Optimal local estimates of visual motion in a natural environment

Many organisms, from flies to humans, use visual signals to estimate their motion through the world. To explore the motion estimation problem, we have constructed a camera/gyroscope system that allows us to sample, at high temporal resolution, the joint distribution of input images and rotational motions during a long walk in the woods. From these data we construct the optimal estimator of velocity based on spatial and temporal derivatives of image intensity in small patches of the visual world. Over the bulk of the naturally occurring dynamic range, the optimal estimator exhibits the same systematic errors seen in neural and behavioral responses, including the confounding of velocity and contrast. These results suggest that apparent errors of sensory processing may reflect an optimal response to the physical signals in the environment

Neural coding of naturalistic motion stimuli

We study a wide field motion sensitive neuron in the visual system of the blowfly {\em Calliphora vicina}. By rotating the fly on a stepper motor outside in a wooded area, and along an angular motion trajectory representative of natural flight, we stimulate the fly's visual system with input that approaches the natural situation. The neural response is analyzed in the framework of information theory, using methods that are free from assumptions. We demonstrate that information about the motion trajectory increases as the light level increases over a natural range. This indicates that the fly's brain utilizes the increase in photon flux to extract more information from the photoreceptor array, suggesting that imprecision in neural signals is dominated by photon shot noise in the physical input, rather than by noise generated within the nervous system itself.Comment: 15 pages, 4 figure

Extending the dynamic range of transcription factor action by translational regulation

A crucial step in the regulation of gene expression is binding of transcription factor (TF) proteins to regulatory sites along the DNA. But transcription factors act at nanomolar concentrations, and noise due to random arrival of these molecules at their binding sites can severely limit the precision of regulation. Recent work on the optimization of information flow through regulatory networks indicates that the lower end of the dynamic range of concentrations is simply inaccessible, overwhelmed by the impact of this noise. Motivated by the behavior of homeodomain proteins, such as the maternal morphogen Bicoid in the fruit fly embryo, we suggest a scheme in which transcription factors also act as indirect translational regulators, binding to the mRNA of other transcription factors. Intuitively, each mRNA molecule acts as an independent sensor of the TF concentration, and averaging over these multiple sensors reduces the noise. We analyze information flow through this new scheme and identify conditions under which it outperforms direct transcriptional regulation. Our results suggest that the dual role of homeodomain proteins is not just a historical accident, but a solution to a crucial physics problem in the regulation of gene expression.Comment: 14 pages, 5 figure

The thermodynamics of prediction

A system responding to a stochastic driving signal can be interpreted as computing, by means of its dynamics, an implicit model of the environmental variables. The system's state retains information about past environmental fluctuations, and a fraction of this information is predictive of future ones. The remaining nonpredictive information reflects model complexity that does not improve predictive power, and thus represents the ineffectiveness of the model. We expose the fundamental equivalence between this model inefficiency and thermodynamic inefficiency, measured by dissipation. Our results hold arbitrarily far from thermodynamic equilibrium and are applicable to a wide range of systems, including biomolecular machines. They highlight a profound connection between the effective use of information and efficient thermodynamic operation: any system constructed to keep memory about its environment and to operate with maximal energetic efficiency has to be predictive.Comment: 5 pages, 1 figur

Congenital Chagas Disease in the United States: Cost Savings Through Maternal Screening

Chagas disease, caused by Trypanosoma cruzi, is transmitted by insect vectors through transfusions, transplants, insect feces in food, and from mother to child during gestation. Congenital infection could perpetuate Chagas disease indefinitely, even in countries without vector transmission. An estimated 30% of infected persons will develop lifelong, potentially fatal, cardiac or digestive complications. Treatment of infants with benznidazole is highly efficacious in eliminating infection. This work evaluates the costs of maternal screening and infant testing and treatment of Chagas disease in the United States. We constructed a decision-analytic model to find the lower cost option, comparing costs of testing and treatment, as needed, for mothers and infants with the lifetime societal costs without testing and the consequent morbidity and mortality due to lack of treatment or late treatment. We found that maternal screening, infant testing, and treatment of Chagas disease in the United States are cost saving for all rates of congenital transmission greater than 0.001% and all levels of maternal prevalence above 0.06% compared with no screening program. Newly approved diagnostics make universal screening cost saving with maternal prevalence as low as 0.008%. The present value of lifetime societal savings due to screening and treatment is about $634 million saved for every birth year cohort. The benefits of universal screening for T. cruzi as part of routine prenatal testing far outweigh the program costs for all U.S. births