History dependence in insect flight decisions during odor tracking

Natural decision-making often involves extended decision sequences in response to variable stimuli with complex structure. As an example, many animals follow odor plumes to locate food sources or mates, but turbulence breaks up the advected odor signal into intermittent filaments and puffs. This scenario provides an opportunity to ask how animals use sparse, instantaneous, and stochastic signal encounters to generate goal-oriented behavioral sequences. Here we examined the trajectories of flying fruit flies (Drosophila melanogaster) and mosquitoes (Aedes aegypti) navigating in controlled plumes of attractive odorants. While it is known that mean odor-triggered flight responses are dominated by upwind turns, individual responses are highly variable. We asked whether deviations from mean responses depended on specific features of odor encounters, and found that odor-triggered turns were slightly but significantly modulated by two features of odor encounters. First, encounters with higher concentrations triggered stronger upwind turns. Second, encounters occurring later in a sequence triggered weaker upwind turns. To contextualize the latter history dependence theoretically, we examined trajectories simulated from three normative tracking strategies. We found that neither a purely reactive strategy nor a strategy in which the tracker learned the plume centerline over time captured the observed history dependence. In contrast, “infotaxis”, in which flight decisions maximized expected information gain about source location, exhibited a history dependence aligned in sign with the data, though much larger in magnitude. These findings suggest that while true plume tracking is dominated by a reactive odor response it might also involve a history-dependent modulation of responses consistent with the accumulation of information about a source over multi-encounter timescales. This suggests that short-term memory processes modulating decision sequences may play a role in natural plume tracking

History dependence in insect flight decisions during odor tracking

Natural decision-making often involves extended decision sequences in response to variable stimuli with complex structure. As an example, many animals follow odor plumes to locate food sources or mates, but turbulence breaks up the advected odor signal into intermittent filaments and puffs. This scenario provides an opportunity to ask how animals use sparse, instantaneous, and stochastic signal encounters to generate goal-oriented behavioral sequences. Here we examined the trajectories of flying fruit flies (Drosophila melanogaster) and mosquitoes (Aedes aegypti) navigating in controlled plumes of attractive odorants. While it is known that mean odor-triggered flight responses are dominated by upwind turns, individual responses are highly variable. We asked whether deviations from mean responses depended on specific features of odor encounters, and found that odor-triggered turns were slightly but significantly modulated by two features of odor encounters. First, encounters with higher concentrations triggered stronger upwind turns. Second, encounters occurring later in a sequence triggered weaker upwind turns. To contextualize the latter history dependence theoretically, we examined trajectories simulated from three normative tracking strategies. We found that neither a purely reactive strategy nor a strategy in which the tracker learned the plume centerline over time captured the observed history dependence. In contrast, “infotaxis”, in which flight decisions maximized expected information gain about source location, exhibited a history dependence aligned in sign with the data, though much larger in magnitude. These findings suggest that while true plume tracking is dominated by a reactive odor response it might also involve a history-dependent modulation of responses consistent with the accumulation of information about a source over multi-encounter timescales. This suggests that short-term memory processes modulating decision sequences may play a role in natural plume tracking

A review of photovoltaic module technologies for increased performance in tropical climate

The global adoption and use of photovoltaic modules (PVMs) as the main source of energy is the key to realising the UN Millennium Development Goals on Green Energy. The technology – projected to contribute about 20% of world energy supply by 2050, over 60% by 2100 and leading to 50% reduction in global CO2 emissions – is threatened by its poor performance in tropical climate. Such performance discourages its regional acceptance. The magnitude of crucial module performance influencing factors (cell temperature, wind speed and relative humidity) reach critical values of 90 °C, 0.2 m/s and 85%, respectively in tropical climates which negatively impact module performance indices which include power output (PO), power conversion efficiency (PCE) and energy payback time (EPBT). This investigation reviews PVM technologies which include cell, contact and interconnection technologies. It identifies critical technology route(s) with potential to increase operational reliability of PVMs in the tropics when adopted. The cell performance is measured by PO, PCE and EPBT while contacts and interconnections performance is measured by the degree of recombination, shading losses and also the rate of thermo-mechanical degradation. It is found that the mono-crystalline cell has the best PCE of 25% while the Cadmium Telluride (CdTe) cell has the lowest EPBT of 8-months. Results show that the poly-crystalline cell has the largest market share amounting to 54%. The CdTe cell exhibits 0% drop in PCE at high-temperatures and low irradiance operations – demonstrating least affected PO by the conditions. Further results establish that back contacts and back-to-back interconnection technologies produce the least recombination losses and demonstrate absence of shading in addition to possessing longest interconnection fatigue life. Based on these findings, the authors propose a PVM comprising CdTe cell, back contacts and back-to-back interconnection technologies as the technology with latent capacity to produce improved performance in tropical climates

Contribution mapping: a method for mapping the contribution of research to enhance its impact.

Background: At a time of growing emphasis on both the use of research and accountability, it is important for research funders, researchers and other stakeholders to monitor and evaluate the extent to which research contributes to better action for health, and find ways to enhance the likelihood that beneficial contributions are realized. Past attempts to assess research 'impact' struggle with operationalizing 'impact', identifying the users of research and attributing impact to research projects as source. In this article we describe Contribution Mapping, a novel approach to research monitoring and evaluation that aims to assess contributions instead of impacts. The approach focuses on processes and actors and systematically assesses anticipatory efforts that aim to enhance contributions, so-called alignment efforts. The approach is designed to be useful for both accountability purposes and for assisting in better employing research to contribute to better action for health.Methods: Contribution Mapping is inspired by a perspective from social studies of science on how research and knowledge utilization processes evolve. For each research project that is assessed, a three-phase process map is developed that includes the main actors, activities and alignment efforts during research formulation, production and knowledge extension (e.g. dissemination and utilization). The approach focuses on the actors involved in, or interacting with, a research project (the linked actors) and the most likely influential users, who are referred to as potential key users. In the first stage, the investigators of the assessed project are interviewed to develop a preliminary version of the process map and first estimation of research-related contributions. In the second stage, potential key-users and other informants are interviewed to trace, explore and triangulate possible contributions. In the third stage, the presence and role of alignment efforts is analyzed and the preliminary results are shared with relevant stakeholders for feedback and validation. After inconsistencies are clarified or described, the results are shared with stakeholders for learning, improvement and accountability purposes.Conclusion: Contribution Mapping provides an interesting alternative to existing methods that aim to assess research impact. The method is expected to be useful for research monitoring, single case studies, comparing multiple cases and indicating how research can better be employed to contribute to better action for health. © 2012 Kok and Schuit; licensee BioMed Central Ltd

Rapid modulation of dynamics and computation in neural systems

Thesis (Ph.D.)--University of Washington, 2019A central goal in theoretical neuroscience is to understand how neural systems perform computations over the continuum of timescales that underlie behavior. In particular, what are the algorithms and mechanisms enabling single-neuron membrane voltage fluctuations, which occur over milliseconds, to produce the dynamics and information processing in behavior that unfold over hours to years? Notably, while the core ionic processes of membrane voltage fluctuations have been largely elucidated and while extensive theories and evidence exist to explain how slow modulation of neural network structures might underlie learning, almost nothing is known about the liminal regime of seconds to minutes that bridges these two timescales. In the work that follows I address three questions in three different systems, each of which centers around neural computations occurring over the timescales of seconds to minutes. I first investigate the navigational decisions made by flying insects during odor tracking, where I show that fruit flies and mosquitoes exhibit a history dependence in their odor-triggered turning responses that is qualitatively similar to an information-maximizing tracking strategy, but not to others. Next, in collaboration with Ari Zolin, Raphael Cohn, and Vanessa Ruta, I analyze the dynamics of dopaminergic neuromodulation of a short-term memory circuit in the fruit fly mushroom body, where we suggest that the fly dopamine system encodes multiplexed representations of a wide diversity of sensory, motor, and valence signals, some of which predict behavior several seconds in the future. Third, I develop a spiking neural network model capable of storing and replaying sequential activity patterns using a heterosynaptic and fast-acting biological plasticity rule, and which reconstructs sequences through the existing recurrent network structure. Collectively, these results elucidate the computational capacities of three distinct systems and shed new light on short-term information processing in neural computations from three novel angles. Finally, in collaboration with Sid Henriksen and Mark Wronkiewicz, I describe a simple network-growth model reproducing several statistical features of mouse brain network connectivity at the mesoscale; while this work does not explicitly address short-term computations, simplified statistical network models will be crucial to eventually understanding how such computations occur within large scale distributed brain networks