Figure 4.
Encoding Performance of Real and Modeled Drosophila Photoreceptors to Naturalistic Stimuli (NS) Match, Showing that Adaptive Sampling Increases the Flow of Information
(A and B) In the simulations, stochastic microvilli models were set to use the corresponding average bump waveforms and latency distributions (here normalized for clarity), estimated from the white-noise contrast experiments at different illuminations (dim and bright; Figures 3H–3I). This was done by refixing two model parameters: ns for the bump shape and la for the latency distribution (see Supplemental Experimental Procedures).
(C and D) One hundred superimposed in vivo responses (light gray) and their average signals to repeated naturalistic dim and bright stimuli (equal contrast), respectively, and the corresponding simulations.
(E and F) NS activate microvilli stochastically with appropriate dynamics and statistics for the given mean illumination (A and B). Due to low-passing input (1/f-statistics; [1]), the number of activated microvilli is mostly responsible for the corresponding response waveforms (above). This encoding uses only a fraction of 30,000 microvilli (in repeated NS, maximally ∼68%) because NS contain long relatively dim periods, allowing refractory microvilli to return to the pool of available ones during stimulation.
(G) The corresponding signal-to-noise ratios (SNR) of real (mean ± SD, n = 5) and simulated responses. ∗∗ marks the difference at lower frequencies, probably due to slow adaptation, instrumental noise and damage, which the model lacks; ∗ marks extra information at higher frequencies, probably due to input from other cells in the network, which the model lacks. The overall shape of SNRs reflects 1/f statistics of NS, as dominated by its low-frequency content.
(H) Mean rate of information transfer of two best photoreceptors (black) and the model (gray) to the same NS at six different illuminations. Recordings' lower information transfer at three brightest intensities can be attributed to damage, experimental noise, and intracellular pupil, which progressively filter out photons. At dim intensities, the effect of experimental noise and adaptation may be compensated by the synaptic network introducing new information from neighboring photoreceptors [34, 40]. Grey dotted line shows how the quantum efficiency (Q.E.) of simulated photoreceptor output drops with brightening NS, while the information transfer approaches a constant rate. Error bars show SD.