Virulent bacterial infection improves aversive learning performance in Drosophila melanogaster.

Virulent infections are expected to impair learning ability, either as a direct consequence of stressed physiological state or as an adaptive response that minimizes diversion of energy from immune defense. This prediction has been well supported for mammals and bees. Here, we report an opposite result in Drosophila melanogaster. Using an odor-mechanical shock conditioning paradigm, we found that intestinal infection with bacterial pathogens Pseudomonas entomophila or Erwinia c. carotovora improved flies' learning performance after a 1h retention interval. Infection with P. entomophila (but not E. c. carotovora) also improved learning performance after 5 min retention. No effect on learning performance was detected for intestinal infections with an avirulent GacA mutant of P. entomophila or for virulent systemic (hemocoel) infection with E. c. carotovora. Assays of unconditioned responses to odorants and shock do not support a major role for changes in general responsiveness to stimuli in explaining the changes in learning performance, although differences in their specific salience for learning cannot be excluded. Our results demonstrate that the effects of pathogens on learning performance in insects are less predictable than suggested by previous studies, and support the notion that immune stress can sometimes boost cognitive abilities.

Quality of Learners' Time and Learning Performance Beyond Quantitative Time-on-Task

Peer-reviewed

Ethnic diversity as a multilevel construct:the combined effects of dissimilarity, group diversity, and societal status on learning performance in work groups

The authors present a model of the multilevel effects of diversity on individual learning performance in work groups. For ethnically diverse work groups, the model predicts that group diversity elicits either positive or negative effects on individual learning performance, depending on whether a focal individual’s ethnic dissimilarity from other group members is high or low. By further considering the societal status of an individual’s ethnic origin within society (Anglo versus non-Anglo for our U.K. context), the authors hypothesize that the model’s predictions hold more strongly for non-Anglo group members than for Anglo group members. We test this model with data from 412 individuals working on a 24-week business simulation in 87 four- to seven-person groups with varying degrees of ethnic diversity. Two of the three hypotheses derived from the model received full support and one hypothesis received partial support. Implications for theory development, methods, and practice in applied group diversity research are discussed.

Evolutionary ecology of learning: insights from fruit flies

Ecologically and evolutionarily oriented research on learning has traditionally been carried out on vertebrates and bees. While less sophisticated than those animals, fruit flies (Drosophila) are capable of several forms of learning, and have an advantage of a short generation time, which makes them an ideal system for experimental evolution studies. This review summarizes the insights into evolutionary questions about learning gained in the last decade from evolutionary experiments on Drosophila. These experiments demonstrate that Drosophila have the genetic potential to evolve substantially improved learning performance in ecologically relevant learning tasks. In at least one set of selected populations the improved learning generalized to another task than that used to impose selection, involving a different behavior, different stimuli, and a different sensory channel for the aversive reinforcement. This improvement in learning ability was associated with reduction in other fitness-related traits, such as larval competitive ability and lifespan, pointing out to evolutionary trade-offs of improved learning. These trade-offs were confirmed by other evolutionary experiments where reduction in learning performance was observed as a correlated response to selection for tolerance to larval nutritional stress or for delayed aging. Such trade-offs could be one reason why fruit flies have not fully used up their evolutionary potential for learning ability. Finally, another evolutionary experiment with Drosophila provided the first direct evidence for the long-standing ideas that learning can under some circumstances accelerate and in other slow down genetically-based evolutionary change. These results demonstrate the usefulness of fruit flies as a model system to address evolutionary questions about learning.

The Relationship between Teachers and Students in the Classroom: Communicative Language Teaching Approach and Cooperative Learning Strategy to Improve Learning

The aim of this research is to to investigate how a supportive relationship between teachers and students in the classroom can improve the learning process. By having a good relationship with students, teachers can offer to students chances to be motivated and feel engaged in the learning process. Students will be engaged actively in the learning instead of being passive learners. I wish to investigate how using communicative approach and cooperative learning strategies while teaching do affect and improve students’ learning performance. To achieve these goals qualitative data collection was used as the primary method. The results show that teachers and students value a supportive and caring relationship between them and that interaction is essential to the teacher-student relationship. This sense of caring and supporting from teachers motivates students to become a more interested learner. Students benefit and are motivated when their teachers create a safe and trustful environment. And also the methods and strategies teachers uses, makes students feel engaged and stimulated to participate in the learning process. The students have in their mind that a positive relationship with their teachers positively impacts their interest and motivation in school which contributes to the enhancement of the learning process.

Effects of inbreeding on aversive learning in Drosophila.

Inbreeding adversely affects life history traits as well as various other fitness-related traits, but its effect on cognitive traits remains largely unexplored, despite their importance to fitness of many animals under natural conditions. We studied the effects of inbreeding on aversive learning (avoidance of an odour previously associated with mechanical shock) in multiple inbred lines of Drosophila melanogaster derived from a natural population through up to 12 generations of sib mating. Whereas the strongly inbred lines after 12 generations of inbreeding (0.75<F<0.93) consistently showed reduced egg-to-adult viability (on average by 28%), the reduction in learning performance varied among assays (average=18% reduction), being most pronounced for intermediate conditioning intensity. Furthermore, moderately inbred lines (F=0.38) showed no detectable decline in learning performance, but still had reduced egg-to-adult viability, which indicates that overall inbreeding effects on learning are mild. Learning performance varied among strongly inbred lines, indicating the presence of segregating variance for learning in the base population. However, the learning performance of some inbred lines matched that of outbred flies, supporting the dominance rather than the overdominance model of inbreeding depression for this trait. Across the inbred lines, learning performance was positively correlated with the egg-to-adult viability. This positive genetic correlation contradicts a trade-off observed in previous selection experiments and suggests that much of the genetic variation for learning is owing to pleiotropic effects of genes affecting functions related to survival. These results suggest that genetic variation that affects learning specifically (rather than pleiotropically through general physiological condition) is either low or mostly due to alleles with additive (semi-dominant) effects.

Natural variation in learning ability in "Drosophila melanogaster"

Learning is the ability of an organism to adapt to the changes of its environment in response to its past experience. It is a widespread ability in the animal kingdom, but its evolutionary aspects are poorly known. Learning ability is supposedly advantageous under some conditions, when environmental conditions are not too stable - because in this case there is no need to learn to predict any event in the environment - and not changing too fast - otherwise environmental cues cannot be used because they are not reliable. Nevertheless, learning ability is also known to be costly in terms of energy needed for neuronal synthesis, memory formation, initial mistakes. During my PhD, I focused on the study of genetic variability of learning ability in natural populations. Genetic variability is the basis on which natural selection and genetic drift can act. How does learning ability vary in nature? What are the roles of additive genetic variation or maternal effects in this variation? Is it involved in evolutionary trade-offs with other fitness-related traits?¦I investigated a natural population of fruit fly, Drosophila melanogaster, as a model organism. Its learning ability is easy to measure with associative memory tests. I used two research tools: multiple inbred and isofemale lines derived from a natural population as a representative sample. My work was divided into three parts.¦First, I investigated the effects of inbreeding on aversive learning (avoidance of an odor previously associated with mechanical shock). While the inbred lines consistently showed reduced egg-to-adult viability by 28 %, the effects of inbreeding on learning performance was 18 % and varied among assays, with a trend to be most pronounced for intermediate conditioning intensity. Variation among inbred lines indicates that ample genetic variance for learning was segregating in the base population, and suggests that the inbreeding depression observed in learning performance was mostly due to dominance rather than overdominance. Across the inbred lines, learning performance was positively correlated with the egg-to-adult viability. This positive genetic correlation contradicts previous studies which observed a trade-off between learning ability and lifespan or larval competitive ability. It suggests that much of the genetic variation for learning is due to pleiotropic effects of genes affecting other functions related to survival. Together with the overall mild effects of inbreeding on learning performance, this suggests that genetic variation specifically affecting learning is either very low, or is due to alleles with mostly additive (semi-dominant) effects. It also suggests that alleles reducing learning performance are on average partially recessive, because their effect does not appear in the outbred base population. Moreover, overdominance seems unlikely as major cause of the inbreeding depression, because even if the overall mean of the inbred line is smaller than the outbred base population, some of the inbred lines show the same learning score as the outbred base population. If overdominance played an important part in inbreeding depression, then all the homozygous lines should show lower learning ability than¦outbred base population.¦In the second part of my project, I sampled the same natural population again and derived isofemale lines (F=0.25) which are less adapted to laboratory conditions and therefore are more representative of the variance of the natural population. They also showed some genetic variability for learning, and for three other fitness-related traits possibly related with learning: resistance to bacterial infection, egg-to-adult viability and developmental time. Nevertheless, the genetic variance of learning ability did not appear to be smaller than the variance of the other traits. The positive correlation previously observed between learning ability and egg- to-adult viability did not appear in isofemale lines (nor a negative correlation). It suggests that there was still genetic variability within isofemale lines and that they did not fix the highly deleterious pleiotropic alleles possibly responsible for the previous correlation.¦In order to investigate the relative amount of nuclear (additive and non-additive effects) and extra-nuclear (maternal and paternal effect) components of variance in learning ability and other fitness-related traits among the inbred lines tested in part one, I performed a diallel cross between them. The nuclear additive genetic variance was higher than other components for learning ability and survival to learning ability, but in contrast, maternal effects were more variable than other effects for developmental traits. This suggests that maternal effects, which reflects effects from mitochondrial DNA, epigenetic effects, or the amount of nutrients that are invested by the mother in the egg, are more important in the early stage of life, and less at the adult stage. There was no additive genetic correlation between learning ability and other traits, indicating that the correlation between learning ability and egg-to-adult viability observed in the first pat of my project was mostly due to recessive genes.¦Finally, my results showed that learning ability is genetically variable. The diallel experiment showed additive genetic variance was the most important component of the total variance. Moreover, every inbred or isofemale line showed some learning ability. This suggested that alleles impairing learning ability are eliminated by selection, and therefore that learning ability is under strong selection in natural populations of Drosophila. My results cannot alone explain the maintenance of the observed genetic variation. Even if I cannot eliminate the hypothesis of pleiotropy between learning ability and the other fitness-related traits I measured, there is no evidence for any trade-off between these traits and learning ability. This contradicts what has been observed between learning ability and other traits like lifespan and larval competitivity.¦L'apprentissage représente la capacité d'un organisme à s'adapter aux changement de son environnement au cours de sa vie, en réponse à son expérience passée. C'est une capacité très répandue dans le règne animal, y compris pour les animaux les plus petits et les plus simples, mais les aspects évolutifs de l'apprentissage sont encore mal connus. L'apprentissage est supposé avantageux dans certaines conditions, quand l'environnement n'est ni trop stable - dans ce cas, il n'y a rien à apprendre - ni trop variable - dans ce cas, les indices sur lesquels se reposer changent trop vite pour apprendre. D'un autre côté, l'apprentissage a aussi des coûts, en terme de synthèse neuronale, pour la formation de la mémoire, ou de coûts d'erreur initiale d'apprentissage. Pendant ma thèse, j'ai étudié la variabilité génétique naturelle des capacités d'apprentissage. Comment varient les capacités d'apprentissage dans la nature ? Quelle est la part de variation additive, l'impact des effets maternel ? Est-ce que l'apprentissage est impliqué dans des interactions, de type compromis évolutifs, avec d'autres traits liés à la fitness ?¦Afin de répondre à ces questions, je me suis intéressée à la mouche du vinaigre, ou drosophile, un organisme modèle. Ses capacités d'apprentissage sont facile à étudier avec un test de mémoire reposant sur l'association entre un choc mécanique et une odeur. Pour étudier ses capacités naturelles, j'ai dérivé de types de lignées d'une population naturelle: des lignées consanguines et des lignées isofemelles.¦Dans une première partie, je me suis intéressée aux effets de la consanguinité sur les capacités d'apprentissage, qui sont peu connues. Alors que les lignées consanguines ont montré une réduction de 28% de leur viabilité (proportion d'adultes émergeants d'un nombre d'oeufs donnés), leurs capacités d'apprentissage n'ont été réduites que de 18%, la plus forte diminution étant obtenue pour un conditionnement modéré. En outre, j'ai également observé que les capacités d'apprentissage était positivement corrélée à la viabilité entre les lignées. Cette corrélation est surprenante car elle est en contradiction avec les résultats obtenus par d'autres études, qui montrent l'existence de compromis évolutifs entre les capacités d'apprentissage et d'autres traits comme le vieillissement ou la compétitivité larvaire. Elle suggère que la variation génétique des capacités d'apprentissage est due aux effets pleiotropes de gènes récessifs affectant d'autres fonctions liées à la survie. Ces résultats indiquent que la variation pour les capacités d'apprentissage est réduite comparée à celle d'autres traits ou est due à des allèles principalement récessifs. L'hypothèse de superdominance semble peu vraisemblable, car certaines des lignées consanguines ont obtenu des scores d'apprentissage égaux à ceux de la population non consanguine, alors qu'en cas de superdominance, elles auraient toutes dû obtenir des scores inférieurs.¦Dans la deuxième partie de mon projet, j'ai mesuré les capacités d'apprentissage de lignées isofemelles issues de la même population initiale que les lignées consanguines. Ces lignées sont issues chacune d'un seul couple, ce qui leur donne un taux d'hétérozygosité supérieur et évite l'élimination de lignées par fixation d'allèles délétères rares. Elles sont ainsi plus représentatives de la variabilité naturelle. Leur variabilité génétique est significative pour les capacités d'apprentissage, et trois traits liés à la fois à la fitness et à l'apprentissage: la viabilité, la résistance à l'infection bactérienne et la vitesse de développement. Cependant, la variabilité des capacités d'apprentissage n'apparaît cette fois pas inférieure à celle des autres traits et aucune corrélation n'est constatée entre les capacité d'apprentissage et les autres traits. Ceci suggère que la corrélation observée auparavant était surtout due à la fixation d'allèles récessifs délétères également responsables de la dépression de consanguinité.¦Durant la troisième partie de mon projet, je me suis penchée sur la décomposition de la variance observée entre les lignées consanguines observée en partie 1. Quatre composants ont été examinés: la variance due à des effets nucléaires (additifs et non additifs), et due à des effets parentaux (maternels et paternels). J'ai réalisé un croisement diallèle de toutes les lignées. La variance additive nucléaire s'est révélée supérieure aux autres composants pour les capacités d'apprentissage et la résistance à l'infection bactérienne. Par contre, les effets maternels étaient plus importants que les autres composants pour les traits développementaux (viabilité et vitesse de développement). Ceci suggère que les effets maternels, dus à G ADN mitochondrial, à l'épistasie ou à la quantité de nutriments investis dans l'oeuf par la mère, sont plus importants dans les premiers stades de développement et que leur effet s'estompe à l'âge adulte. Il n'y a en revanche pas de corrélation statistiquement significative entre les effets additifs des capacités d'apprentissage et des autres traits, ce qui indique encore une fois que la corrélation observée entre les capacités d'apprentissage et la viabilité dans la première partie du projet était due à des effets d'allèles partiellement récessifs.¦Au, final, mes résultats montrent bien l'existence d'une variabilité génétique pour les capacités d'apprentissage, et l'expérience du diallèle montre que la variance additive de cette capacité est importante, ce qui permet une réponse à la sélection naturelle. Toutes les lignées, consanguines ou isofemelles, ont obtenu des scores d'apprentissage supérieurs à zéro. Ceci suggère que les allèles supprimant les capacités d'apprentissage sont fortement contre-sélectionnés dans la nature Néanmoins, mes résultats ne peuvent pas expliquer le maintien de cette variabilité génétique par eux-même. Même si l'hypothèse de pléiotropie entre les capacités d'apprentissage et l'un des traits liés à la fitness que j'ai mesuré ne peut être éliminée, il n'y a aucune preuve d'un compromis évolutif pouvant contribuer au maintien de la variabilité.

Firm's network capability and innovation performance: evidences from China hi-tech industry

ABSTRACT : A firm's competitive advantage can arise from internal resources as well as from an interfirm network. -This dissertation investigates the competitive advantage of a firm involved in an innovation network by integrating strategic management theory and social network theory. It develops theory and provides empirical evidence that illustrates how a networked firm enables the network value and appropriates this value in an optimal way according to its strategic purpose. The four inter-related essays in this dissertation provide a framework that sheds light on the extraction of value from an innovation network by managing and designing the network in a proactive manner. The first essay reviews research in social network theory and knowledge transfer management, and identifies the crucial factors of innovation network configuration for a firm's learning performance or innovation output. The findings suggest that network structure, network relationship, and network position all impact on a firm's performance. Although the previous literature indicates that there are disagreements about the impact of dense or spare structure, as well as strong or weak ties, case evidence from Chinese software companies reveals that dense and strong connections with partners are positively associated with firms' performance. The second essay is a theoretical essay that illustrates the limitations of social network theory for explaining the source of network value and offers a new theoretical model that applies resource-based view to network environments. It suggests that network configurations, such as network structure, network relationship and network position, can be considered important network resources. In addition, this essay introduces the concept of network capability, and suggests that four types of network capabilities play an important role in unlocking the potential value of network resources and determining the distribution of network rents between partners. This essay also highlights the contingent effects of network capability on a firm's innovation output, and explains how the different impacts of network capability depend on a firm's strategic choices. This new theoretical model has been pre-tested with a case study of China software industry, which enhances the internal validity of this theory. The third essay addresses the questions of what impact network capability has on firm innovation performance and what are the antecedent factors of network capability. This essay employs a structural equation modelling methodology that uses a sample of 211 Chinese Hi-tech firms. It develops a measurement of network capability and reveals that networked firms deal with cooperation between, and coordination with partners on different levels according to their levels of network capability. The empirical results also suggests that IT maturity, the openness of culture, management system involved, and experience with network activities are antecedents of network capabilities. Furthermore, the two-group analysis of the role of international partner(s) shows that when there is a culture and norm gap between foreign partners, a firm must mobilize more resources and effort to improve its performance with respect to its innovation network. The fourth essay addresses the way in which network capabilities influence firm innovation performance. By using hierarchical multiple regression with data from Chinese Hi-tech firms, the findings suggest that there is a significant partial mediating effect of knowledge transfer on the relationships between network capabilities and innovation performance. The findings also reveal that the impacts of network capabilities divert with the environment and strategic decision the firm has made: exploration or exploitation. Network constructing capability provides a greater positive impact on and yields more contributions to innovation performance than does network operating capability in an exploration network. Network operating capability is more important than network constructing capability for innovative firms in an exploitation network. Therefore, these findings highlight that the firm can shape the innovation network proactively for better benefits, but when it does so, it should adjust its focus and change its efforts in accordance with its innovation purposes or strategic orientation.

The handaxe and the microscope: individual and social learning in a multidimensional model of adaptation

When individuals learn by trial-and-error, they perform randomly chosen actions and then reinforce those actions that led to a high payoff. However, individuals do not always have to physically perform an action in order to evaluate its consequences. Rather, they may be able to mentally simulate actions and their consequences without actually performing them. Such fictitious learners can select actions with high payoffs without making long chains of trial-and-error learning. Here, we analyze the evolution of an n-dimensional cultural trait (or artifact) by learning, in a payoff landscape with a single optimum. We derive the stochastic learning dynamics of the distance to the optimum in trait space when choice between alternative artifacts follows the standard logit choice rule. We show that for both trial-and-error and fictitious learners, the learning dynamics stabilize at an approximate distance of root n/(2 lambda(e)) away from the optimum, where lambda(e) is an effective learning performance parameter depending on the learning rule under scrutiny. Individual learners are thus unlikely to reach the optimum when traits are complex (n large), and so face a barrier to further improvement of the artifact. We show, however, that this barrier can be significantly reduced in a large population of learners performing payoff-biased social learning, in which case lambda(e) becomes proportional to population size. Overall, our results illustrate the effects of errors in learning, levels of cognition, and population size for the evolution of complex cultural traits. (C) 2013 Elsevier Inc. All rights reserved.

Emotions in a Web-based Learning Environment

The aim of this thesis was to examine emotions in a web-based learning environment (WBLE). Theoretically, the thesis was grounded on the dimensional model of emotions. Four empirical studies were conducted. Study I focused on students’ anxiety and their self-efficacy in computer-using situations. Studies II and III examined the influence of experienced emotions on students’ collaborative visible and non-collaborative invisible activities and lurking in a WBLE. Study II also focused on the antecedents of the emotions students experience in a web-based learning environment. Study IV concentrated on clarifying the differences between emotions experienced in face-to-face and web-based collaborative learning. The results of these studies are reported in four original research articles published in scientific journals. The present studies demonstrate that emotions are important determinants of student behaviour in a web-based learning, and justify the conclusion that interactions on the web can and do have an emotional content. Based on the results of these empirical studies, it can be concluded that the emotions students experience during the web-based learning result mostly from the social interactions rather than from the technological context. The studies indicate that the technology itself is not the only antecedent of students’ emotional reactions in the collaborative web-based learning situations. However, the technology itself also exerted an influence on students’ behaviour. It was found that students’ computer anxiety was associated with their negative expectations of the consequences of using technology-based learning environments in their studies. Moreover, the results also indicated that student behaviours in a WBLE can be divided into three partially overlapping classes: i) collaborative visible ii) non-collaborative invisible activities, and iii) lurking. What is more, students’ emotions experienced during the web-based learning affected how actively they participated in such activities in the environment. Especially lurkers, i.e. students who seldom participated in discussions but frequently visited the online environment, experienced more negatively valenced emotions during the courses than did the other students. This result indicates that such negatively toned emotional experiences can make the lurking individuals less eager to participate in other WBLE courses in the future. Therefore, future research should also focus more precisely on the reasons that cause individuals to lurk in online learning groups, and the development of learning tasks that do not encourage or permit lurking or inactivity. Finally, the results from the study comparing emotional reactions in web-based and face-to-face collaborative learning indicated that the learning by means of web-based communication resulted in more affective reactivity when compared to learning in a face-to-face situation. The results imply that the students in the web-based learning group experienced more intense emotions than the students in the face-to-face learning group.The interpretations of this result are that the lack of means for expressing emotional reactions and perceiving others’ emotions increased the affectivity in the web-based learning groups. Such increased affective reactivity could, for example, debilitate individual’s learning performance, especially in complex learning tasks. Therefore, it is recommended that in the future more studies should be focused on the possibilities to express emotions in a text-based web environment to ensure better means for communicating emotions, and subsequently, possibly decrease the high level of affectivity. However, we do not yet know whether the use of means for communicating emotional expressions via the web (for example, “smileys” or “emoticons”) would be beneficial or disadvantageous in formal learning situations. Therefore, future studies should also focus on assessing how the use of such symbols as a means for expressing emotions in a text-based web environment would affect students’ and teachers’ behaviour and emotional state in web-based learning environments.

New kernel functions and learning methods for text and data mining

Recent advances in machine learning methods enable increasingly the automatic construction of various types of computer assisted methods that have been difficult or laborious to program by human experts. The tasks for which this kind of tools are needed arise in many areas, here especially in the fields of bioinformatics and natural language processing. The machine learning methods may not work satisfactorily if they are not appropriately tailored to the task in question. However, their learning performance can often be improved by taking advantage of deeper insight of the application domain or the learning problem at hand. This thesis considers developing kernel-based learning algorithms incorporating this kind of prior knowledge of the task in question in an advantageous way. Moreover, computationally efficient algorithms for training the learning machines for specific tasks are presented. In the context of kernel-based learning methods, the incorporation of prior knowledge is often done by designing appropriate kernel functions. Another well-known way is to develop cost functions that fit to the task under consideration. For disambiguation tasks in natural language, we develop kernel functions that take account of the positional information and the mutual similarities of words. It is shown that the use of this information significantly improves the disambiguation performance of the learning machine. Further, we design a new cost function that is better suitable for the task of information retrieval and for more general ranking problems than the cost functions designed for regression and classification. We also consider other applications of the kernel-based learning algorithms such as text categorization, and pattern recognition in differential display. We develop computationally efficient algorithms for training the considered learning machines with the proposed kernel functions. We also design a fast cross-validation algorithm for regularized least-squares type of learning algorithm. Further, an efficient version of the regularized least-squares algorithm that can be used together with the new cost function for preference learning and ranking tasks is proposed. In summary, we demonstrate that the incorporation of prior knowledge is possible and beneficial, and novel advanced kernels and cost functions can be used in algorithms efficiently.

Learning Spike-Based Population Codes by Reward and Population Feedback

We investigate a recently proposed model for decision learning in a population of spiking neurons where synaptic plasticity is modulated by a population signal in addition to reward feedback. For the basic model, binary population decision making based on spike/no-spike coding, a detailed computational analysis is given about how learning performance depends on population size and task complexity. Next, we extend the basic model to n-ary decision making and show that it can also be used in conjunction with other population codes such as rate or even latency coding.

Gradient estimation in dendritic reinforcement learning

We study synaptic plasticity in a complex neuronal cell model where NMDA-spikes can arise in certain dendritic zones. In the context of reinforcement learning, two kinds of plasticity rules are derived, zone reinforcement (ZR) and cell reinforcement (CR), which both optimize the expected reward by stochastic gradient ascent. For ZR, the synaptic plasticity response to the external reward signal is modulated exclusively by quantities which are local to the NMDA-spike initiation zone in which the synapse is situated. CR, in addition, uses nonlocal feedback from the soma of the cell, provided by mechanisms such as the backpropagating action potential. Simulation results show that, compared to ZR, the use of nonlocal feedback in CR can drastically enhance learning performance. We suggest that the availability of nonlocal feedback for learning is a key advantage of complex neurons over networks of simple point neurons, which have previously been found to be largely equivalent with regard to computational capability.

Learning flexible sensori-motor mappings in a complex network

Given the complex structure of the brain, how can synaptic plasticity explain the learning and forgetting of associations when these are continuously changing? We address this question by studying different reinforcement learning rules in a multilayer network in order to reproduce monkey behavior in a visuomotor association task. Our model can only reproduce the learning performance of the monkey if the synaptic modifications depend on the pre- and postsynaptic activity, and if the intrinsic level of stochasticity is low. This favored learning rule is based on reward modulated Hebbian synaptic plasticity and shows the interesting feature that the learning performance does not substantially degrade when adding layers to the network, even for a complex problem.