Behavioral analysis of learning and memory in Anastrepha fraterculus

We evaluate the influence of prior exposure to artificial substrate for oviposition on learning and memory in the fruit fly Anastrepha fraterculus (Wiedemann) (Diptera: Tephritidae). Some females were previously exposed to artificial fruits made of water, agar, and blackberry [Rubus spec. (Rosaceae)] or guava [Psidium guajava L. (Myrtaceae)] pulp for 48 and 72 h. We also studied adult flies exposed for 72 h to essential oil of lemongrass [Cymbopogon citratus (DC) Stapf, Poaceae] and adult flies from larvae exposed to the oil. Control females were naive with respect to these experimental substrates. Prior experience with blackberry‐based artificial fruits resulted in an increase in the number of punctures and deposited eggs by A. fraterculus, and memory lasted for up to 72 h. On the other hand, fly behavior was independent of exposure to guava‐based substrate. Prior exposure of 1‐ or 15‐day‐old females to artificial substrate with lemongrass oil modified innate substrate selection behavior. The scent of lemongrass oil during the larval stage modified innate oviposition responses of adult A. fraterculus. The study shows that A. fraterculus females are able to learn and retain information through chemical stimuli released by both host (blackberry and guava) and non‐host (lemongrass) species, and they can use olfactory memory obtained during the larval stage to select oviposition sites.     

Chained learning architectures in a simple closed-loop behavioural context

Objective

Living creatures can learn or improve their behaviour by temporally correlating sensor cues where near-senses (e.g., touch, taste) follow after far-senses (vision, smell). Such type of learning is related to classical and/or operant conditioning. Algorithmically all these approaches are very simple and consist of single learning unit. The current study is trying to solve this problem focusing on chained learning architectures in a simple closed-loop behavioural context.

Methods

We applied temporal sequence learning (Porr B and Wörgötter F 2006) in a closed-loop behavioural system where a driving robot learns to follow a line. Here for the first time we introduced two types of chained learning architectures named linear chain and honeycomb chain. We analyzed such architectures in an open and closed-loop context and compared them to the simple learning unit.

Conclusions

By implementing two types of simple chained learning architectures we have demonstrated that stable behaviour can also be obtained in such architectures. Results also suggest that chained architectures can be employed and better behavioural performance can be obtained compared to simple architectures in cases where we have sparse inputs in time and learning normally fails because of weak correlations.     

Analytical performance models for closed-loop flexible assembly systems

Flexible Assembly Systems (FASs), which form an important subset of modern manufacturing systems, are finding increasing use in today's industry. In the planning and design phase of these systems, it is useful to have tools that predict system performance for various operating conditions. In this article, we present such a performance analysis tool based on queueing approximation for a class of FASs, namely, closed-loop flexible assembly systems (CL-FASs). For CL-FASs, we describe iterative algorithms for computing steady-state performance measures, including production rate and station utilizations. These algorithms are computationally simple and have a fast convergence rate. We derive a new approximation to correct the mean delay at each queue. This improves the accuracy of performance prediction, especially in the case of small CL-FASs. Comparisons with simulation results indicate that the approximation technique is reasonably accurate for a broad range of parameter values and system sizes. This makes possible efficient (fast and computationally inexpensive) analysis of CL-FASs under various conditions.     

Incremental learning in biological and machine learning systems

Incremental learning concepts are reviewed in machine learning and neurobiology. They are identified in evolution, neurodevelopment and learning. A timeline of qualitative axon, neuron and synapse development summarizes the review on neurodevelopment. A discussion of experimental results on data incremental learning with recurrent artificial neural networks reveals that incremental learning often seems to be more efficient or powerful than standard learning but can produce unexpected side effects. A characterization of incremental learning is proposed which takes the elaborated biological and machine learning concepts into account.     

Behavioral contingency analysis

This paper presents a formal symbolic language, with its own specialized vocabulary and grammar, for codifying any behavioral contingency, including the complex multiparty contingencies encountered in law, economics, business, public affairs, sociology, education, and psychotherapy. This language specifies the "if, then" and temporal relationships between acts and their consequences for the parties involved. It provides for the notation of the probabilities, magnitudes, positive or negative valences, or time delays of the consequences for the parties, and for the parties that would perceive, misperceive, not perceive, predict, mispredict, or not predict events. The language's fractal-like hierarchical and recursive grammar provides for the flexible combination and permutation of the modifiers of the language's four nouns: acts, consequences, time intervals, and agents of acts; and its four verbs: consequate, prevent, perceive, and predict-thereby giving the language the ability to describe and codify various nuances of such complex contingencies as fraud, betting, blackmail, various types of games, theft, crime and punishment, contracts, family dynamics, racing, competition, mutual deterrence, feuding, bargaining, deception, borrowing, insurance, elections, global warming, tipping for service, vigilance, sexual overtures, decision making, and mistaken identity. Applications to the management of practical situations and techniques for doing so, as well as applications in current behavior analysis research and neuroscience, are discussed.     

Behavioral and neural analysis of associative learning in the honeybee: a taste from the magic well

Equipped with a mini brain smaller than one cubic millimeter and containing only 950,000 neurons, honeybees could be indeed considered as having rather limited cognitive abilities. However, bees display a rich and interesting behavioral repertoire, in which learning and memory play a fundamental role in the framework of foraging activities. We focus on the question of whether adaptive behavior in honeybees exceeds simple forms of learning and whether the neural mechanisms of complex learning can be unraveled by studying the honeybee brain. Besides elemental forms of learning, in which bees learn specific and univocal links between events in their environment, bees also master different forms of non-elemental learning, including categorization, contextual learning and rule abstraction, both in the visual and in the olfactory domain. Different protocols allow accessing the neural substrates of some of these learning forms and understanding how complex problem solving can be achieved by a relatively simple neural architecture. These results underline the enormous richness of experience-dependent behavior in honeybees, its high flexibility, and the fact that it is possible to formalize and characterize in controlled laboratory protocols basic and higher-order cognitive processing using an insect as a model. This paper is dedicated to the memory of Guillermo ‘Willy’ Zaccardi (1972–2007), disciple and friend beyond time and distance, who will always be remembered with a smile.     

Candidate gene prioritization by network analysis of differential expression using machine learning approaches

Background  

Discovering novel disease genes is still challenging for diseases for which no prior knowledge - such as known disease genes or disease-related pathways - is available. Performing genetic studies frequently results in large lists of candidate genes of which only few can be followed up for further investigation. We have recently developed a computational method for constitutional genetic disorders that identifies the most promising candidate genes by replacing prior knowledge by experimental data of differential gene expression between affected and healthy individuals.     

A quantitative dynamical systems approach to differential learning: self-organization principle and order parameter equations

Differential learning is a learning concept that assists subjects to find individual optimal performance patterns for given complex motor skills. To this end, training is provided in terms of noisy training sessions that feature a large variety of between-exercises differences. In several previous experimental studies it has been shown that performance improvement due to differential learning is higher than due to traditional learning and performance improvement due to differential learning occurs even during post-training periods. In this study we develop a quantitative dynamical systems approach to differential learning. Accordingly, differential learning is regarded as a self-organized process that results in the emergence of subject- and context-dependent attractors. These attractors emerge due to noise-induced bifurcations involving order parameters in terms of learning rates. In contrast, traditional learning is regarded as an externally driven process that results in the emergence of environmentally specified attractors. Performance improvement during post-training periods is explained as an hysteresis effect. An order parameter equation for differential learning involving a fourth-order polynomial potential is discussed explicitly. New predictions concerning the relationship between traditional and differential learning are derived.     

Behavioral genetics in larval zebrafish: learning from the young

Deciphering the genetic code that determines how the vertebrate nervous system assembles into neural circuits that ultimately control behavior is a fascinating and challenging question in modern neurobiology. Because of the complexity of this problem, successful strategies require a simple yet focused experimental approach without limiting the scope of the discovery. Unbiased, large-scale forward genetic screens in invertebrate organisms have yielded great insight into the genetic regulation of neural circuit assembly and function. For many reasons, this highly successful approach has been difficult to recapitulate in the behavioral neuroscience field's classic vertebrate model organisms-rodents. Here, we discuss how larval zebrafish provide a promising model system to which we can apply the design of invertebrate behavior-based screens to reveal the genetic mechanisms critical for neural circuit assembly and function in vertebrates.     

Behavioral and neurochemical investigation of circadian time-place learning in the rat

The ability to form an association between the time and the place of food availability, namely time-place learning, is presumably important for survival. The present study was designed to examine time-place learning and to identify exogenous and endogenous factors that may affect this behavior in rats. In an initial experiment, rats displayed poor time-place behavior and appeared to prefer the feeder that was closer to the center aisle and water supply. When these cues were minimized in a subsequent experiment, rats consistently displayed the time-place discrimination by exhibiting food anticipatory activity (FAA) at the correct location prior to each meal time. These rats also showed significant correlations between the level of FAA and the amount of dopamine turnover (the dihydroxyphenylacetic acid/dopamine ratio) in the nucleus accumbens and paraventricular nucleus of the hypothalamus, indicating possible involvement of regional dopaminergic activity in time-place behavior. No correlation was found for norepinephrine, epinephrine, or serotonin. In addition, the correlation between FAA and dopamine turnover was not found when rats were entrained to only one meal per day. Together, the data suggest that rats can learn the time-place discrimination under proper experimental conditions and that dopamine may play a role in the expression of this behavior.     

The role of differential reinforcement in predator avoidance learning

Little is known about how predator recognition develops under natural conditions. Predispositions to respond to some stimuli preferentially are likely to interact with the effects of experience. Convergent evidence from several studies suggests that predator-nai;ve tammar wallabies (Macropus eugenii) have some ability to respond to vertebrate predators differently from non-predators and that antipredator responses can be selectively enhanced by experience. Here, we examined the effects of differential reinforcement on responses to a model fox (Vulpes vulpes), cat (Felis catus) and conspecific wallaby. During training, tammars experienced paired presentations of a model fox and a simulated capture, as well as presentations of a wallaby and a cat alone. Training enhanced responses to the fox, relative to the conspecific wallaby, but acquired responses to the two predators did not differ, despite repeated, non-reinforced presentations of the cat. Results suggest that experience interacts with the wallabies' ability to perceive predators as a natural category.     

Specialization in multi-agent systems through learning

Specialization is a common feature in animal societies that leads to an improvement in the fitness of the team members and to an increase in the resources obtained by the team. In this paper we propose a simple reinforcement learning approach to specialization in an artificial multi-agent system. The system is composed of homogeneous and non-communicating agents. Because there is no communication, the number of agents in the team can easily scale up. Agents have the same initial functionalities, but they learn to specialize and so cooperate to achieve a complex gathering task efficiently. Simulation experiments show how the multi-agent system specializes appropriately so as to reach optimal (or near-to-optimal) performance in unknown and changing environments. Received: 12 September 1996 / Accepted in revised form: 5 February 1997     

Behavioral and neural analysis of extinction

The neural mechanisms by which fear is inhibited are poorly understood at the present time. Behaviorally, a conditioned fear response may be reduced in intensity through a number of means. Among the simplest of these is extinction, a form of learning characterized by a decrease in the amplitude and frequency of a conditioned response when the conditioned stimulus that elicits it is repeatedly nonreinforced. Because clinical interventions for patients suffering from fear dysregulation seek to inhibit abnormal, presumably learned fear responses, an understanding of fear extinction is likely to inform and increase the efficacy of these forms of treatment. This review considers the behavioral, cellular, and molecular literatures on extinction and presents the most recent advances in our understanding while identifying issues that require considerable further research.     

Machine learning applications in systems metabolic engineering

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