Martin Egelhaaf

Principles of visual motion detection

Motion information is required for the solution of many complex tasks of the visual system such as depth perception by motion parallax and figure/ground discrimination by relative motion. However, motion information is not explicitly encoded at the level of the retinal input. Instead, it has to be computed from the time-dependent brightness patterns of the retinal image as sensed by the two-dimensional array of photoreceptors. Different models have been proposed which describe the neural computations underlying motion detection in various ways. To what extent do biological motion detectors approximate any of these models? As will be argued here, there is increasing evidence from the different disciplines studying biological motion vision, that, throughout the animal kingdom ranging from invertebrates to vertebrates including man, the mechanisms underlying motion detection can be attributed to only a few, essentially equivalent computational principles. Motion detection may, therefore, be one of the first examples in computational neurosciences where common principles can be found not only at the cellular level (e.g., dendritic integration, spike propagation, synaptic transmission) but also at the level of computations performed by small neural networks.

Neural Mechanisms of Visual Course Control in Insects

Visual orientation and course-stabilization of flying insects rely essentially on the evaluation of the retinal motion patterns perceived by the animals during flight. Apparent motions of the entire surrounding indicate the direction and speed of self-motion in space and are used as visual feedback signals during optomotor course-control manoeuvres. Discontinuities in the motion pattern and relative motions between pattern-segments indicate the existence of stationary or moving objects and represent the basic visual cues for flight-orientation during fixation-and tracking-sequences, and possibly also for the avoidance of obstacles, and the selection of landing sites.

Computational structure of a biological motion-detection system as revealed by local detector analysis in the fly's nervous system

The computations performed by individual movement detectors are analyzed by intracellularly recording from an identified direction-selective motion-sensitive interneuron in the flys brain and by comparing these results with model predictions based on movement detectors of the correlation type. Three main conclusions were drawn with respect to the movement-detection system of the fly: (1) The essential nonlinear interaction between the two movement-detector input channels can be characterized formally by a mathematically almost perfect multiplication process. (2) Even at high contrasts no significant nonlinearities seem to distort the time course of the movement-detector input signals. (3) The movement detectors of the fly are not perfectly antisymmetrical; i.e., they respond with different time courses and amplitudes to motion in their preferred and null directions. As a consequence of this property, the motion detectors can respond to some degree to stationary patterns whose brightness is modulated in time. Moreover, the direction selectivity, i.e., the relative difference of the responses to motion in the preferred and null directions, depends on the contrast and on the spatial-frequency content of the stimulus pattern.

Transient and steady-state response properties of movement detectors

The transient and steady-state responses of movement detectors are studied at various pattern contrasts (i) by intracellularly recording from an identified movement-sensitive interneuron in the flys brain and (ii) by comparing these results with computer simulations of an array of movement detectors of the correlation type. At the onset of stimulus motion, the membrane potential oscillates with a frequency corresponding to the temporal frequency of the stimulus pattern before it settles at its steady-state level. Both the transient and the steady-state response amplitudes show a characteristic contrast dependence. As is shown by computer modeling, the transient behavior that we found in the experiments reflects an intrinsic property of the general scheme of movement detectors of the correlation type. To account for the contrast dependence, however, this general scheme has to be elaborated by (i) a subtraction stage, which eliminates the background light intensity from the detector input signal, and (ii) saturation characteristics in both branches of each movement-detector subunit.

Neural encoding of behaviourally relevant visual-motion information in the fly

Information processing in visual systems is constrained by the spatial and temporal characteristics of the sensory input and by the biophysical properties of the neuronal circuits. Hence, to understand how visual systems encode behaviourally relevant information, we need to know about both the computational capabilities of the nervous system and the natural conditions under which animals normally operate. By combining behavioural, neurophysiological and computational approaches, it is now possible in the fly to assess adaptations that process visual-motion information under the constraints of its natural input. It is concluded that neuronal operating ranges and coding strategies appear to be closely matched to the inputs the animal encounters under behaviourally relevant conditions.

On the neuronal basis of figure-ground discrimination by relative motion in the visual system of the fly

It has been concluded in the preceding papers (Egelhaaf, 1985a, b) that two functional classes of output elements of the visual ganglia might be involved in figure-ground discrimination by relative motion in the fly: The Horizontal Cells which respond best to the motion of large textured patterns and the FD-cells which are most sensitive to small moving objects. In this paper it is studied by computer simulations (1) in what way the input circuitry of the FD-cells might be organized and (2) the role the FD-cells play in figure-ground discrimination.The characteristic functional properties of the FD-cells can be explained by various alternative model networks. In all models the main input to the FD-cells is formed by two retinotopic arrays of small-field elementary movement detectors, responding to either front-to-back or back-to-front motion. According to their preferred direction of motion the FD-cells are excited by one of these movement detector classes and inhibited by the other. The synaptic transmission between the movement detectors and the FD-cells is assumed to be non-linear. It is a common property of all these model circuits that the inhibition of the FD-cells induced by large-field motion is mediated by pool cells which cover altogether the entire horizontal extent of the visual field of both eyes. These pool cells affect the response of the FD-cells either by pre- or postsynaptic shunting inhibition. Depending on the FD-cell under consideration, the pool cells are directionally selective for motion or sensitive to motion in either horizontal direction.The role the FD-cells and the Horizontal Cells are likely to play in figure-ground discrimination can be demonstrated by computer simulations of a composite neuronal model consisting of the model circuits for these cell types. According to their divergent spatial integration properties they perform different tasks in figure-ground discrimination: Whereas the Horizontal Cells mainly mediate information on wide-field motion, the FD-cells are selectively tuned to efficient detection of relatively small targets. Both cell classes together appear to be sufficient to account for figure-ground discrimination as it has been shown by analysis at the behavioural level.

Visual course control in flies relies on neuronal computation of object and background motion

The spatial distribution of light intensity received by the eyes changes continually when an animal moves around in its environment. These retinal activity patterns contain a wealth of information on the structure of the environment, the direction and speed of self-motion, and on the independent motion of objects1,2. If evaluated properly by the nervous system this information can be used in visual orientation. In a combination of both behavioural and electrophysiological analysis and modelling, this article establishes the neural mechanisms by which the visual system of the fly evaluates two types of basic retinal motion patterns: coherent retinal large-field motion as induced by self-motion of the animal, and relative motion between objects and their background. Separate neuronal networks are specifically tuned to each of these motion patterns and make use of them in two different visual orientation tasks.

Mechanisms of dendritic integration underlying gain control in fly motion-sensitive interneurons

In the compensatory optomotor response of the fly the interesting phenomenon of gain control has been observed by Reichardt and colleagues (Reichardt et al., 1983): The amplitude of the response tends to saturate with increasing stimulus size, but different saturation plateaus are assumed with different velocities at which the stimulus is moving. This characteristic can already be found in the motion-sensitive large field neurons of the fly optic lobes that play a role in mediating this behavioral response (Hausen, 1982; Reichardt et al, 1983; Egelhaaf, 1985; Haag et al., 1992). To account for gain control a model was proposed involving shunting inhibition of these cells by another cell, the so-called pool cell (Reichardt et al., 1983), both cells sharing common input from an array of local motion detectors. This article describes an alternative model which only requires dendritic integration of the output signals of two types of local motion detectors with opposite polarity. The explanation of gain control relies on recent findings that these input elements are not perfectly directionally selective and that their direction selectivity is a function of pattern velocity. As a consequence, the resulting postsynaptic potential in the dendrite of the integrating cell saturates with increasing pattern size at a level between the excitatory and inhibitory reversal potentials. The exact value of saturation is then set by the activation ratio of excitatory and inhibitory input elements which in turn is a function of other stimulus parameters such as pattern velocity. Thus, the apparently complex phenomenon of gain control can be simply explained by the biophysics of dendritic integration in conjunction with the properties of the motion-sensitive input elements.

Function of a fly motion-sensitive neuron matches eye movements during free flight.

Sensing is often implicitly assumed to be the passive acquisition of information. However, part of the sensory information is generated actively when animals move. For instance, humans shift their gaze actively in a sequence of saccades towards interesting locations in a scene. Likewise, many insects shift their gaze by saccadic turns of body and head, keeping their gaze fixed between saccades. Here we employ a novel panoramic virtual reality stimulator and show that motion computation in a blowfly visual interneuron is tuned to make efficient use of the characteristic dynamics of retinal image flow. The neuron is able to extract information about the spatial layout of the environment by utilizing intervals of stable vision resulting from the saccadic viewing strategy. The extraction is possible because the retinal image flow evoked by translation, containing information about object distances, is confined to low frequencies. This flow component can be derived from the total optic flow between saccades because the residual intersaccadic head rotations are small and encoded at higher frequencies. Information about the spatial layout of the environment can thus be extracted by the neuron in a computationally parsimonious way. These results on neuronal function based on naturalistic, behaviourally generated optic flow are in stark contrast to conclusions based on conventional visual stimuli that the neuron primarily represents a detector for yaw rotations of the animal.

Dynamic response properties of movement detectors: theoretical analysis and electrophysiological investigation in the visual system of the fly

Dynamic aspects of the computation of visual motion information are analysed both theoretically and experimentally. The theoretical analysis is based on the type of movement detector which has been proposed to be realized in the visual system of insects (e.g. Hassenstein and Reichardt 1956; Reichardt 1957, 1961; Buchner 1984), but also of man (e.g. van Doorn and Koenderink 1982a, b; van Santen and Sperling 1984; Wilson 1985). The output of both a single movement detector and a one-dimensional array of detectors is formulated mathematically as a function of time. The resulting movement detector theory can be applied to a much wider range of moving stimuli than has been possible on the basis of previous formulations of the detector output. These stimuli comprise one-dimensional “smooth” detector input functions, i.e. functions which can be expanded into a time-dependent convergent Taylor series for any value of the spatial coordinate.The movement detector response can be represented by a power series. Each term of this series consists of one exclusively time-dependent component and of another component that depends, in addition, on the properties of the pattern. Even the exclusively time-dependent components of the movement detector output are not solely determined by the stimulus velocity. They rather depend in a non-linear way on the weighted sum of the instantaneous velocity and all its higher order time derivatives. The latter point represents another reason — not discussed so far in the literature — that movement detectors of the type analysed here do not represent pure velocity sensors.The significance of this movement detector theory is established for the visual system of the fly. This is done by comparing the spatially integrated movement detector response with the functional properties of the directionally-selective motion-sensitive. Horizontal Cells of the third visual ganglion of the flys brain.These integrate local motion information over large parts of the visual field. The time course of the spatially integrated movement detector response is about proportional to the velocity of the stimulus pattern only as long as the pattern velocity and its time derivatives are sufficiently small. For large velocities and velocity changes of the stimulus pattern characteristic deviations of the response profiles from being proportional to pattern velocity are predicted on the basis of the detector theory developed here. These deviations are clearly reflected in the response of the wide-field Horizontal Cells, thus, providing very specific evidence that the movement detector theory developed here can be applied to motion detection in the fly. The characteristic dynamic features of the theoretically predicted and the experimentally determined cellular responses are exploited to estimate the time constant of the movement detector filter.