Adaptation of single photon responses in photoreceptors of the housefly, Musca domestica: a novel spectral analysis

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.

Elementary and macroscopic light-induced currents and their Ca(2+)-dependence in the photoreceptors of Periplaneta americana

In a microvillar photoreceptor, absorption of an incident photon initiates a phototransduction reaction that generates a depolarizing light-induced current (LIC) in the microvillus. Although in-depth knowledge about these processes in photoreceptors of the fruitfly Drosophila is available, not much is known about their nature in other insect species. Here, we present description of some basic properties of both elementary and macroscopic LICs and their Ca(2+)-dependence in the photoreceptors of a dark-active species, the cockroach Periplaneta americana. Cockroach photoreceptors respond to single photon absorptions by generating quantum bumps with about 5-fold larger amplitudes than in Drosophila. At the macroscopic current level, cockroach photoreceptors responded to light with variable sensitivity and current waveform. This variability could be partially attributed to differences in whole-cell capacitance. Transient LICs, both elementary and macroscopic, showed only moderate dependence on extracellular Ca(2+). However, with long light pulses, response inactivation was largely abolished and the overall size of LICs increased when extracellular Ca(2+) was omitted. Finally, by determining relative ionic permeabilities from reversals of LICs, we demonstrate that when compared to Drosophila, cockroach light-gated channels are only moderately Ca(2+)-selective.

Visual coding in locust photoreceptors

Information capture by photoreceptors ultimately limits the quality of visual processing in the brain. Using conventional sharp microelectrodes, we studied how locust photoreceptors encode random (white-noise, WN) and naturalistic (1/f stimuli, NS) light patterns in vivo and how this coding changes with mean illumination and ambient temperature. We also examined the role of their plasma membrane in shaping voltage responses. We found that brightening or warming increase and accelerate voltage responses, but reduce noise, enabling photoreceptors to encode more information. For WN stimuli, this was accompanied by broadening of the linear frequency range. On the contrary, with NS the signaling took place within a constant bandwidth, possibly revealing a ‘preference’ for inputs with 1/f statistics. The faster signaling was caused by acceleration of the elementary phototransduction current - leading to bumps - and their distribution. The membrane linearly translated phototransduction currents into voltage responses without limiting the throughput of these messages. As the bumps reflected fast changes in membrane resistance, the data suggest that their shape is predominantly driven by fast changes in the light-gated conductance. On the other hand, the slower bump latency distribution is likely to represent slower enzymatic intracellular reactions. Furthermore, the Q₁₀s of bump duration and latency distribution depended on light intensity. Altogether, this study suggests that biochemical constraints imposed upon signaling change continuously as locust photoreceptors adapt to environmental light and temperature conditions.

Fly photoreceptors demonstrate energy-information trade-offs in neural coding

Trade-offs between energy consumption and neuronal performance must shape the design and evolution of nervous systems, but we lack empirical data showing how neuronal energy costs vary according to performance. Using intracellular recordings from the intact retinas of four flies, Drosophila melanogaster, D. virilis, Calliphora vicina, and Sarcophaga carnaria, we measured the rates at which homologous R1–6 photoreceptors of these species transmit information from the same stimuli and estimated the energy they consumed. In all species, both information rate and energy consumption increase with light intensity. Energy consumption rises from a baseline, the energy required to maintain the dark resting potential. This substantial fixed cost, ∼20% of a photoreceptor's maximum consumption, causes the unit cost of information (ATP molecules hydrolysed per bit) to fall as information rate increases. The highest information rates, achieved at bright daylight levels, differed according to species, from ∼200 bits s⁻¹ in D. melanogaster to ∼1,000 bits s⁻¹ in S. carnaria. Comparing species, the fixed cost, the total cost of signalling, and the unit cost (cost per bit) all increase with a photoreceptor's highest information rate to make information more expensive in higher performance cells. This law of diminishing returns promotes the evolution of economical structures by severely penalising overcapacity. Similar relationships could influence the function and design of many neurons because they are subject to similar biophysical constraints on information throughput.

Many animals show striking reductions or enlargements of sense organs or brain regions according to their lifestyle and habitat. For example, cave dwelling or subterranean animals often have reduced eyes and brain regions involved in visual processing. These differences suggest that although there are benefits to possessing a particular sense organ or brain region, there are also significant costs that shape the evolution of the nervous system, but little is known about this trade-off, particularly at the level of single neurons. We measured the trade-off between performance and energetic costs by recording electrical signals from single photoreceptors in different fly species. We discovered that photoreceptors in the blowfly transmit five times more information than the smaller photoreceptors of the diminutive fruit fly Drosophila. The blowfly pays a high price for better performance; its photoreceptor uses ten times more energy to code the same quantity of information. We conclude that, for basic biophysical reasons, neuronal energy consumption increases much more steeply than performance, and this intensifies the evolutionary pressure to reduce performance to the minimum required for adequate function. Thus the biophysical properties of sensory neurons help to explain why the sense organs and brains of different species vary in size and performance.

Evidence from single-neuron recordings supports the law of diminishing returns, i.e., high performance eyes in larger, faster flies have less efficient photoreceptors than those of their small, sluggish counterparts.