Biomedical subjects

Martin Egelhaaf

Publications and source records attributed to Martin Egelhaaf.

14 recordsLinked to original sources

PubMed2007

Synapses in the fly motion-vision pathway: evidence for a broad range of signal amplitudes and dynamics.

Synapses are generally considered to operate efficiently only when their signaling range matches the spectrum of prevailing presynaptic signals in terms of both amplitudes and dynamics. However, the prerequisites for optimally matching the signaling ranges may differ between spike-mediated and graded synaptic transmission. This poses a problem for synapses that convey both graded and spike signals at the same time. We addressed this issue by tracing transmission systematically in vivo in the blowfly's visual-motion pathway by recording from single neurons that receive mixed potential signals consisting of rather slow graded fluctuations superimposed with highly variable spikes from a small number of presynaptic elements. Both pre- and postsynaptic neurons were previously shown to represent preferred-direction motion velocity reliably and linearly at low fluctuation frequencies. To selectively assess the performance of individual synapses and to precisely control presynaptic signals, we voltage clamped one of the presynaptic neurons. Results showed that synapses can effectively convey signals over a much larger amplitude and frequency range than is normally used during graded transmission of visual signals. An explanation for this unexpected finding might lie in the transmission of the spike component that reaches larger amplitudes and contains higher frequencies than graded signals.

Animals↗

PubMed2006

Robust integration of motion information in the fly visual system revealed by single cell photoablation.

In the brain, sensory information needs often to be read out from the ensemble activity of presynaptic neurons. In the most basic case, this may be accomplished by an individual postsynaptic neuron. In the visual system of the blowfly, an identified motion-sensitive spiking neuron is known to be postsynaptic to an ensemble of graded-potential presynaptic input elements. Both the presynaptic and postsynaptic neurons were shown previously to be capable of representing the velocity of preferred-direction motion reliably and linearly over a large frequency range of velocity fluctuations. Accordingly, the synaptic transfer properties of the connecting excitatory synapses between individual input elements and the postsynaptic neuron were shown to be linear over a similar range of presynaptic membrane potential fluctuations. It was not known, however, how the postsynaptic neuron integrates and reads out the presynaptic ensemble activity. We were able to compare the response properties of the integrating cell before and after eliminating individual presynaptic elements by a laser ablation technique. For most of the input elements, we found that their elimination strongly affected the activity of the postsynaptic neuron but did not degrade its performance to encode motion with constant and time-varying velocity. Our results suggest that the integration of individual synaptic inputs within the neural circuit operates with some redundancy. This feature might help the postsynaptic neuron to encode in a highly robust way the direction and the velocity of self-motion of the animal.

Action Potentials↗

PubMed2006

Implications of functionally different synaptic inputs for neuronal gain and computational properties of fly visual interneurons.

Neurons embedded in networks are thought to receive synaptic inputs that do not drive them on their own, but modulate the responsiveness to driving input. Although studies on brain slices have led to detailed knowledge of how nondriving input affects dendritic integration, its origin and functional implications remain unclear. We tackle this issue using an ensemble of fly wide-field visual interneurons. These neurons offer the opportunity not only to combine in vivo recording techniques and natural sensory stimulation but also to interpret electrophysiological results in a behavioral context. By targeted manipulation of the animal's visual input we find a pronounced modulating impact of nondriving input, whereas functionally important cellular properties like direction tuning and the coding of pattern velocity are left almost unaffected. We propose that the integration of functionally different synaptic inputs is a mechanism that immanently equalizes the ensemble's sensitivity irrespective of the specific stimulus conditions.

Animals↗

PubMed2005

Population coding of self-motion: applying bayesian analysis to a population of visual interneurons in the fly.

Coding of sensory information often involves the activity of neuronal populations. We demonstrate how the accuracy of a population code depends on integration time, the size of the population, and noise correlation between the participating neurons. The population we study consists of 10 identified visual interneurons in the blowfly Calliphora vicina involved in optic flow processing. These neurons are assumed to encode the animal's head or body rotations around horizontal axes by means of graded potential changes. From electrophysiological experiments we obtain parameters for modeling the neurons' responses. From applying a Bayesian analysis on the modeled population response we draw three major conclusions. First, integration of neuronal activities over a time period of only 5 ms after response onset is sufficient to decode accurately the rotation axis. Second, noise correlation between neurons has only little impact on the population's performance. And third, although a population of only two neurons would be sufficient to encode any horizontal rotation axis, the population of 10 vertical system neurons is advantageous if the available integration time is short. For the fly, short integration times to decode neuronal responses are important when controlling rapid flight maneuvers.

Animals↗

PubMed2005

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.

Animals↗

PubMed2005

A single control system for smooth and saccade-like pursuit in blowflies.

During courtship, male blowflies perform aerobatic pursuits that rank among the fastest visual behaviours that can be observed in nature. The viewing strategies during pursuit behaviour of blowflies appear to be very similar to eye movements during pursuit in primates: a combination of smooth pursuit and catch-up saccades. Whereas in primates these two components of pursuit eye movements are thought to be controlled by distinct oculomotor subsystems, we present evidence that in blowflies both types of pursuit responses can be produced by a single control system. In numerical simulations of chasing behaviour the proposed control system generates qualitatively the same behaviour as with real blowflies. As a consequence of time constants in the control system, mimicking neuronal processing times, muscular dynamics and inertia, saccade-like changes in gaze direction are generated if the target is displaced rapidly on the pursuing fly's retina. In the behavioural context of visual pursuit, saccade-like changes of the fly's gaze direction can thus be parsimoniously explained as an emergent property of a smooth pursuit system without assuming a priori different mechanisms underlying smooth and saccadic tracking behaviour.

Animals↗

PubMed2004

In vivo two-photon laser-scanning microscopy of Ca2+ dynamics in visual motion-sensitive neurons.

We applied two-photon laser-scanning microscopy (TPLSM) to motion-sensitive visual interneurons of the fly to study Ca(2+) dynamics in vivo at a higher spatial and temporal resolution than possible with conventional fluorescence microscopy. Based on a custom-built two-photon microscope, we performed line scans to measure changes in presynaptic Ca(2+) concentrations elicited by visual stimulation. We used a fast avalanche photodiode (APD) with a high quantum efficiency to detect even low levels of emitted fluorescence. Our experiments show that our in vivo preparation is amenable to TPLSM: with excitation intensities low enough not to cause photodamage, activity-dependent fluorescence changes of Ca(2+)-sensitive dyes can be detected in small neuronal branches. The performance of two-photon and conventional Ca(2+) imaging carried out consecutively at the same neuron is compared and it is demonstrated that two-photon imaging allows us to detect differences in Ca(2+) dynamics between individual neurites.

Animals↗

PubMed2003

Impact of photon noise on the reliability of a motion-sensitive neuron in the fly's visual system.

Variable behavioral responses to identical visual stimuli can, in part, be traced back to variable neuronal signals that provide unreliable information about the outside world. This unreliability in encoding of visual information is caused by several noise sources such as photon noise, synaptic noise, or the stochastic nature of ion channels. Neurons of the fly's visual motion pathway have been claimed to represent perfect encoders, with photon noise as the main noise source limiting their performance. Other studies on the fly's visual system suggest, however, that internal noise emerging within the nervous system also affects the reliability of motion vision. To resolve these contradictory interpretations, we performed an electrophysiological investigation, inspired by the "equivalent noise" paradigm applied in psychophysics, on the fly's motion-sensitive H1 neuron. Noise-like brightness fluctuations of different strength were superimposed on the motion stimuli. Because the noise level found to affect the temporal properties of the spike responses is much larger than the estimate of photon noise under the experimental conditions, our results indicate that motion vision is more likely to be limited by internal sources of variability than by photon noise.

Action Potentials↗

PubMed2003

Steering a virtual blowfly: simulation of visual pursuit.

The behavioural repertoire of male flies includes visually guided chasing after moving targets. The visuomotor control system for these pursuits belongs to the fastest found in the animal kingdom. We simulated a virtual fly, to test whether or not experimentally established hypotheses on the underlying control system are sufficient to explain chasing behaviour. Two operating instructions for steering the chasing virtual fly were derived from behavioural experiments: (i) the retinal size of the target controls the fly's forward speed and, thus, indirectly its distance to the target; and (ii) a smooth pursuit system uses the retinal position of the target to regulate the fly's flight direction. Low-pass filters implement neuronal processing time. Treating the virtual fly as a point mass, its kinematics are modelled in consideration of the effects of translatory inertia and air friction. Despite its simplicity, the model shows behaviour similar to that of real flies. Depending on its starting position and orientation as well as on target size and speed, the virtual fly either catches the target or follows it indefinitely without capture. These two behavioural modes of the virtual fly emerge from the control system for flight steering without implementation of an explicit decision maker.

Animals↗

PubMed2003

Robustness of the tuning of fly visual interneurons to rotatory optic flow.

The sophisticated receptive field organization of motion-sensitive tangential cells in the visual system of the blowfly Calliphora vicina matches the structure of particular optic flow fields. Hypotheses on the tuning of particular tangential cells to rotatory self-motion are based on local motion measurements. So far, tangential cells have never been tested with global optic flow stimuli. Therefore we measured the responses of an identifiable neuron, the V1 tangential cell, to wide-field motion stimuli mimicking optic flow fields similar to those the fly encounters during particular self-motions. The stimuli were generated by a "planetarium-projector," casting a pattern of moving light dots on a large spherical projection screen. We determined the tuning curves of the V1-cell to optic flow fields as induced by the animal during 1) rotation about horizontally aligned body axes, 2) upward/downward translation, and 3) a combination of both components. We found that the V1-cell does not respond as specifically to self-rotations, as had been concluded from its receptive field organization. The neuron responds strongly to upward translation and its tuning to rotations is much coarser than expected. The discrepancies between the responses to global optic flow and the predictions based on the receptive field organization are likely due to nonlinear integration properties of tangential neurons. Response parameters like orientation, shape, and width of the tuning curve are largely unaffected by changes in rotation velocity or a superposition of rotational and translational optic flow.

Animals↗

PubMed2003

Chasing a dummy target: smooth pursuit and velocity control in male blowflies.

Male blowflies chase and catch other flies in fast acrobatic flights. To unravel the underlying control system, we presented a black moving sphere instead of a real fly as a pursuit target. By varying the size and speed of the target, we were able to systematically analyse the decisive visual determinants that guide chasing behaviour. Flies pursue targets of a wide range of sizes and velocities. The percentage of pursuits resulting in target capture decreases with increasing target size and speed. Chasing male flies adjust their forward velocity depending on the retinal size of the target, indicating that retinal size is a relevant input variable of the control system. The chasing fly focuses the target with great accuracy in the frontal part of its visual field by means of a smooth pursuit control system using the retinal position of the target to determine the flight direction. We conclude that for a comprehensive understanding of chasing control different time lags in the control systems of angular and forward velocity together with the impact of inertia on fly movements need to be taken into account.

Animals↗

PubMed2003

Natural patterns of neural activity: how physiological mechanisms are orchestrated to cope with real life.

Physiological mechanisms of neuronal information processing have been shaped during evolution by a continual interplay between organisms and their sensory surroundings. Thus, when asking for the functional significance of such mechanisms, the natural conditions under which they operate must be considered. This has been done successfully in several studies that employ sensory stimulation under in vivo conditions. These studies address the question of how physiological mechanisms within neurons are properly adjusted to the characteristics of natural stimuli and to the demands imposed on the system being studied. Results from diverse animal models show how neurons exploit natural stimulus statistics efficiently by utilizing specific filtering capacities. Mechanisms that allow neurons to adapt to the currently relevant range from an often immense stimulus spectrum are outlined, and examples are provided that suggest that information transfer between neurons is shaped by the system-specific computational tasks in the behavioral context.

Action Potentials↗

PubMed2002

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.

Adaptation, Physiological↗

PubMed2002

Vision in flying insects.

Vision guides flight behaviour in numerous insects. Despite their small brain, insects easily outperform current man-made autonomous vehicles in many respects. Examples are the virtuosic chasing manoeuvres male flies perform as part of their mating behaviour and the ability of bees to assess, on the basis of visual motion cues, the distance travelled in a novel environment. Analyses at both the behavioural and neuronal levels are beginning to unveil reasons for such extraordinary capabilities of insects. One recipe for their success is the adaptation of visual information processing to the specific requirements of the behavioural tasks and to the specific spatiotemporal properties of the natural input.

Animals↗