Neuroethology of olfactory-guided behavior and its potential application in the control of harmful insects

Harmful insects include pests of crops and storage goods, and vectors of human and animal diseases. Throughout their history, humans have been fighting them using diverse methods. The fairly recent development of synthetic chemical insecticides promised efficient crop and health protection at a relatively low cost. However, the negative effects of those insecticides on human health and the environment, as well as the development of insect resistance, have been fueling the search for alternative control tools. New and promising alternative methods to fight harmful insects include the manipulation of their behavior using synthetic versions of "semiochemicals", which are natural volatile and non-volatile substances involved in the intra-and/or inter-specific communication between organisms. Synthetic semiochemicals can be used as trap baits to monitor the presence of insects, so that insecticide spraying can be planned rationally (i.e., only when and where insects are actually present). Other methods that use semiochemicals include insect annihilation by mass trapping, attract-and-kill techniques, behavioral disruption, and the use of repellents. In the last decades many investigations focused on the neural bases of insect's responses to semiochemicals. Those studies help understand how the olfactory system detects and processes information about odors, which could lead to the design of efficient control tools, including odor baits, repellents or ways to confound insects. Here we review our current knowledge about the neural mechanisms controlling olfactory responses to semiochemicals in harmful insects. We also discuss how this neuroethology approach can be used to design or improve pest/vector management strategies.Fil: Reisenman, Carolina Esther. University of California at Berkeley; Estados UnidosFil: Lei, Hong. University of Arizona; Estados UnidosFil: Guerenstein, Pablo Gustavo. Provincia de Entre Ríos. Centro de Investigaciones Científicas y Transferencia de Tecnología a la Producción. Universidad Autónoma de Entre Ríos. Centro de Investigaciones Científicas y Transferencia de Tecnología a la Producción. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Centro de Investigaciones Científicas y Transferencia de Tecnología a la Producción; Argentina. Universidad Nacional de Entre Ríos. Facultad de Ingeniería; Argentin

Oral Branched-Chain Amino Acid Supplements That Reduce Brain Serotonin During Exercise in Rats Also Lower Brain Catecholamines

Exercise raises brain serotonin release and is postulated to cause fatigue in athletes; ingestion of branched-chain amino acids (BCAA), by competitively inhibiting tryptophan transport into brain, lowers brain tryptophan uptake and serotonin synthesis and release in rats, and reputedly in humans prevents exercise-induced increases in serotonin and fatigue. This latter effect in humans is disputed. But BCAA also competitively inhibit tyrosine uptake into brain, and thus catecholamine synthesis and release. Since increasing brain catecholamines enhances physical performance, BCAA ingestion could lower catecholamines, reduce performance and thus negate any serotonin-linked benefit. We therefore examined in rats whether BCAA would reduce both brain tryptophan and tyrosine concentrations and serotonin and catecholamine synthesis. Sedentary and exercising rats received BCAA or vehicle orally; tryptophan and tyrosine concentrations and serotonin and catecholamine synthesis rates were measured 1 h later in brain. BCAA reduced brain tryptophan and tyrosine concentrations, and serotonin and catecholamine synthesis. These reductions in tyrosine concentrations and catecholamine synthesis, but not tryptophan or serotonin synthesis, could be prevented by co-administering tyrosine with BCAA. Complete essential amino acid mixtures, used to maintain or build muscle mass, were also studied, and produced different effects on brain tryptophan and tyrosine concentrations and serotonin and catecholamine synthesis. Since pharmacologically increasing brain catecholamine function improves physical performance, the finding that BCAA reduce catecholamine synthesis may explain why this treatment does not enhance physical performance in humans, despite reducing serotonin synthesis. If so, adding tyrosine to BCAA supplements might allow a positive action on performance to emerge

Data-driven honeybee antennal lobe model suggests how stimulus-onset asynchrony can aid odour segregation

Insects have a remarkable ability to identify and track odour sources in multi-odour backgrounds. Recent behavioural experiments show that this ability relies on detecting millisecond stimulus asynchronies between odourants that originate from different sources. Honeybees, Apis mellifera , are able to distinguish mixtures where both odourants arrive at the same time (synchronous mixtures) from those where odourant onsets are staggered (asynchronous mixtures) down to an onset delay of only 6 ms. In this paper we explore this surprising ability in a model of the insects' primary olfactory brain area, the antennal lobe. We hypothesize that a winner-take-all inhibitory network of local neurons in the antennal lobe has a symmetry-breaking effect, such that the response pattern in projection neurons to an asynchronous mixture is different from the response pattern to the corresponding synchronous mixture for an extended period of time beyond the initial odourant onset where the two mixture conditions actually differ. The prolonged difference between response patterns to synchronous and asynchronous mixtures could facilitate odour segregation in downstream circuits of the olfactory pathway. We present a detailed data-driven model of the bee antennal lobe that reproduces a large data set of experimentally observed physiological odour responses, successfully implements the hypothesised symmetry-breaking mechanism and so demonstrates that this mechanism is consistent with our current knowledge of the olfactory circuits in the bee brain

Heterogeneous Multi-task Learning for Human Pose Estimation with Deep Convolutional Neural Network

We propose an heterogeneous multi-task learning framework for human pose estimation from monocular image with deep convolutional neural network. In particular, we simultaneously learn a pose-joint regressor and a sliding-window body-part detector in a deep network architecture. We show that including the body-part detection task helps to regularize the network, directing it to converge to a good solution. We report competitive and state-of-art results on several data sets. We also empirically show that the learned neurons in the middle layer of our network are tuned to localized body parts

Solving constraint-satisfaction problems with distributed neocortical-like neuronal networks

Finding actions that satisfy the constraints imposed by both external inputs and internal representations is central to decision making. We demonstrate that some important classes of constraint satisfaction problems (CSPs) can be solved by networks composed of homogeneous cooperative-competitive modules that have connectivity similar to motifs observed in the superficial layers of neocortex. The winner-take-all modules are sparsely coupled by programming neurons that embed the constraints onto the otherwise homogeneous modular computational substrate. We show rules that embed any instance of the CSPs planar four-color graph coloring, maximum independent set, and Sudoku on this substrate, and provide mathematical proofs that guarantee these graph coloring problems will convergence to a solution. The network is composed of non-saturating linear threshold neurons. Their lack of right saturation allows the overall network to explore the problem space driven through the unstable dynamics generated by recurrent excitation. The direction of exploration is steered by the constraint neurons. While many problems can be solved using only linear inhibitory constraints, network performance on hard problems benefits significantly when these negative constraints are implemented by non-linear multiplicative inhibition. Overall, our results demonstrate the importance of instability rather than stability in network computation, and also offer insight into the computational role of dual inhibitory mechanisms in neural circuits.Comment: Accepted manuscript, in press, Neural Computation (2018