Biophysical modelling of a drosophila photoreceptor

It remains unclear how visual information is co-processed by different layers of neurons in the retina. In particular, relatively little is known how retina translates vast environmental light changes into neural responses of limited range. We began examining this question in a bottom-up way in a relatively simple °y eye. To gain understanding of how complex bio-molecular interactions govern the conversion of light input into voltage output (phototransduction), we are building a biophysical model of the Drosophila R1-R6 photoreceptor. Our model, which relates molecular dynamics of the underlying biochemical reactions to external light input, attempts to capture the molecular dynamics of phototransduction gain control in a quantitative way

Fractional Ca2+ Currents through TRP and TRPL Channels in Drosophila Photoreceptors

AbstractLight responses in Drosophila photoreceptors are mediated by two Ca2+ permeable cation channels, transient receptor potential (TRP) and TRP-like (TRPL). Although Ca2+ influx via these channels is critical for amplification, inactivation, and light adaptation, the fractional contribution of Ca2+ to the currents (Pf) has not been measured. We describe a slow (τ ∼ 350 ms) tail current in voltage-clamped light responses and show that it is mediated by electrogenic Na+/Ca2+ exchange. Assuming a 3Na:1Ca stoichiometry, we derive empirical estimates of Pf by comparing the charge integrals of the exchanger and light-induced currents. For TRPL channels, Pf was ∼17% as predicted by Goldman-Hodgkin-Katz (GHK) theory. Pf for TRP (29%) and wild-type flies (26%) was higher, but lower than the GHK prediction (45% and 42%). As predicted by GHK theory, Pf for both channels increased with extracellular [Ca2+], and was largely independent of voltage between –100 and –30 mV. A model incorporating intra- and extracellular geometry, ion permeation, diffusion, extrusion, and buffering suggested that the deviation from GHK predictions was largely accounted for by extracellular ionic depletion during the light-induced currents, and the time course of the Na+/Ca2+ exchange current could be used to obtain estimates of cellular Ca2+ buffering capacities

Sparse Identification of Contrast Gain Control in the Fruit Fly Photoreceptor and Amacrine Cell Layer

The fruit fly's natural visual environment is often characterized by light intensities ranging across several orders of magnitude and by rapidly varying contrast across space and time. Fruit fly photoreceptors robustly transduce and, in conjunction with amacrine cells, process visual scenes and provide the resulting signal to downstream targets. Here we model the first step of visual processing in the photoreceptor-amacrine cell layer. We propose a novel divisive normalization processor (DNP) for modeling the computation taking place in the photoreceptor-amacrine cell layer. The DNP explicitly models the photoreceptor feedforward and temporal feedback processing paths and the spatio-temporal feedback path of the amacrine cells. We then formally characterize the contrast gain control of the DNP and provide sparse identification algorithms that can efficiently identify each the feedforward and feedback DNP components. The algorithms presented here are the first demonstration of tractable and robust identification of the components of a divisive normalization processor. The sparse identification algorithms can be readily employed in experimental settings, and their effectiveness is demonstrated with several examples

Random Photon Absorption Model Elucidates How Early Gain Control in Fly Photoreceptors Arises from Quantal Sampling

Many diurnal photoreceptors encode vast real-world light changes effectively, but how this performance originates from photon sampling is unclear. A 4-module biophysically-realistic fly photoreceptor model, in which information capture is limited by the number of its sampling units (microvilli) and their photon-hit recovery time (refractoriness), can accurately simulate real recordings and their information content. However, sublinear summation in quantum bump production (quantum-gain-nonlinearity) may also cause adaptation by reducing the bump/photon gain when multiple photons hit the same microvillus simultaneously. Here, we use a Random Photon Absorption Model (RandPAM), which is the 1st module of the 4-module fly photoreceptor model, to quantify the contribution of quantum-gain-nonlinearity in light adaptation. We show how quantum-gain-nonlinearity already results from photon sampling alone. In the extreme case, when two or more simultaneous photon-hits reduce to a single sublinear value, quantum-gain-nonlinearity is preset before the phototransduction reactions adapt the quantum bump waveform. However, the contribution of quantum-gain-nonlinearity in light adaptation depends upon the likelihood of multi-photon-hits, which is strictly determined by the number of microvilli and light intensity. Specifically, its contribution to light-adaptation is marginal (≤1%) in fly photoreceptors with many thousands of microvilli, because the probability of simultaneous multi-photon-hits on any one microvillus is low even during daylight conditions. However, in cells with fewer sampling units, the impact of quantum-gain-nonlinearity increases with brightening light

Microsaccadic sampling of moving image information provides Drosophila hyperacute vision.

Small fly eyes should not see fine image details. Because flies exhibit saccadic visual behaviors and their compound eyes have relatively few ommatidia (sampling points), their photoreceptors would be expected to generate blurry and coarse retinal images of the world. Here we demonstrate that Drosophila see the world far better than predicted from the classic theories. By using electrophysiological, optical and behavioral assays, we found that R1-R6 photoreceptors' encoding capacity in time is maximized to fast high-contrast bursts, which resemble their light input during saccadic behaviors. Whilst over space, R1-R6s resolve moving objects at saccadic speeds beyond the predicted motion-blur-limit. Our results show how refractory phototransduction and rapid photomechanical photoreceptor contractions jointly sharpen retinal images of moving objects in space-time, enabling hyperacute vision, and explain how such microsaccadic information sampling exceeds the compound eyes' optical limits. These discoveries elucidate how acuity depends upon photoreceptor function and eye movements

Modeling elucidates how refractory period can provide profound nonlinear gain control to graded potential neurons

Refractory period (RP) plays a central role in neural signaling. Because it limits an excitable membrane's recovery time from a previous excitation, it can restrict information transmission. Classically, RP means the recovery time from an action potential (spike), and its impact to encoding has been mostly studied in spiking neurons. However, many sensory neurons do not communicate with spikes but convey information by graded potential changes. In these systems, RP can arise as an intrinsic property of their quantal micro/nanodomain sampling events, as recently revealed for quantum bumps (single photon responses) in microvillar photoreceptors. Whilst RP is directly unobservable and hard to measure, masked by the graded macroscopic response that integrates numerous quantal events, modeling can uncover its role in encoding. Here, we investigate computationally how RP can affect encoding of graded neural responses. Simulations in a simple stochastic process model for a fly photoreceptor elucidate how RP can profoundly contribute to nonlinear gain control to achieve a large dynamic range. [Abstract copyright: © 2017 The Authors. Physiological Reports published by Wiley Periodicals, Inc. on behalf of The Physiological Society and the American Physiological Society.

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

Light intensities (photons/s/um2) 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 to its limited output range, so that signal is neither buried into noise in darkness and nor saturated in brightness. A fly photoreceptor has achieved such a large dynamic range; it can encode intensity changes from single photons to billions more, outperforming man-made light sensors. This performance requires powerful light-adaptation, the neural implementation of which has only become clearer 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 (GPCR) signalling pathway to adaptively transduce photons into quantum bumps (QBs, or samples). QBs then sum up the macroscopic photoreceptor responses, governed by four quantal sampling factors (limitations): (1) the number of photon sampling units in the cell structure (microvilli); (2) sample size (QB waveform); (3) latency distribution (time delay between photon arrival to emergence of a QB), and (4) 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

Internal structure of the fly elementary motion detector

Flies use visual motion information for flight control, stabilization and object tracking. However, information about local motion such as direction and velocity is not explicitly represented at the level of the retina but must be computed by subsequent motion detection circuitry. The output of these circuits can be recorded in large, direction-selective lobula plate tangential cells, that integrate over hundreds of elementary motion detectors. The computational structure of these detectors is best described by the Reichardt model, where the signals from two neighboring photoreceptors become multiplied after one of them has been delayed. However, the neural correlate of the Reichardt Detector, i.e. the identity, physiology and connectivity of the constituting cells, has escaped further characterization due to technical difficulties in recording from these small neurons. In this thesis, I investigated the internal structure of the fly motion detection circuit by a combination of electrophysiology, computer simulations and mathematical modeling. First, I studied the effect of the mean luminance on motion detection. I found that the response strength of lobula plate tangential cells strongly depends on stimulus contrast but barely changes as a function of mean luminance. Adaptation to a new mean luminance follows an exponential decay with a time constant of several hundred milliseconds. I next investigated the structural consequences of splitting the visual input into ON and OFF components, as recently discovered in the fruit fly. The original Reichardt Detector can be refined by incorporating these findings, giving rise to two alternative structures. The 4-Quadrant-Detector consists of four independent subunits of the Reichardt type, correlating ON with ON, OFF with OFF, ON with OFF and OFF with ON signals. In contrast, the 2-Quadrant-Detector consists of two subunits only, that correlate ON with ON and OFF with OFF signals. In order to distinguish between these two models, I first stimulated flies with apparent motion stimuli consisting of a sequence of two brightness steps at neighboring locations, while recording the motion detector output in lobula plate tangential cells of the blow fly. I found strongly direction-selective responses to ON-ON and OFF-OFF sequences, but also to ON-OFF and OFF-ON sequences. At first sight, these results seem to support the 4-Quadrant-Detector. However, I showed with simulations and an analytical treatment that the 2-Quadrant-Detector, when equipped with an appropriate preprocessing stage, is capable of reproducing such responses as well. Based on predictions from model simulations, I designed a new stimulus protocol consisting of a sequence of short brightness pulses instead of steps. For such stimuli, the 2-Quadrant-Detector does not produce significant responses to ON-OFF and OFF-ON sequences, in contrast to the 4-Quadrant-Detector. The corresponding recordings cannot be reconciled with the 4-Quadrant-Detector but are in good agreement with the 2-Quadrant-Detector. I therefore conclude that the internal structure of the y elementary motion detector consists of two non-interacting subunits for detecting ON and OFF motion, respectively. These results mark an important step in the ongoing dissection of the insect motion detection circuit by providing an updated model that better matches the structure and physiology of the corresponding neural hardware

Internal structure of the fly elementary motion detector

Flies use visual motion information for flight control, stabilization and object tracking. However, information about local motion such as direction and velocity is not explicitly represented at the level of the retina but must be computed by subsequent motion detection circuitry. The output of these circuits can be recorded in large, direction-selective lobula plate tangential cells, that integrate over hundreds of elementary motion detectors. The computational structure of these detectors is best described by the Reichardt model, where the signals from two neighboring photoreceptors become multiplied after one of them has been delayed. However, the neural correlate of the Reichardt Detector, i.e. the identity, physiology and connectivity of the constituting cells, has escaped further characterization due to technical difficulties in recording from these small neurons. In this thesis, I investigated the internal structure of the fly motion detection circuit by a combination of electrophysiology, computer simulations and mathematical modeling. First, I studied the effect of the mean luminance on motion detection. I found that the response strength of lobula plate tangential cells strongly depends on stimulus contrast but barely changes as a function of mean luminance. Adaptation to a new mean luminance follows an exponential decay with a time constant of several hundred milliseconds. I next investigated the structural consequences of splitting the visual input into ON and OFF components, as recently discovered in the fruit fly. The original Reichardt Detector can be refined by incorporating these findings, giving rise to two alternative structures. The 4-Quadrant-Detector consists of four independent subunits of the Reichardt type, correlating ON with ON, OFF with OFF, ON with OFF and OFF with ON signals. In contrast, the 2-Quadrant-Detector consists of two subunits only, that correlate ON with ON and OFF with OFF signals. In order to distinguish between these two models, I first stimulated flies with apparent motion stimuli consisting of a sequence of two brightness steps at neighboring locations, while recording the motion detector output in lobula plate tangential cells of the blow fly. I found strongly direction-selective responses to ON-ON and OFF-OFF sequences, but also to ON-OFF and OFF-ON sequences. At first sight, these results seem to support the 4-Quadrant-Detector. However, I showed with simulations and an analytical treatment that the 2-Quadrant-Detector, when equipped with an appropriate preprocessing stage, is capable of reproducing such responses as well. Based on predictions from model simulations, I designed a new stimulus protocol consisting of a sequence of short brightness pulses instead of steps. For such stimuli, the 2-Quadrant-Detector does not produce significant responses to ON-OFF and OFF-ON sequences, in contrast to the 4-Quadrant-Detector. The corresponding recordings cannot be reconciled with the 4-Quadrant-Detector but are in good agreement with the 2-Quadrant-Detector. I therefore conclude that the internal structure of the y elementary motion detector consists of two non-interacting subunits for detecting ON and OFF motion, respectively. These results mark an important step in the ongoing dissection of the insect motion detection circuit by providing an updated model that better matches the structure and physiology of the corresponding neural hardware