Visual coding in locust photoreceptors

Theory of morphodynamic information processing: Linking sensing to behaviour

The traditional understanding of brain function has predominantly focused on chemical and electrical processes. However, new research in fruit fly (Drosophila) binocular vision reveals ultrafast photomechanical photoreceptor movements significantly enhance information processing, thereby impacting a fly's perception of its environment and behaviour. The coding advantages resulting from these mechanical processes suggest that similar physical motion-based coding strategies may affect neural communication ubiquitously. The theory of neural morphodynamics proposes that rapid biomechanical movements and microstructural changes at the level of neurons and synapses enhance the speed and efficiency of sensory information processing, intrinsic thoughts, and actions by regulating neural information in a phasic manner. We propose that morphodynamic information processing evolved to drive predictive coding, synchronising cognitive processes across neural networks to match the behavioural demands at hand effectively.

The geometry of decision-making in individuals and collectives

Almost all animals must make decisions on the move. Here, employing an approach that integrates theory and high-throughput experiments (using state-of-the-art virtual reality), we reveal that there exist fundamental geometrical principles that result from the inherent interplay between movement and organisms’ internal representation of space. Specifically, we find that animals spontaneously reduce the world into a series of sequential binary decisions, a response that facilitates effective decision-making and is robust both to the number of options available and to context, such as whether options are static (e.g., refuges) or mobile (e.g., other animals). We present evidence that these same principles, hitherto overlooked, apply across scales of biological organization, from individual to collective decision-making.

Choosing among spatially distributed options is a central challenge for animals, from deciding among alternative potential food sources or refuges to choosing with whom to associate. Using an integrated theoretical and experimental approach (employing immersive virtual reality), we consider the interplay between movement and vectorial integration during decision-making regarding two, or more, options in space. In computational models of this process, we reveal the occurrence of spontaneous and abrupt “critical” transitions (associated with specific geometrical relationships) whereby organisms spontaneously switch from averaging vectorial information among, to suddenly excluding one among, the remaining options. This bifurcation process repeats until only one option—the one ultimately selected—remains. Thus, we predict that the brain repeatedly breaks multichoice decisions into a series of binary decisions in space–time. Experiments with fruit flies, desert locusts, and larval zebrafish reveal that they exhibit these same bifurcations, demonstrating that across taxa and ecological contexts, there exist fundamental geometric principles that are essential to explain how, and why, animals move the way they do.

A biomimetic fly photoreceptor model elucidates how stochastic adaptive quantal sampling provides a large dynamic range

Light intensities (photons s^-1  μm^-2 ) in a natural scene vary over several orders of magnitude from shady woods to direct sunlight. A major challenge facing the visual system is how to map such a large dynamic input range into its limited output range, so that a signal is neither buried in noise in darkness nor saturated in brightness. A fly photoreceptor has achieved such a large dynamic range; it can encode intensity changes from single to billions of photons, outperforming man-made light sensors. This performance requires powerful light adaptation, the neural implementation of which has only become clear recently. A computational fly photoreceptor model, which mimics the real phototransduction processes, has elucidated how light adaptation happens dynamically through stochastic adaptive quantal information sampling. A Drosophila R1-R6 photoreceptor's light sensor, the rhabdomere, has 30,000 microvilli, each of which stochastically samples incoming photons. Each microvillus employs a full G-protein-coupled receptor signalling pathway to adaptively transduce photons into quantum bumps (QBs, or samples). QBs then sum the macroscopic photoreceptor responses, governed by four quantal sampling factors (limitations): (i) the number of photon sampling units in the cell structure (microvilli), (ii) sample size (QB waveform), (iii) latency distribution (time delay between photon arrival and emergence of a QB), and (iv) refractory period distribution (time for a microvillus to recover after a QB). Here, we review how these factors jointly orchestrate light adaptation over a large dynamic range.

How a fly photoreceptor samples light information in time

A photoreceptor's information capture is constrained by the structure and function of its light‐sensitive parts. Specifically, in a fly photoreceptor, this limit is set by the number of its photon sampling units (microvilli), constituting its light sensor (the rhabdomere), and the speed and recoverability of their phototransduction reactions. In this review, using an insightful constructionist viewpoint of a fly photoreceptor being an ‘imperfect’ photon counting machine, we explain how these constraints give rise to adaptive quantal information sampling in time, which maximises information in responses to salient light changes while antialiasing visual signals. Interestingly, such sampling innately determines also why photoreceptors extract more information, and more economically, from naturalistic light contrast changes than Gaussian white‐noise stimuli, and we explicate why this is so. Our main message is that stochasticity in quantal information sampling is less noise and more processing, representing an ‘evolutionary adaptation’ to generate a reliable neural estimate of the variable world.

Electrophysiological Method for Recording Intracellular Voltage Responses of Drosophila Photoreceptors and Interneurons to Light Stimuli In Vivo

Voltage responses of insect photoreceptors and visual interneurons can be accurately recorded with conventional sharp microelectrodes. The method described here enables the investigator to measure long-lasting (from minutes to hours) high-quality intracellular responses from single Drosophila R1-R6 photoreceptors and Large Monopolar Cells (LMCs) to light stimuli. Because the recording system has low noise, it can be used to study variability among individual cells in the fly eye, and how their outputs reflect the physical properties of the visual environment. We outline all key steps in performing this technique. The basic steps in constructing an appropriate electrophysiology set-up for recording, such as design and selection of the experimental equipment are described. We also explain how to prepare for recording by making appropriate (sharp) recording and (blunt) reference electrodes. Details are given on how to fix an intact fly in a bespoke fly-holder, prepare a small window in its eye and insert a recording electrode through this hole with minimal damage. We explain how to localize the center of a cell's receptive field, dark- or light-adapt the studied cell, and to record its voltage responses to dynamic light stimuli. Finally, we describe the criteria for stable normal recordings, show characteristic high-quality voltage responses of individual cells to different light stimuli, and briefly define how to quantify their signaling performance. Many aspects of the method are technically challenging and require practice and patience to master. But once learned and optimized for the investigator's experimental objectives, it grants outstanding in vivo neurophysiological data.

Visual ecology and potassium conductances of insect photoreceptors

Voltage-activated potassium channels (Kv channels) in the microvillar photoreceptors of arthropods are responsible for repolarization and regulation of photoreceptor signaling bandwidth. On the basis of analyzing Kv channels in dipteran flies, it was suggested that diurnal, rapidly flying insects predominantly express sustained K(+) conductances, whereas crepuscular and nocturnally active animals exhibit strongly inactivating Kv conductances. The latter was suggested to function for minimizing cellular energy consumption. In this study we further explore the evolutionary adaptations of the photoreceptor channelome to visual ecology and behavior by comparing K(+) conductances in 15 phylogenetically diverse insects, using patch-clamp recordings from dissociated ommatidia. We show that rapid diurnal flyers such as the blowfly (Calliphora vicina) and the honeybee (Apis mellifera) express relatively large noninactivating Kv conductances, conforming to the earlier hypothesis in Diptera. Nocturnal and/or slow-moving species do not in general exhibit stronger Kv conductance inactivation in the physiological membrane voltage range, but the photoreceptors in species that are known to rely more on vision behaviorally had higher densities of sustained Kv conductances than photoreceptors of less visually guided species. No statistically significant trends related to visual performance could be identified for the rapidly inactivating Kv conductances. Counterintuitively, strong negative correlations were observed between photoreceptor capacitance and specific membrane conductance for both sustained and inactivating fractions of Kv conductance, suggesting insignificant evolutionary pressure to offset negative effects of high capacitance on membrane filtering with increased conductance.

Biophysical properties of photoreceptors in Corixa punctata facilitate diurnal life-style

Measurement of evolutionary adaptations of a visual system to its visual and operational ecology requires comparison of visual function in different species with similar morphologies and visual ecologies, occupying the same habitats but displaying differences in visually-guided behavior. The goal here was to document the biophysical properties of photoreceptors in the lesser water boatman Corixa punctata, which shares many features with the previously studied aquatic predator water boatman backswimmer Notonecta glauca. However, unlike the backswimmer, which heavily relies on vision to catch its prey, Corixa is a detritivore. Using the patch-clamp method, I found that the average whole-cell capacitance of Corixa photoreceptors was 441±206 pF, higher than in any other insect studied so far, and that absolute sensitivity was positively correlated with capacitance (Spearman rank correlation coefficient, 0.73). Interestingly, both the sensitivity distribution median and variation in Corixa were similar to the corresponding values in the diurnal water strider Gerris lacustris and were substantially smaller than in the noctidial N. glauca or the nocturnal/crepuscular cockroach Periplaneta americana. Furthermore, capacitance was correlated with the amplitudes of light-induced (0.70) and delayed rectifier K(+) (0.46) currents, membrane corner frequency (0.68) and maximal information rate (IRmax, 0.74). No correlation was observed between capacitance and transient K(+) current. Average IRmax in Corixa was 36.0±21.3 bits s(-1), much higher than in G. lacustris but smaller than in N. glauca. These findings support the hypothesis that Corixa's retinal function is adapted to its diurnal life-style, which is also consistent with field observations.

Refractory sampling links efficiency and costs of sensory encoding to stimulus statistics

Sensory neurons integrate information about the world, adapting their sampling to its changes. However, little is understood mechanistically how this primary encoding process, which ultimately limits perception, depends upon stimulus statistics. Here, we analyze this open question systematically by using intracellular recordings from fly (Drosophila melanogaster and Coenosia attenuata) photoreceptors and corresponding stochastic simulations from biophysically realistic photoreceptor models. Recordings show that photoreceptors can sample more information from naturalistic light intensity time series (NS) than from Gaussian white-noise (GWN), shuffled-NS or Gaussian-1/f stimuli; integrating larger responses with higher signal-to-noise ratio and encoding efficiency to large bursty contrast changes. Simulations reveal how a photoreceptor's information capture depends critically upon the stochastic refractoriness of its 30,000 sampling units (microvilli). In daylight, refractoriness sacrifices sensitivity to enhance intensity changes in neural image representations, with more and faster microvilli improving encoding. But for GWN and other stimuli, which lack longer dark contrasts of real-world intensity changes that reduce microvilli refractoriness, these performance gains are submaximal and energetically costly. These results provide mechanistic reasons why information sampling is more efficient for natural/naturalistic stimulation and novel insight into the operation, design, and evolution of signaling and code in sensory neurons.

Equilibrating errors: reliable estimation of information transmission rates in biological systems with spectral analysis-based methods

Shannon's seminal approach to estimating information capacity is widely used to quantify information processing by biological systems. However, the Shannon information theory, which is based on power spectrum estimation, necessarily contains two sources of error: time delay bias error and random error. These errors are particularly important for systems with relatively large time delay values and for responses of limited duration, as is often the case in experimental work. The window function type and size chosen, as well as the values of inherent delays cause changes in both the delay bias and random errors, with possibly strong effect on the estimates of system properties. Here, we investigated the properties of these errors using white-noise simulations and analysis of experimental photoreceptor responses to naturalistic and white-noise light contrasts. Photoreceptors were used from several insect species, each characterized by different visual performance, behavior, and ecology. We show that the effect of random error on the spectral estimates of photoreceptor performance (gain, coherence, signal-to-noise ratio, Shannon information rate) is opposite to that of the time delay bias error: the former overestimates information rate, while the latter underestimates it. We propose a new algorithm for reducing the impact of time delay bias error and random error, based on discovering, and then using that size of window, at which the absolute values of these errors are equal and opposite, thus cancelling each other, allowing minimally biased measurement of neural coding.

Collision-avoidance behaviors of minimally restrained flying locusts to looming stimuli

Visually guided collision avoidance is of paramount importance in flight, for instance to allow escape from potential predators. Yet, little is known about the types of collision-avoidance behaviors that may be generated by flying animals in response to an impending visual threat. We studied the behavior of minimally restrained locusts flying in a wind tunnel as they were subjected to looming stimuli presented to the side of the animal, simulating the approach of an object on a collision course. Using high-speed movie recordings, we observed a wide variety of collision-avoidance behaviors including climbs and dives away from - but also towards - the stimulus. In a more restrained setting, we were able to relate kinematic parameters of the flapping wings with yaw changes in the trajectory of the animal. Asymmetric wing flapping was most strongly correlated with changes in yaw, but we also observed a substantial effect of wing deformations. Additionally, the effect of wing deformations on yaw was relatively independent of that of wing asymmetries. Thus, flying locusts exhibit a rich range of collision-avoidance behaviors that depend on several distinct aerodynamic characteristics of wing flapping flight.

Postembryonic developmental changes in photoreceptors of the stick insect Carausius morosus enhance the shift to an adult nocturnal life-style

Optimization of sensory processing during development can be studied by using photoreceptors of hemimetabolous insects (with incomplete metamorphosis) as a research model. We have addressed this topic in the stick insect Carausius morosus, where retinal growth after hatching is accompanied by a diurnal-to-nocturnal shift in behavior, by recording from photoreceptors of first instar nymphs and adult animals using the patch-clamp method. In the nymphs, ommatidia were smaller and photoreceptors were on average 15-fold less sensitive to light than in adults. The magnitude of A-type K(+) current did not increase but the delayed rectifier doubled in adults compared with nymphs, the K(+) current densities being greater in the nymphs. By contrast, the density of light-induced current did not increase, although its magnitude increased 8.6-fold, probably due to the growth of microvilli. Nymph photoreceptors performed poorly, demonstrating a peak information rate (IR) of 2.9 ± 0.7 bits/s versus 34.1 ± 5.0 bits/s in adults in response to white-noise stimulation. Strong correlations were found between photoreceptor capacitance (a proxy for cell size) and IR, and between light sensitivity and IR, with larger and more sensitive photoreceptors performing better. In adults, IR peaked at light intensities matching irradiation from the evening sky. Our results indicate that biophysical properties of photoreceptors at each age stage and visual behavior are interdependent and that developmental improvement in photoreceptor performance may facilitate the switch from the diurnal to the safer nocturnal lifestyle. This also has implications for how photoreceptors achieve optimal performance.

Signal coding in cockroach photoreceptors is tuned to dim environments

In dim light, scarcity of photons typically leads to poor vision. Nonetheless, many animals show visually guided behavior with dim environments. We investigated the signaling properties of photoreceptors of the dark active cockroach ( Periplaneta americana) using intracellular and whole-cell patch-clamp recordings to determine whether they show selective functional adaptations to dark. Expectedly, dark-adapted photoreceptors generated large and slow responses to single photons. However, when light adapted, responses of both phototransduction and the nontransductive membrane to white noise (WN)-modulated stimuli remained slow with corner frequencies ∼20 Hz. This promotes temporal integration of light inputs and maintains high sensitivity of vision. Adaptive changes in dynamics were limited to dim conditions. Characteristically, both step and frequency responses stayed effectively unchanged for intensities >1,000 photons/s/photoreceptor. A signal-to-noise ratio (SNR) of the light responses was transiently higher at frequencies <5 Hz for ∼5 s after light onset but deteriorated to a lower value upon longer stimulation. Naturalistic light stimuli, as opposed to WN, evoked markedly larger responses with higher SNRs at low frequencies. This allowed realistic estimates of information transfer rates, which saturated at ∼100 bits/s at low-light intensities. We found, therefore, selective adaptations beneficial for vision in dim environments in cockroach photoreceptors: large amplitude of single-photon responses, constant high level of temporal integration of light inputs, saturation of response properties at low intensities, and only transiently efficient encoding of light contrasts. The results also suggest that the sources of the large functional variability among different photoreceptors reside mostly in phototransduction processes and not in the properties of the nontransductive membrane.

Stochastic, adaptive sampling of information by microvilli in fly photoreceptors

Background

In fly photoreceptors, light is focused onto a photosensitive waveguide, the rhabdomere, consisting of tens of thousands of microvilli. Each microvillus is capable of generating elementary responses, quantum bumps, in response to single photons using a stochastically operating phototransduction cascade. Whereas much is known about the cascade reactions, less is known about how the concerted action of the microvilli population encodes light changes into neural information and how the ultrastructure and biochemical machinery of photoreceptors of flies and other insects evolved in relation to the information sampling and processing they perform.

Results

We generated biophysically realistic fly photoreceptor models, which accurately simulate the encoding of visual information. By comparing stochastic simulations with single cell recordings from Drosophila photoreceptors, we show how adaptive sampling by 30,000 microvilli captures the temporal structure of natural contrast changes. Following each bump, individual microvilli are rendered briefly (∼100–200 ms) refractory, thereby reducing quantum efficiency with increasing intensity. The refractory period opposes saturation, dynamically and stochastically adjusting availability of microvilli (bump production rate: sample rate), whereas intracellular calcium and voltage adapt bump amplitude and waveform (sample size). These adapting sampling principles result in robust encoding of natural light changes, which both approximates perceptual contrast constancy and enhances novel events under different light conditions, and predict information processing across a range of species with different visual ecologies.

Conclusions

These results clarify why fly photoreceptors are structured the way they are and function as they do, linking sensory information to sensory evolution and revealing benefits of stochasticity for neural information processing.

Impact of neural noise on a sensory-motor pathway signaling impending collision

Noise is a major concern in circuits processing electrical signals, including neural circuits. There are many factors that influence how noise propagates through neural circuits, and there are few systems in which noise levels have been studied throughout a processing pathway. We recorded intracellularly from multiple stages of a sensory-motor pathway in the locust that detects approaching objects. We found that responses are more variable and that signal-to-noise ratios (SNRs) are lower further from the sensory periphery. SNRs remain low even with the use of stimuli for which the pathway is most selective and for which the neuron representing its final sensory level must integrate many synaptic inputs. Modeling of this neuron shows that variability in the strength of individual synaptic inputs within a large population has little effect on the variability of the spiking output. In contrast, jitter in the timing of individual inputs and spontaneous variability is important for shaping the responses to preferred stimuli. These results suggest that neural noise is inherent to the processing of visual stimuli signaling impending collision and contributes to shaping neural responses along this sensory-motor pathway.

Network adaptation improves temporal representation of naturalistic stimuli in Drosophila eye: I dynamics

Because of the limited processing capacity of eyes, retinal networks must adapt constantly to best present the ever changing visual world to the brain. However, we still know little about how adaptation in retinal networks shapes neural encoding of changing information. To study this question, we recorded voltage responses from photoreceptors (R1–R6) and their output neurons (LMCs) in the Drosophila eye to repeated patterns of contrast values, collected from natural scenes. By analyzing the continuous photoreceptor-to-LMC transformations of these graded-potential neurons, we show that the efficiency of coding is dynamically improved by adaptation. In particular, adaptation enhances both the frequency and amplitude distribution of LMC output by improving sensitivity to under-represented signals within seconds. Moreover, the signal-to-noise ratio of LMC output increases in the same time scale. We suggest that these coding properties can be used to study network adaptation using the genetic tools in Drosophila, as shown in a companion paper (Part II).