Abstract
Noise is a prevalent and sometimes even dominant aspect of many biological processes. While many natural systems have adapted to attenuate or even usefully integrate noise, the variability it introduces often still delimits the achievable precision across biological functions. This is particularly so for visual phototransduction, the process responsible for converting photons of light into usable electrical signals (quantum bumps). Here, randomness of both the photon inputs (regarded as extrinsic noise) and the conversion process (intrinsic noise) are seen as two distinct, independent and significant limitations on visual reliability. Past research has attempted to quantify the relative effects of these noise sources by using approximate methods that do not fully account for the discrete, point process and time ordered nature of the problem. As a result the conclusions drawn from these different approaches have led to inconsistent expositions of phototransduction noise performance.This paper provides a fresh and complete analysis of the relative impact of intrinsic and extrinsic noise in invertebrate phototransduction using minimum mean squared error reconstruction techniques based on Bayesian point process (Snyder) filters. An integrate-fire based algorithm is developed to reliably estimate photon times from quantum bumps and Snyder filters are then used to causally estimate random light intensities both at the front and back end of the phototransduction cascade. Comparison of these estimates reveals that the dominant noise source transitions from extrinsic to intrinsic as light intensity increases. By extending the filtering techniques to account for delays, it is further found that among the intrinsic noise components, which include bump latency (mean delay and jitter) and shape (amplitude and width) variance, it is the mean delay that is critical to noise performance. As the timeliness of visual information is important for real-time action, this delay could potentially limit the speed at which invertebrates can respond to stimuli. Consequently, if one wants to increase visual fidelity, reducing the photoconversion lag is much more important than improving the regularity of the electrical signal.
Highlights
The intrinsic-extrinsic relative noise problem in phototransductionThe phototransduction cascade consists of a series of chemical reactions which convert light inputs into usable current signals at the retina
This paper focuses on disentangling the relative contributions of these various noise sources
This paper develops algorithms for estimating and bounding x^qb and mseqb
Summary
The intrinsic-extrinsic relative noise problem in phototransduction. The phototransduction cascade consists of a series of chemical reactions which convert light inputs into usable current signals at the retina. The cascade mechanism involves small numbers of molecules and randomly executing reactions. The electrical representation of each photon is nonidentical and subject to inevitable variability. The signal is degraded during photoconversion and the cascade said to be intrinsically noisy [2]. The cascade is not responsible for all the signal variability measurable at the retina. The light input itself introduces randomness through the variable timing of photons. Since the cascade has single photon sensitivity [3] this extrinsically introduced noise is transferred across the cascade. This paper focuses on disentangling the relative contributions of these various noise sources
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