived from a less refined stage of processing than motion inputs to the lobula plate.

Velocity constancy and models for wide-field visual motion detection in insects

“…Because the tangential cells are large and amenable to intracellular electrophysiolo gical recording, they have been the source of much indirect physiological evidence regarding the EMD. Although some recent work has attempted to identify and model the neuronal basis of the EMD itself (Higgins et al, 2004), this has proved hard to pinpoint due to the technical difficulty of recording from the very small neurons that are believed to be involved.…”

Elementary Motion Detection

“…However, there are several cell types in the fly visual system which might be suitable to compute one of the different steps that are necessary for a motion detection mechanism based on the Reichardt detector (Higgins et al, 2004). I will use the following section to introduce the visual system of the fly, with special focus on motion computation.…”

“…However, it has been proposed that they take part in high-pass filtering the visual input before it reaches further motion processing stages (Higgins et al, 2004;Jarvileh and Zettler, 1973). Genetic tools have given some interesting insights into information processing in the lamina, including direct evidence of the role of lamina cells in motion vision.…”

“…Using the model, I calculated the best fit for the low-pass filter in order to explain the results. Low-pass filtering takes place in the presynaptic circuity of LPTCs, presumable in the medulla (Higgins et al, 2004). In addition, anatomical projections of octopaminergic neurons into the medulla and into the lobula complex are known (Sinakevitch and Strausfeld, 2006;Busch et al, 2009).…”

Humanitarian Demining Using an Insect Based Chemical Unmanned Aerial Vehicle

“…Visual processing is mainly done by feed-forward neural structures dedicated to extract wide filed directional optic flow that is believed to be used for flight control (Hausen 1982a;Hausen 1982b;Egelhaaf & Borst 1993a;Krapp et al 1998;Franz & Krapp 2000;Douglass & Strausfeld 2003;Higgins et al 2004). In later processing stages, this information is integrated and used for landing, course and altitude control and collision avoidance (Rowel 1971;David 1982;Rind & Simmons 1992;Egelhaaf & Borst 1993b;Srinivasan et al 1996;Srinivasan et al 2000;Tammero & Dickinson 2002;Higgins et al 2004). …”