WorldWideScience

Sample records for rank-based hebbian learning

  1. Adaptive polymeric system for Hebbian type learning

    OpenAIRE

    2011-01-01

    Abstract We present the experimental realization of an adaptive polymeric system displaying a ?learning behaviour?. The system consists on a statistically organized networks of memristive elements (memory-resitors) based on polyaniline. In a such network the path followed by the current increments its conductivity, a property which makes the system able to mimic Hebbian type learning and have application in hardware neural networks. After discussing the working principle of ...

  2. Hebbian Learning is about contingency, not contiguity, and explains the emergence of predictive mirror neurons

    NARCIS (Netherlands)

    Keysers, C.; Perrett, David I; Gazzola, Valeria

    Hebbian Learning should not be reduced to contiguity, as it detects contingency and causality. Hebbian Learning accounts of mirror neurons make predictions that differ from associative learning: Through Hebbian Learning, mirror neurons become dynamic networks that calculate predictions and

  3. Real-time modeling of primitive environments through wavelet sensors and Hebbian learning

    Science.gov (United States)

    Vaccaro, James M.; Yaworsky, Paul S.

    1999-06-01

    Modeling the world through sensory input necessarily provides a unique perspective for the observer. Given a limited perspective, objects and events cannot always be encoded precisely but must involve crude, quick approximations to deal with sensory information in a real- time manner. As an example, when avoiding an oncoming car, a pedestrian needs to identify the fact that a car is approaching before ascertaining the model or color of the vehicle. In our methodology, we use wavelet-based sensors with self-organized learning to encode basic sensory information in real-time. The wavelet-based sensors provide necessary transformations while a rank-based Hebbian learning scheme encodes a self-organized environment through translation, scale and orientation invariant sensors. Such a self-organized environment is made possible by combining wavelet sets which are orthonormal, log-scale with linear orientation and have automatically generated membership functions. In earlier work we used Gabor wavelet filters, rank-based Hebbian learning and an exponential modulation function to encode textural information from images. Many different types of modulation are possible, but based on biological findings the exponential modulation function provided a good approximation of first spike coding of `integrate and fire' neurons. These types of Hebbian encoding schemes (e.g., exponential modulation, etc.) are useful for quick response and learning, provide several advantages over contemporary neural network learning approaches, and have been found to quantize data nonlinearly. By combining wavelets with Hebbian learning we can provide a real-time front-end for modeling an intelligent process, such as the autonomous control of agents in a simulated environment.

  4. Associative (not Hebbian) learning and the mirror neuron system.

    Science.gov (United States)

    Cooper, Richard P; Cook, Richard; Dickinson, Anthony; Heyes, Cecilia M

    2013-04-12

    The associative sequence learning (ASL) hypothesis suggests that sensorimotor experience plays an inductive role in the development of the mirror neuron system, and that it can play this crucial role because its effects are mediated by learning that is sensitive to both contingency and contiguity. The Hebbian hypothesis proposes that sensorimotor experience plays a facilitative role, and that its effects are mediated by learning that is sensitive only to contiguity. We tested the associative and Hebbian accounts by computational modelling of automatic imitation data indicating that MNS responsivity is reduced more by contingent and signalled than by non-contingent sensorimotor training (Cook et al. [7]). Supporting the associative account, we found that the reduction in automatic imitation could be reproduced by an existing interactive activation model of imitative compatibility when augmented with Rescorla-Wagner learning, but not with Hebbian or quasi-Hebbian learning. The work argues for an associative, but against a Hebbian, account of the effect of sensorimotor training on automatic imitation. We argue, by extension, that associative learning is potentially sufficient for MNS development. Copyright © 2012 Elsevier Ireland Ltd. All rights reserved.

  5. Hebbian Learning is about contingency, not contiguity, and explains the emergence of predictive mirror neurons.

    Science.gov (United States)

    Keysers, Christian; Perrett, David I; Gazzola, Valeria

    2014-04-01

    Hebbian Learning should not be reduced to contiguity, as it detects contingency and causality. Hebbian Learning accounts of mirror neurons make predictions that differ from associative learning: Through Hebbian Learning, mirror neurons become dynamic networks that calculate predictions and prediction errors and relate to ideomotor theories. The social force of imitation is important for mirror neuron emergence and suggests canalization.

  6. Hebbian Learning is about contingency, not contiguity, and explains the emergence of predictive mirror neurons

    OpenAIRE

    Keysers, C.; Perrett, D.I.; Gazzola, V.

    2014-01-01

    Hebbian Learning should not be reduced to contiguity, as it detects contingency and causality. Hebbian Learning accounts of mirror neurons make predictions that differ from associative learning: Through Hebbian Learning, mirror neurons become dynamic networks that calculate predictions and prediction errors and relate to ideomotor theories. The social force of imitation is important for mirror neuron emergence and suggests canalization. Publisher PDF Peer reviewed

  7. Hebbian errors in learning: an analysis using the Oja model.

    Science.gov (United States)

    Rădulescu, Anca; Cox, Kingsley; Adams, Paul

    2009-06-21

    Recent work on long term potentiation in brain slices shows that Hebb's rule is not completely synapse-specific, probably due to intersynapse diffusion of calcium or other factors. We previously suggested that such errors in Hebbian learning might be analogous to mutations in evolution. We examine this proposal quantitatively, extending the classical Oja unsupervised model of learning by a single linear neuron to include Hebbian inspecificity. We introduce an error matrix E, which expresses possible crosstalk between updating at different connections. When there is no inspecificity, this gives the classical result of convergence to the first principal component of the input distribution (PC1). We show the modified algorithm converges to the leading eigenvector of the matrix EC, where C is the input covariance matrix. In the most biologically plausible case when there are no intrinsically privileged connections, E has diagonal elements Q and off-diagonal elements (1-Q)/(n-1), where Q, the quality, is expected to decrease with the number of inputs n and with a synaptic parameter b that reflects synapse density, calcium diffusion, etc. We study the dependence of the learning accuracy on b, n and the amount of input activity or correlation (analytically and computationally). We find that accuracy increases (learning becomes gradually less useful) with increases in b, particularly for intermediate (i.e., biologically realistic) correlation strength, although some useful learning always occurs up to the trivial limit Q=1/n. We discuss the relation of our results to Hebbian unsupervised learning in the brain. When the mechanism lacks specificity, the network fails to learn the expected, and typically most useful, result, especially when the input correlation is weak. Hebbian crosstalk would reflect the very high density of synapses along dendrites, and inevitably degrades learning.

  8. Hebbian learning and predictive mirror neurons for actions, sensations and emotions

    NARCIS (Netherlands)

    Keysers, C.; Gazzola, Valeria

    2014-01-01

    Spike-timing-dependent plasticity is considered the neurophysiological basis of Hebbian learning and has been shown to be sensitive to both contingency and contiguity between pre- and postsynaptic activity. Here, we will examine how applying this Hebbian learning rule to a system of interconnected

  9. Learning to Generate Sequences with Combination of Hebbian and Non-hebbian Plasticity in Recurrent Spiking Neural Networks.

    Science.gov (United States)

    Panda, Priyadarshini; Roy, Kaushik

    2017-01-01

    Synaptic Plasticity, the foundation for learning and memory formation in the human brain, manifests in various forms. Here, we combine the standard spike timing correlation based Hebbian plasticity with a non-Hebbian synaptic decay mechanism for training a recurrent spiking neural model to generate sequences. We show that inclusion of the adaptive decay of synaptic weights with standard STDP helps learn stable contextual dependencies between temporal sequences, while reducing the strong attractor states that emerge in recurrent models due to feedback loops. Furthermore, we show that the combined learning scheme suppresses the chaotic activity in the recurrent model substantially, thereby enhancing its' ability to generate sequences consistently even in the presence of perturbations.

  10. A rank-based Prediction Algorithm of Learning User's Intention

    Science.gov (United States)

    Shen, Jie; Gao, Ying; Chen, Cang; Gong, HaiPing

    Internet search has become an important part in people's daily life. People can find many types of information to meet different needs through search engines on the Internet. There are two issues for the current search engines: first, the users should predetermine the types of information they want and then change to the appropriate types of search engine interfaces. Second, most search engines can support multiple kinds of search functions, each function has its own separate search interface. While users need different types of information, they must switch between different interfaces. In practice, most queries are corresponding to various types of information results. These queries can search the relevant results in various search engines, such as query "Palace" contains the websites about the introduction of the National Palace Museum, blog, Wikipedia, some pictures and video information. This paper presents a new aggregative algorithm for all kinds of search results. It can filter and sort the search results by learning three aspects about the query words, search results and search history logs to achieve the purpose of detecting user's intention. Experiments demonstrate that this rank-based method for multi-types of search results is effective. It can meet the user's search needs well, enhance user's satisfaction, provide an effective and rational model for optimizing search engines and improve user's search experience.

  11. Towards autonomous neuroprosthetic control using Hebbian reinforcement learning.

    Science.gov (United States)

    Mahmoudi, Babak; Pohlmeyer, Eric A; Prins, Noeline W; Geng, Shijia; Sanchez, Justin C

    2013-12-01

    Our goal was to design an adaptive neuroprosthetic controller that could learn the mapping from neural states to prosthetic actions and automatically adjust adaptation using only a binary evaluative feedback as a measure of desirability/undesirability of performance. Hebbian reinforcement learning (HRL) in a connectionist network was used for the design of the adaptive controller. The method combines the efficiency of supervised learning with the generality of reinforcement learning. The convergence properties of this approach were studied using both closed-loop control simulations and open-loop simulations that used primate neural data from robot-assisted reaching tasks. The HRL controller was able to perform classification and regression tasks using its episodic and sequential learning modes, respectively. In our experiments, the HRL controller quickly achieved convergence to an effective control policy, followed by robust performance. The controller also automatically stopped adapting the parameters after converging to a satisfactory control policy. Additionally, when the input neural vector was reorganized, the controller resumed adaptation to maintain performance. By estimating an evaluative feedback directly from the user, the HRL control algorithm may provide an efficient method for autonomous adaptation of neuroprosthetic systems. This method may enable the user to teach the controller the desired behavior using only a simple feedback signal.

  12. Hebbian learning and predictive mirror neurons for actions, sensations and emotions

    OpenAIRE

    Keysers, C.; Gazzola, Valeria

    2014-01-01

    Spike-timing-dependent plasticity is considered the neurophysiological basis of Hebbian learning and has been shown to be sensitive to both contingency and contiguity between pre- and postsynaptic activity. Here, we will examine how applying this Hebbian learning rule to a system of interconnected neurons in the presence of direct or indirect re-afference (e.g. seeing/hearing one's own actions) predicts the emergence of mirror neurons with predictive properties. In this framework, we analyse ...

  13. Hebbian learning and predictive mirror neurons for actions, sensations and emotions.

    Science.gov (United States)

    Keysers, Christian; Gazzola, Valeria

    2014-01-01

    Spike-timing-dependent plasticity is considered the neurophysiological basis of Hebbian learning and has been shown to be sensitive to both contingency and contiguity between pre- and postsynaptic activity. Here, we will examine how applying this Hebbian learning rule to a system of interconnected neurons in the presence of direct or indirect re-afference (e.g. seeing/hearing one's own actions) predicts the emergence of mirror neurons with predictive properties. In this framework, we analyse how mirror neurons become a dynamic system that performs active inferences about the actions of others and allows joint actions despite sensorimotor delays. We explore how this system performs a projection of the self onto others, with egocentric biases to contribute to mind-reading. Finally, we argue that Hebbian learning predicts mirror-like neurons for sensations and emotions and review evidence for the presence of such vicarious activations outside the motor system.

  14. On Rationality of Decision Models Incorporating Emotion-Related Valuing and Hebbian Learning

    NARCIS (Netherlands)

    Treur, J.; Umair, M.

    2011-01-01

    In this paper an adaptive decision model based on predictive loops through feeling states is analysed from the perspective of rationality. Four different variations of Hebbian learning are considered for different types of connections in the decision model. To assess the extent of rationality, a

  15. Nonlinear Hebbian Learning as a Unifying Principle in Receptive Field Formation.

    Science.gov (United States)

    Brito, Carlos S N; Gerstner, Wulfram

    2016-09-01

    The development of sensory receptive fields has been modeled in the past by a variety of models including normative models such as sparse coding or independent component analysis and bottom-up models such as spike-timing dependent plasticity or the Bienenstock-Cooper-Munro model of synaptic plasticity. Here we show that the above variety of approaches can all be unified into a single common principle, namely nonlinear Hebbian learning. When nonlinear Hebbian learning is applied to natural images, receptive field shapes were strongly constrained by the input statistics and preprocessing, but exhibited only modest variation across different choices of nonlinearities in neuron models or synaptic plasticity rules. Neither overcompleteness nor sparse network activity are necessary for the development of localized receptive fields. The analysis of alternative sensory modalities such as auditory models or V2 development lead to the same conclusions. In all examples, receptive fields can be predicted a priori by reformulating an abstract model as nonlinear Hebbian learning. Thus nonlinear Hebbian learning and natural statistics can account for many aspects of receptive field formation across models and sensory modalities.

  16. Nonlinear Hebbian Learning as a Unifying Principle in Receptive Field Formation.

    Directory of Open Access Journals (Sweden)

    Carlos S N Brito

    2016-09-01

    Full Text Available The development of sensory receptive fields has been modeled in the past by a variety of models including normative models such as sparse coding or independent component analysis and bottom-up models such as spike-timing dependent plasticity or the Bienenstock-Cooper-Munro model of synaptic plasticity. Here we show that the above variety of approaches can all be unified into a single common principle, namely nonlinear Hebbian learning. When nonlinear Hebbian learning is applied to natural images, receptive field shapes were strongly constrained by the input statistics and preprocessing, but exhibited only modest variation across different choices of nonlinearities in neuron models or synaptic plasticity rules. Neither overcompleteness nor sparse network activity are necessary for the development of localized receptive fields. The analysis of alternative sensory modalities such as auditory models or V2 development lead to the same conclusions. In all examples, receptive fields can be predicted a priori by reformulating an abstract model as nonlinear Hebbian learning. Thus nonlinear Hebbian learning and natural statistics can account for many aspects of receptive field formation across models and sensory modalities.

  17. Non-Hebbian learning implementation in light-controlled resistive memory devices.

    Science.gov (United States)

    Ungureanu, Mariana; Stoliar, Pablo; Llopis, Roger; Casanova, Fèlix; Hueso, Luis E

    2012-01-01

    Non-Hebbian learning is often encountered in different bio-organisms. In these processes, the strength of a synapse connecting two neurons is controlled not only by the signals exchanged between the neurons, but also by an additional factor external to the synaptic structure. Here we show the implementation of non-Hebbian learning in a single solid-state resistive memory device. The output of our device is controlled not only by the applied voltages, but also by the illumination conditions under which it operates. We demonstrate that our metal/oxide/semiconductor device learns more efficiently at higher applied voltages but also when light, an external parameter, is present during the information writing steps. Conversely, memory erasing is more efficiently at higher applied voltages and in the dark. Translating neuronal activity into simple solid-state devices could provide a deeper understanding of complex brain processes and give insight into non-binary computing possibilities.

  18. Behavioral analysis of differential Hebbian learning in closed-loop systems

    DEFF Research Database (Denmark)

    Kulvicius, Tomas; Kolodziejski, Christoph; Tamosiunaite, Minija

    2010-01-01

    Understanding closed loop behavioral systems is a non-trivial problem, especially when they change during learning. Descriptions of closed loop systems in terms of information theory date back to the 1950s, however, there have been only a few attempts which take into account learning, mostly...... measuring information of inputs. In this study we analyze a specific type of closed loop system by looking at the input as well as the output space. For this, we investigate simulated agents that perform differential Hebbian learning (STDP). In the first part we show that analytical solutions can be found...

  19. The neuroscience of learning: beyond the Hebbian synapse.

    Science.gov (United States)

    Gallistel, C R; Matzel, Louis D

    2013-01-01

    From the traditional perspective of associative learning theory, the hypothesis linking modifications of synaptic transmission to learning and memory is plausible. It is less so from an information-processing perspective, in which learning is mediated by computations that make implicit commitments to physical and mathematical principles governing the domains where domain-specific cognitive mechanisms operate. We compare the properties of associative learning and memory to the properties of long-term potentiation, concluding that the properties of the latter do not explain the fundamental properties of the former. We briefly review the neuroscience of reinforcement learning, emphasizing the representational implications of the neuroscientific findings. We then review more extensively findings that confirm the existence of complex computations in three information-processing domains: probabilistic inference, the representation of uncertainty, and the representation of space. We argue for a change in the conceptual framework within which neuroscientists approach the study of learning mechanisms in the brain.

  20. Learning with three factors: modulating Hebbian plasticity with errors.

    Science.gov (United States)

    Kuśmierz, Łukasz; Isomura, Takuya; Toyoizumi, Taro

    2017-10-01

    Synaptic plasticity is a central theme in neuroscience. A framework of three-factor learning rules provides a powerful abstraction, helping to navigate through the abundance of models of synaptic plasticity. It is well-known that the dopamine modulation of learning is related to reward, but theoretical models predict other functional roles of the modulatory third factor; it may encode errors for supervised learning, summary statistics of the population activity for unsupervised learning or attentional feedback. Specialized structures may be needed in order to generate and propagate third factors in the neural network. Copyright © 2017 The Authors. Published by Elsevier Ltd.. All rights reserved.

  1. Spike-Based Bayesian-Hebbian Learning of Temporal Sequences

    DEFF Research Database (Denmark)

    Tully, Philip J; Lindén, Henrik; Hennig, Matthias H

    2016-01-01

    Many cognitive and motor functions are enabled by the temporal representation and processing of stimuli, but it remains an open issue how neocortical microcircuits can reliably encode and replay such sequences of information. To better understand this, a modular attractor memory network is proposed...... in which meta-stable sequential attractor transitions are learned through changes to synaptic weights and intrinsic excitabilities via the spike-based Bayesian Confidence Propagation Neural Network (BCPNN) learning rule. We find that the formation of distributed memories, embodied by increased periods...

  2. Spike-Based Bayesian-Hebbian Learning of Temporal Sequences.

    Directory of Open Access Journals (Sweden)

    Philip J Tully

    2016-05-01

    Full Text Available Many cognitive and motor functions are enabled by the temporal representation and processing of stimuli, but it remains an open issue how neocortical microcircuits can reliably encode and replay such sequences of information. To better understand this, a modular attractor memory network is proposed in which meta-stable sequential attractor transitions are learned through changes to synaptic weights and intrinsic excitabilities via the spike-based Bayesian Confidence Propagation Neural Network (BCPNN learning rule. We find that the formation of distributed memories, embodied by increased periods of firing in pools of excitatory neurons, together with asymmetrical associations between these distinct network states, can be acquired through plasticity. The model's feasibility is demonstrated using simulations of adaptive exponential integrate-and-fire model neurons (AdEx. We show that the learning and speed of sequence replay depends on a confluence of biophysically relevant parameters including stimulus duration, level of background noise, ratio of synaptic currents, and strengths of short-term depression and adaptation. Moreover, sequence elements are shown to flexibly participate multiple times in the sequence, suggesting that spiking attractor networks of this type can support an efficient combinatorial code. The model provides a principled approach towards understanding how multiple interacting plasticity mechanisms can coordinate hetero-associative learning in unison.

  3. Dynamic Hebbian Cross-Correlation Learning Resolves the Spike Timing Dependent Plasticity Conundrum

    Directory of Open Access Journals (Sweden)

    Tjeerd V. olde Scheper

    2018-01-01

    Full Text Available Spike Timing-Dependent Plasticity has been found to assume many different forms. The classic STDP curve, with one potentiating and one depressing window, is only one of many possible curves that describe synaptic learning using the STDP mechanism. It has been shown experimentally that STDP curves may contain multiple LTP and LTD windows of variable width, and even inverted windows. The underlying STDP mechanism that is capable of producing such an extensive, and apparently incompatible, range of learning curves is still under investigation. In this paper, it is shown that STDP originates from a combination of two dynamic Hebbian cross-correlations of local activity at the synapse. The correlation of the presynaptic activity with the local postsynaptic activity is a robust and reliable indicator of the discrepancy between the presynaptic neuron and the postsynaptic neuron's activity. The second correlation is between the local postsynaptic activity with dendritic activity which is a good indicator of matching local synaptic and dendritic activity. We show that this simple time-independent learning rule can give rise to many forms of the STDP learning curve. The rule regulates synaptic strength without the need for spike matching or other supervisory learning mechanisms. Local differences in dendritic activity at the synapse greatly affect the cross-correlation difference which determines the relative contributions of different neural activity sources. Dendritic activity due to nearby synapses, action potentials, both forward and back-propagating, as well as inhibitory synapses will dynamically modify the local activity at the synapse, and the resulting STDP learning rule. The dynamic Hebbian learning rule ensures furthermore, that the resulting synaptic strength is dynamically stable, and that interactions between synapses do not result in local instabilities. The rule clearly demonstrates that synapses function as independent localized

  4. Hebbian Learning in a Random Network Captures Selectivity Properties of the Prefrontal Cortex

    Science.gov (United States)

    Lindsay, Grace W.

    2017-01-01

    Complex cognitive behaviors, such as context-switching and rule-following, are thought to be supported by the prefrontal cortex (PFC). Neural activity in the PFC must thus be specialized to specific tasks while retaining flexibility. Nonlinear “mixed” selectivity is an important neurophysiological trait for enabling complex and context-dependent behaviors. Here we investigate (1) the extent to which the PFC exhibits computationally relevant properties, such as mixed selectivity, and (2) how such properties could arise via circuit mechanisms. We show that PFC cells recorded from male and female rhesus macaques during a complex task show a moderate level of specialization and structure that is not replicated by a model wherein cells receive random feedforward inputs. While random connectivity can be effective at generating mixed selectivity, the data show significantly more mixed selectivity than predicted by a model with otherwise matched parameters. A simple Hebbian learning rule applied to the random connectivity, however, increases mixed selectivity and enables the model to match the data more accurately. To explain how learning achieves this, we provide analysis along with a clear geometric interpretation of the impact of learning on selectivity. After learning, the model also matches the data on measures of noise, response density, clustering, and the distribution of selectivities. Of two styles of Hebbian learning tested, the simpler and more biologically plausible option better matches the data. These modeling results provide clues about how neural properties important for cognition can arise in a circuit and make clear experimental predictions regarding how various measures of selectivity would evolve during animal training. SIGNIFICANCE STATEMENT The prefrontal cortex is a brain region believed to support the ability of animals to engage in complex behavior. How neurons in this area respond to stimuli—and in particular, to combinations of stimuli (

  5. E-I balance emerges naturally from continuous Hebbian learning in autonomous neural networks.

    Science.gov (United States)

    Trapp, Philip; Echeveste, Rodrigo; Gros, Claudius

    2018-06-12

    Spontaneous brain activity is characterized in part by a balanced asynchronous chaotic state. Cortical recordings show that excitatory (E) and inhibitory (I) drivings in the E-I balanced state are substantially larger than the overall input. We show that such a state arises naturally in fully adapting networks which are deterministic, autonomously active and not subject to stochastic external or internal drivings. Temporary imbalances between excitatory and inhibitory inputs lead to large but short-lived activity bursts that stabilize irregular dynamics. We simulate autonomous networks of rate-encoding neurons for which all synaptic weights are plastic and subject to a Hebbian plasticity rule, the flux rule, that can be derived from the stationarity principle of statistical learning. Moreover, the average firing rate is regulated individually via a standard homeostatic adaption of the bias of each neuron's input-output non-linear function. Additionally, networks with and without short-term plasticity are considered. E-I balance may arise only when the mean excitatory and inhibitory weights are themselves balanced, modulo the overall activity level. We show that synaptic weight balance, which has been considered hitherto as given, naturally arises in autonomous neural networks when the here considered self-limiting Hebbian synaptic plasticity rule is continuously active.

  6. Emotions as a Vehicle for Rationality: Rational Decision Making Models Based on Emotion-Related Valuing and Hebbian Learning

    NARCIS (Netherlands)

    Treur, J.; Umair, M.

    2015-01-01

    In this paper an adaptive decision model based on predictive loops through feeling states is analysed from the perspective of rationality. Hebbian learning is considered for different types of connections in the decision model. To assess the extent of rationality, a measure is introduced reflecting

  7. Logarithmic distributions prove that intrinsic learning is Hebbian [version 2; referees: 2 approved

    Directory of Open Access Journals (Sweden)

    Gabriele Scheler

    2017-10-01

    Full Text Available In this paper, we present data for the lognormal distributions of spike rates, synaptic weights and intrinsic excitability (gain for neurons in various brain areas, such as auditory or visual cortex, hippocampus, cerebellum, striatum, midbrain nuclei. We find a remarkable consistency of heavy-tailed, specifically lognormal, distributions for rates, weights and gains in all brain areas examined. The difference between strongly recurrent and feed-forward connectivity (cortex vs. striatum and cerebellum, neurotransmitter (GABA (striatum or glutamate (cortex or the level of activation (low in cortex, high in Purkinje cells and midbrain nuclei turns out to be irrelevant for this feature. Logarithmic scale distribution of weights and gains appears to be a general, functional property in all cases analyzed. We then created a generic neural model to investigate adaptive learning rules that create and maintain lognormal distributions. We conclusively demonstrate that not only weights, but also intrinsic gains, need to have strong Hebbian learning in order to produce and maintain the experimentally attested distributions. This provides a solution to the long-standing question about the type of plasticity exhibited by intrinsic excitability.

  8. Hebbian learning of hand-centred representations in a hierarchical neural network model of the primate visual system.

    Science.gov (United States)

    Born, Jannis; Galeazzi, Juan M; Stringer, Simon M

    2017-01-01

    A subset of neurons in the posterior parietal and premotor areas of the primate brain respond to the locations of visual targets in a hand-centred frame of reference. Such hand-centred visual representations are thought to play an important role in visually-guided reaching to target locations in space. In this paper we show how a biologically plausible, Hebbian learning mechanism may account for the development of localized hand-centred representations in a hierarchical neural network model of the primate visual system, VisNet. The hand-centered neurons developed in the model use an invariance learning mechanism known as continuous transformation (CT) learning. In contrast to previous theoretical proposals for the development of hand-centered visual representations, CT learning does not need a memory trace of recent neuronal activity to be incorporated in the synaptic learning rule. Instead, CT learning relies solely on a Hebbian learning rule, which is able to exploit the spatial overlap that naturally occurs between successive images of a hand-object configuration as it is shifted across different retinal locations due to saccades. Our simulations show how individual neurons in the network model can learn to respond selectively to target objects in particular locations with respect to the hand, irrespective of where the hand-object configuration occurs on the retina. The response properties of these hand-centred neurons further generalise to localised receptive fields in the hand-centred space when tested on novel hand-object configurations that have not been explored during training. Indeed, even when the network is trained with target objects presented across a near continuum of locations around the hand during training, the model continues to develop hand-centred neurons with localised receptive fields in hand-centred space. With the help of principal component analysis, we provide the first theoretical framework that explains the behavior of Hebbian learning

  9. Hebbian learning of hand-centred representations in a hierarchical neural network model of the primate visual system

    Science.gov (United States)

    Born, Jannis; Stringer, Simon M.

    2017-01-01

    A subset of neurons in the posterior parietal and premotor areas of the primate brain respond to the locations of visual targets in a hand-centred frame of reference. Such hand-centred visual representations are thought to play an important role in visually-guided reaching to target locations in space. In this paper we show how a biologically plausible, Hebbian learning mechanism may account for the development of localized hand-centred representations in a hierarchical neural network model of the primate visual system, VisNet. The hand-centered neurons developed in the model use an invariance learning mechanism known as continuous transformation (CT) learning. In contrast to previous theoretical proposals for the development of hand-centered visual representations, CT learning does not need a memory trace of recent neuronal activity to be incorporated in the synaptic learning rule. Instead, CT learning relies solely on a Hebbian learning rule, which is able to exploit the spatial overlap that naturally occurs between successive images of a hand-object configuration as it is shifted across different retinal locations due to saccades. Our simulations show how individual neurons in the network model can learn to respond selectively to target objects in particular locations with respect to the hand, irrespective of where the hand-object configuration occurs on the retina. The response properties of these hand-centred neurons further generalise to localised receptive fields in the hand-centred space when tested on novel hand-object configurations that have not been explored during training. Indeed, even when the network is trained with target objects presented across a near continuum of locations around the hand during training, the model continues to develop hand-centred neurons with localised receptive fields in hand-centred space. With the help of principal component analysis, we provide the first theoretical framework that explains the behavior of Hebbian learning

  10. Hebbian learning of hand-centred representations in a hierarchical neural network model of the primate visual system.

    Directory of Open Access Journals (Sweden)

    Jannis Born

    Full Text Available A subset of neurons in the posterior parietal and premotor areas of the primate brain respond to the locations of visual targets in a hand-centred frame of reference. Such hand-centred visual representations are thought to play an important role in visually-guided reaching to target locations in space. In this paper we show how a biologically plausible, Hebbian learning mechanism may account for the development of localized hand-centred representations in a hierarchical neural network model of the primate visual system, VisNet. The hand-centered neurons developed in the model use an invariance learning mechanism known as continuous transformation (CT learning. In contrast to previous theoretical proposals for the development of hand-centered visual representations, CT learning does not need a memory trace of recent neuronal activity to be incorporated in the synaptic learning rule. Instead, CT learning relies solely on a Hebbian learning rule, which is able to exploit the spatial overlap that naturally occurs between successive images of a hand-object configuration as it is shifted across different retinal locations due to saccades. Our simulations show how individual neurons in the network model can learn to respond selectively to target objects in particular locations with respect to the hand, irrespective of where the hand-object configuration occurs on the retina. The response properties of these hand-centred neurons further generalise to localised receptive fields in the hand-centred space when tested on novel hand-object configurations that have not been explored during training. Indeed, even when the network is trained with target objects presented across a near continuum of locations around the hand during training, the model continues to develop hand-centred neurons with localised receptive fields in hand-centred space. With the help of principal component analysis, we provide the first theoretical framework that explains the behavior

  11. A mathematical analysis of the effects of Hebbian learning rules on the dynamics and structure of discrete-time random recurrent neural networks.

    Science.gov (United States)

    Siri, Benoît; Berry, Hugues; Cessac, Bruno; Delord, Bruno; Quoy, Mathias

    2008-12-01

    We present a mathematical analysis of the effects of Hebbian learning in random recurrent neural networks, with a generic Hebbian learning rule, including passive forgetting and different timescales, for neuronal activity and learning dynamics. Previous numerical work has reported that Hebbian learning drives the system from chaos to a steady state through a sequence of bifurcations. Here, we interpret these results mathematically and show that these effects, involving a complex coupling between neuronal dynamics and synaptic graph structure, can be analyzed using Jacobian matrices, which introduce both a structural and a dynamical point of view on neural network evolution. Furthermore, we show that sensitivity to a learned pattern is maximal when the largest Lyapunov exponent is close to 0. We discuss how neural networks may take advantage of this regime of high functional interest.

  12. Effects of arousal on cognitive control: empirical tests of the conflict-modulated Hebbian-learning hypothesis.

    Science.gov (United States)

    Brown, Stephen B R E; van Steenbergen, Henk; Kedar, Tomer; Nieuwenhuis, Sander

    2014-01-01

    An increasing number of empirical phenomena that were previously interpreted as a result of cognitive control, turn out to reflect (in part) simple associative-learning effects. A prime example is the proportion congruency effect, the finding that interference effects (such as the Stroop effect) decrease as the proportion of incongruent stimuli increases. While this was previously regarded as strong evidence for a global conflict monitoring-cognitive control loop, recent evidence has shown that the proportion congruency effect is largely item-specific and hence must be due to associative learning. The goal of our research was to test a recent hypothesis about the mechanism underlying such associative-learning effects, the conflict-modulated Hebbian-learning hypothesis, which proposes that the effect of conflict on associative learning is mediated by phasic arousal responses. In Experiment 1, we examined in detail the relationship between the item-specific proportion congruency effect and an autonomic measure of phasic arousal: task-evoked pupillary responses. In Experiment 2, we used a task-irrelevant phasic arousal manipulation and examined the effect on item-specific learning of incongruent stimulus-response associations. The results provide little evidence for the conflict-modulated Hebbian-learning hypothesis, which requires additional empirical support to remain tenable.

  13. Effects of arousal on cognitive control: Empirical tests of the conflict-modulated Hebbian-learning hypothesis

    Directory of Open Access Journals (Sweden)

    Stephen B.R.E. Brown

    2014-01-01

    Full Text Available An increasing number of empirical phenomena that were previously interpreted as a result of cognitive control, turn out to reflect (in part simple associative-learning effects. A prime example is the proportion congruency effect, the finding that interference effects (such as the Stroop effect decrease as the proportion of incongruent stimuli increases. While this was previously regarded as strong evidence for a global conflict monitoring-cognitive control loop, recent evidence has shown that the proportion congruency effect is largely item-specific and hence must be due to associative learning. The goal of our research was to test a recent hypothesis about the mechanism underlying such associative-learning effects, the conflict-modulated Hebbian-learning hypothesis, which proposes that the effect of conflict on associative learning is mediated by phasic arousal responses. In Experiment 1, we examined in detail the relationship between the item-specific proportion congruency effect and an autonomic measure of phasic arousal: task-evoked pupillary responses. In Experiment 2, we used a task-irrelevant phasic arousal manipulation and examined the effect on item-specific learning of incongruent stimulus-response associations. The results provide little evidence for the conflict-modulated Hebbian-learning hypothesis, which requires additional empirical support to remain tenable.

  14. A global bioheat model with self-tuning optimal regulation of body temperature using Hebbian feedback covariance learning.

    Science.gov (United States)

    Ong, M L; Ng, E Y K

    2005-12-01

    In the lower brain, body temperature is continually being regulated almost flawlessly despite huge fluctuations in ambient and physiological conditions that constantly threaten the well-being of the body. The underlying control problem defining thermal homeostasis is one of great enormity: Many systems and sub-systems are involved in temperature regulation and physiological processes are intrinsically complex and intertwined. Thus the defining control system has to take into account the complications of nonlinearities, system uncertainties, delayed feedback loops as well as internal and external disturbances. In this paper, we propose a self-tuning adaptive thermal controller based upon Hebbian feedback covariance learning where the system is to be regulated continually to best suit its environment. This hypothesis is supported in part by postulations of the presence of adaptive optimization behavior in biological systems of certain organisms which face limited resources vital for survival. We demonstrate the use of Hebbian feedback covariance learning as a possible self-adaptive controller in body temperature regulation. The model postulates an important role of Hebbian covariance adaptation as a means of reinforcement learning in the thermal controller. The passive system is based on a simplified 2-node core and shell representation of the body, where global responses are captured. Model predictions are consistent with observed thermoregulatory responses to conditions of exercise and rest, and heat and cold stress. An important implication of the model is that optimal physiological behaviors arising from self-tuning adaptive regulation in the thermal controller may be responsible for the departure from homeostasis in abnormal states, e.g., fever. This was previously unexplained using the conventional "set-point" control theory.

  15. A Hebbian learning rule gives rise to mirror neurons and links them to control theoretic inverse models

    Directory of Open Access Journals (Sweden)

    Alexander eHanuschkin

    2013-06-01

    Full Text Available Mirror neurons are neurons whose responses to the observation of a motor act resemble responses measured during production of that act. Computationally, mirror neurons have been viewed as evidence for the existence of internal inverse models. Such models, rooted within control theory, map desired sensory targets onto the motor commands required to generate those targets. To jointly explore both the formation of mirrored responses and their functional contribution to inverse models, we develop a correlation-based theory of interactions between a sensory and a motor area. We show that a simple eligibility-weighted Hebbian learning rule, operating within a sensorimotor loop during motor explorations and stabilized by heterosynaptic competition, naturally gives rise to mirror neurons as well as control theoretic inverse models encoded in the synaptic weights from sensory to motor neurons. Crucially, we find that the correlational structure or stereotypy of the neural code underlying motor explorations determines the nature of the learned inverse model: Random motor codes lead to causal inverses that map sensory activity patterns to their motor causes; such inverses are maximally useful, they allow for imitating arbitrary sensory target sequences. By contrast, stereotyped motor codes lead to less useful predictive inverses that map sensory activity to future motor actions.Our theory generalizes previous work on inverse models by showing that such models can be learned in a simple Hebbian framework without the need for error signals or backpropagation, and it makes new conceptual connections between the causal nature of inverse models, the statistical structure of motor variability, and the time-lag between sensory and motor responses of mirror neurons. Applied to bird song learning, our theory can account for puzzling aspects of the song system, including necessity of sensorimotor gating and selectivity of auditory responses to bird’s own song

  16. A Hebbian learning rule gives rise to mirror neurons and links them to control theoretic inverse models.

    Science.gov (United States)

    Hanuschkin, A; Ganguli, S; Hahnloser, R H R

    2013-01-01

    Mirror neurons are neurons whose responses to the observation of a motor act resemble responses measured during production of that act. Computationally, mirror neurons have been viewed as evidence for the existence of internal inverse models. Such models, rooted within control theory, map-desired sensory targets onto the motor commands required to generate those targets. To jointly explore both the formation of mirrored responses and their functional contribution to inverse models, we develop a correlation-based theory of interactions between a sensory and a motor area. We show that a simple eligibility-weighted Hebbian learning rule, operating within a sensorimotor loop during motor explorations and stabilized by heterosynaptic competition, naturally gives rise to mirror neurons as well as control theoretic inverse models encoded in the synaptic weights from sensory to motor neurons. Crucially, we find that the correlational structure or stereotypy of the neural code underlying motor explorations determines the nature of the learned inverse model: random motor codes lead to causal inverses that map sensory activity patterns to their motor causes; such inverses are maximally useful, by allowing the imitation of arbitrary sensory target sequences. By contrast, stereotyped motor codes lead to less useful predictive inverses that map sensory activity to future motor actions. Our theory generalizes previous work on inverse models by showing that such models can be learned in a simple Hebbian framework without the need for error signals or backpropagation, and it makes new conceptual connections between the causal nature of inverse models, the statistical structure of motor variability, and the time-lag between sensory and motor responses of mirror neurons. Applied to bird song learning, our theory can account for puzzling aspects of the song system, including necessity of sensorimotor gating and selectivity of auditory responses to bird's own song (BOS) stimuli.

  17. Hebbian learning in a model with dynamic rate-coded neurons: an alternative to the generative model approach for learning receptive fields from natural scenes.

    Science.gov (United States)

    Hamker, Fred H; Wiltschut, Jan

    2007-09-01

    Most computational models of coding are based on a generative model according to which the feedback signal aims to reconstruct the visual scene as close as possible. We here explore an alternative model of feedback. It is derived from studies of attention and thus, probably more flexible with respect to attentive processing in higher brain areas. According to this model, feedback implements a gain increase of the feedforward signal. We use a dynamic model with presynaptic inhibition and Hebbian learning to simultaneously learn feedforward and feedback weights. The weights converge to localized, oriented, and bandpass filters similar as the ones found in V1. Due to presynaptic inhibition the model predicts the organization of receptive fields within the feedforward pathway, whereas feedback primarily serves to tune early visual processing according to the needs of the task.

  18. Demystifying social cognition : a Hebbian perspective

    NARCIS (Netherlands)

    Keysers, C; Perrett, DI

    For humans and monkeys, understanding the actions of others is central to survival. Here we review the physiological properties of three cortical areas involved in this capacity: the STS, PF and F5. Based on the anatomical connections of these areas, and the Hebbian learning rule, we propose a

  19. Integrating Hebbian and homeostatic plasticity: introduction.

    Science.gov (United States)

    Fox, Kevin; Stryker, Michael

    2017-03-05

    Hebbian plasticity is widely considered to be the mechanism by which information can be coded and retained in neurons in the brain. Homeostatic plasticity moves the neuron back towards its original state following a perturbation, including perturbations produced by Hebbian plasticity. How then does homeostatic plasticity avoid erasing the Hebbian coded information? To understand how plasticity works in the brain, and therefore to understand learning, memory, sensory adaptation, development and recovery from injury, requires development of a theory of plasticity that integrates both forms of plasticity into a whole. In April 2016, a group of computational and experimental neuroscientists met in London at a discussion meeting hosted by the Royal Society to identify the critical questions in the field and to frame the research agenda for the next steps. Here, we provide a brief introduction to the papers arising from the meeting and highlight some of the themes to have emerged from the discussions.This article is part of the themed issue 'Integrating Hebbian and homeostatic plasticity'. © 2017 The Author(s).

  20. Recruitment and Consolidation of Cell Assemblies for Words by Way of Hebbian Learning and Competition in a Multi-Layer Neural Network.

    Science.gov (United States)

    Garagnani, Max; Wennekers, Thomas; Pulvermüller, Friedemann

    2009-06-01

    Current cognitive theories postulate either localist representations of knowledge or fully overlapping, distributed ones. We use a connectionist model that closely replicates known anatomical properties of the cerebral cortex and neurophysiological principles to show that Hebbian learning in a multi-layer neural network leads to memory traces (cell assemblies) that are both distributed and anatomically distinct. Taking the example of word learning based on action-perception correlation, we document mechanisms underlying the emergence of these assemblies, especially (i) the recruitment of neurons and consolidation of connections defining the kernel of the assembly along with (ii) the pruning of the cell assembly's halo (consisting of very weakly connected cells). We found that, whereas a learning rule mapping covariance led to significant overlap and merging of assemblies, a neurobiologically grounded synaptic plasticity rule with fixed LTP/LTD thresholds produced minimal overlap and prevented merging, exhibiting competitive learning behaviour. Our results are discussed in light of current theories of language and memory. As simulations with neurobiologically realistic neural networks demonstrate here spontaneous emergence of lexical representations that are both cortically dispersed and anatomically distinct, both localist and distributed cognitive accounts receive partial support.

  1. View-tolerant face recognition and Hebbian learning imply mirror-symmetric neural tuning to head orientation

    Science.gov (United States)

    Leibo, Joel Z.; Liao, Qianli; Freiwald, Winrich A.; Anselmi, Fabio; Poggio, Tomaso

    2017-01-01

    SUMMARY The primate brain contains a hierarchy of visual areas, dubbed the ventral stream, which rapidly computes object representations that are both specific for object identity and robust against identity-preserving transformations like depth-rotations [1, 2]. Current computational models of object recognition, including recent deep learning networks, generate these properties through a hierarchy of alternating selectivity-increasing filtering and tolerance-increasing pooling operations, similar to simple-complex cells operations [3, 4, 5, 6]. Here we prove that a class of hierarchical architectures and a broad set of biologically plausible learning rules generate approximate invariance to identity-preserving transformations at the top level of the processing hierarchy. However, all past models tested failed to reproduce the most salient property of an intermediate representation of a three-level face-processing hierarchy in the brain: mirror-symmetric tuning to head orientation [7]. Here we demonstrate that one specific biologically-plausible Hebb-type learning rule generates mirror-symmetric tuning to bilaterally symmetric stimuli like faces at intermediate levels of the architecture and show why it does so. Thus the tuning properties of individual cells inside the visual stream appear to result from group properties of the stimuli they encode and to reflect the learning rules that sculpted the information-processing system within which they reside. PMID:27916522

  2. Adaptive Game Level Creation through Rank-based Interactive Evolution

    DEFF Research Database (Denmark)

    Liapis, Antonios; Martínez, Héctor Pérez; Togelius, Julian

    2013-01-01

    as fitness functions for the optimization of the generated content. The preference models are built via ranking-based preference learning, while the content is generated via evolutionary search. The proposed method is evaluated on the creation of strategy game maps, and its performance is tested using...

  3. Anti-Hebbian long-term potentiation in the hippocampal feedback inhibitory circuit.

    Science.gov (United States)

    Lamsa, Karri P; Heeroma, Joost H; Somogyi, Peter; Rusakov, Dmitri A; Kullmann, Dimitri M

    2007-03-02

    Long-term potentiation (LTP), which approximates Hebb's postulate of associative learning, typically requires depolarization-dependent glutamate receptors of the NMDA (N-methyl-D-aspartate) subtype. However, in some neurons, LTP depends instead on calcium-permeable AMPA-type receptors. This is paradoxical because intracellular polyamines block such receptors during depolarization. We report that LTP at synapses on hippocampal interneurons mediating feedback inhibition is "anti-Hebbian":Itis induced by presynaptic activity but prevented by postsynaptic depolarization. Anti-Hebbian LTP may occur in interneurons that are silent during periods of intense pyramidal cell firing, such as sharp waves, and lead to their altered activation during theta activity.

  4. Sleep: The hebbian reinforcement of the local inhibitory synapses.

    Science.gov (United States)

    Touzet, Claude

    2015-09-01

    Sleep is ubiquitous among the animal realm, and represents about 30% of our lives. Despite numerous efforts, the reason behind our need for sleep is still unknown. The Theory of neuronal Cognition (TnC) proposes that sleep is the period of time during which the local inhibitory synapses (in particular the cortical ones) are replenished. Indeed, as long as the active brain stays awake, hebbian learning guarantees that efficient inhibitory synapses lose their efficiency – just because they are efficient at avoiding the activation of the targeted neurons. Since hebbian learning is the only known mechanism of synapse modification, it follows that to replenish the inhibitory synapses' efficiency, source and targeted neurons must be activated together. This is achieved by a local depolarization that may travel (wave). The period of time during which such slow waves are experienced has been named the "slow-wave sleep" (SWS). It is cut into several pieces by shorter periods of paradoxical sleep (REM) which activity resembles that of the awake state. Indeed, SWS – because it only allows local neural activation – decreases the excitatory long distance connections strength. To avoid losing the associations built during the awake state, these long distance activations are played again during the REM sleep. REM and SWS sleeps act together to guarantee that when the subject awakes again, his inhibitory synaptic efficiency is restored and his (excitatory) long distance associations are still there. Copyright © 2015 Elsevier Ltd. All rights reserved.

  5. The dependence of neuronal encoding efficiency on Hebbian plasticity and homeostatic regulation of neurotransmitter release

    Science.gov (United States)

    Faghihi, Faramarz; Moustafa, Ahmed A.

    2015-01-01

    Synapses act as information filters by different molecular mechanisms including retrograde messenger that affect neuronal spiking activity. One of the well-known effects of retrograde messenger in presynaptic neurons is a change of the probability of neurotransmitter release. Hebbian learning describe a strengthening of a synapse between a presynaptic input onto a postsynaptic neuron when both pre- and postsynaptic neurons are coactive. In this work, a theory of homeostatic regulation of neurotransmitter release by retrograde messenger and Hebbian plasticity in neuronal encoding is presented. Encoding efficiency was measured for different synaptic conditions. In order to gain high encoding efficiency, the spiking pattern of a neuron should be dependent on the intensity of the input and show low levels of noise. In this work, we represent spiking trains as zeros and ones (corresponding to non-spike or spike in a time bin, respectively) as words with length equal to three. Then the frequency of each word (here eight words) is measured using spiking trains. These frequencies are used to measure neuronal efficiency in different conditions and for different parameter values. Results show that neurons that have synapses acting as band-pass filters show the highest efficiency to encode their input when both Hebbian mechanism and homeostatic regulation of neurotransmitter release exist in synapses. Specifically, the integration of homeostatic regulation of feedback inhibition with Hebbian mechanism and homeostatic regulation of neurotransmitter release in the synapses leads to even higher efficiency when high stimulus intensity is presented to the neurons. However, neurons with synapses acting as high-pass filters show no remarkable increase in encoding efficiency for all simulated synaptic plasticity mechanisms. This study demonstrates the importance of cooperation of Hebbian mechanism with regulation of neurotransmitter release induced by rapid diffused retrograde

  6. Anti-Hebbian Spike Timing Dependent Plasticity and Adaptive Sensory Processing

    Directory of Open Access Journals (Sweden)

    Patrick D Roberts

    2010-12-01

    Full Text Available Adaptive processing influences the central nervous system's interpretation of incoming sensory information. One of the functions of this adaptative sensory processing is to allow the nervous system to ignore predictable sensory information so that it may focus on important new information needed to improve performance of specific tasks. The mechanism of spike timing-dependent plasticity (STDP has proven to be intriguing in this context because of its dual role in long-term memory and ongoing adaptation to maintain optimal tuning of neural responses. Some of the clearest links between STDP and adaptive sensory processing have come from in vitro, in vivo, and modeling studies of the electrosensory systems of fish. Plasticity in such systems is anti-Hebbian, i.e. presynaptic inputs that repeatedly precede and hence could contribute to a postsynaptic neuron’s firing are weakened. The learning dynamics of anti-Hebbian STDP learning rules are stable if the timing relations obey strict constraints. The stability of these learning rules leads to clear predictions of how functional consequences can arise from the detailed structure of the plasticity. Here we review the connection between theoretical predictions and functional consequences of anti-Hebbian STDP, focusing on adaptive processing in the electrosensory system of weakly electric fish. After introducing electrosensory adaptive processing and the dynamics of anti-Hebbian STDP learning rules, we address issues of predictive sensory cancellation and novelty detection, descending control of plasticity, synaptic scaling, and optimal sensory tuning. We conclude with examples in other systems where these principles may apply.

  7. Anti-hebbian spike-timing-dependent plasticity and adaptive sensory processing.

    Science.gov (United States)

    Roberts, Patrick D; Leen, Todd K

    2010-01-01

    Adaptive sensory processing influences the central nervous system's interpretation of incoming sensory information. One of the functions of this adaptive sensory processing is to allow the nervous system to ignore predictable sensory information so that it may focus on important novel information needed to improve performance of specific tasks. The mechanism of spike-timing-dependent plasticity (STDP) has proven to be intriguing in this context because of its dual role in long-term memory and ongoing adaptation to maintain optimal tuning of neural responses. Some of the clearest links between STDP and adaptive sensory processing have come from in vitro, in vivo, and modeling studies of the electrosensory systems of weakly electric fish. Plasticity in these systems is anti-Hebbian, so that presynaptic inputs that repeatedly precede, and possibly could contribute to, a postsynaptic neuron's firing are weakened. The learning dynamics of anti-Hebbian STDP learning rules are stable if the timing relations obey strict constraints. The stability of these learning rules leads to clear predictions of how functional consequences can arise from the detailed structure of the plasticity. Here we review the connection between theoretical predictions and functional consequences of anti-Hebbian STDP, focusing on adaptive processing in the electrosensory system of weakly electric fish. After introducing electrosensory adaptive processing and the dynamics of anti-Hebbian STDP learning rules, we address issues of predictive sensory cancelation and novelty detection, descending control of plasticity, synaptic scaling, and optimal sensory tuning. We conclude with examples in other systems where these principles may apply.

  8. A Spiking Working Memory Model Based on Hebbian Short-Term Potentiation

    Science.gov (United States)

    Fiebig, Florian

    2017-01-01

    A dominant theory of working memory (WM), referred to as the persistent activity hypothesis, holds that recurrently connected neural networks, presumably located in the prefrontal cortex, encode and maintain WM memory items through sustained elevated activity. Reexamination of experimental data has shown that prefrontal cortex activity in single units during delay periods is much more variable than predicted by such a theory and associated computational models. Alternative models of WM maintenance based on synaptic plasticity, such as short-term nonassociative (non-Hebbian) synaptic facilitation, have been suggested but cannot account for encoding of novel associations. Here we test the hypothesis that a recently identified fast-expressing form of Hebbian synaptic plasticity (associative short-term potentiation) is a possible mechanism for WM encoding and maintenance. Our simulations using a spiking neural network model of cortex reproduce a range of cognitive memory effects in the classical multi-item WM task of encoding and immediate free recall of word lists. Memory reactivation in the model occurs in discrete oscillatory bursts rather than as sustained activity. We relate dynamic network activity as well as key synaptic characteristics to electrophysiological measurements. Our findings support the hypothesis that fast Hebbian short-term potentiation is a key WM mechanism. SIGNIFICANCE STATEMENT Working memory (WM) is a key component of cognition. Hypotheses about the neural mechanism behind WM are currently under revision. Reflecting recent findings of fast Hebbian synaptic plasticity in cortex, we test whether a cortical spiking neural network model with such a mechanism can learn a multi-item WM task (word list learning). We show that our model can reproduce human cognitive phenomena and achieve comparable memory performance in both free and cued recall while being simultaneously compatible with experimental data on structure, connectivity, and

  9. A Spiking Working Memory Model Based on Hebbian Short-Term Potentiation.

    Science.gov (United States)

    Fiebig, Florian; Lansner, Anders

    2017-01-04

    A dominant theory of working memory (WM), referred to as the persistent activity hypothesis, holds that recurrently connected neural networks, presumably located in the prefrontal cortex, encode and maintain WM memory items through sustained elevated activity. Reexamination of experimental data has shown that prefrontal cortex activity in single units during delay periods is much more variable than predicted by such a theory and associated computational models. Alternative models of WM maintenance based on synaptic plasticity, such as short-term nonassociative (non-Hebbian) synaptic facilitation, have been suggested but cannot account for encoding of novel associations. Here we test the hypothesis that a recently identified fast-expressing form of Hebbian synaptic plasticity (associative short-term potentiation) is a possible mechanism for WM encoding and maintenance. Our simulations using a spiking neural network model of cortex reproduce a range of cognitive memory effects in the classical multi-item WM task of encoding and immediate free recall of word lists. Memory reactivation in the model occurs in discrete oscillatory bursts rather than as sustained activity. We relate dynamic network activity as well as key synaptic characteristics to electrophysiological measurements. Our findings support the hypothesis that fast Hebbian short-term potentiation is a key WM mechanism. Working memory (WM) is a key component of cognition. Hypotheses about the neural mechanism behind WM are currently under revision. Reflecting recent findings of fast Hebbian synaptic plasticity in cortex, we test whether a cortical spiking neural network model with such a mechanism can learn a multi-item WM task (word list learning). We show that our model can reproduce human cognitive phenomena and achieve comparable memory performance in both free and cued recall while being simultaneously compatible with experimental data on structure, connectivity, and neurophysiology of the underlying

  10. Distant Supervision for Relation Extraction with Ranking-Based Methods

    Directory of Open Access Journals (Sweden)

    Yang Xiang

    2016-05-01

    Full Text Available Relation extraction has benefited from distant supervision in recent years with the development of natural language processing techniques and data explosion. However, distant supervision is still greatly limited by the quality of training data, due to its natural motivation for greatly reducing the heavy cost of data annotation. In this paper, we construct an architecture called MIML-sort (Multi-instance Multi-label Learning with Sorting Strategies, which is built on the famous MIML framework. Based on MIML-sort, we propose three ranking-based methods for sample selection with which we identify relation extractors from a subset of the training data. Experiments are set up on the KBP (Knowledge Base Propagation corpus, one of the benchmark datasets for distant supervision, which is large and noisy. Compared with previous work, the proposed methods produce considerably better results. Furthermore, the three methods together achieve the best F1 on the official testing set, with an optimal enhancement of F1 from 27.3% to 29.98%.

  11. The dependence of neuronal encoding efficiency on Hebbian plasticity and homeostatic regulation of neurotransmitter release

    Directory of Open Access Journals (Sweden)

    Faramarz eFaghihi

    2015-04-01

    Full Text Available Synapses act as information filters by different molecular mechanisms including retrograde messenger that affect neuronal spiking activity. One of the well-known effects of retrograde messenger in presynaptic neurons is a change of the probability of neurotransmitter release. Hebbian learning describe a strengthening of a synapse between a presynaptic input onto a postsynaptic neuron when both pre- and postsynaptic neurons are coactive. In this work, a theory of homeostatic regulation of neurotransmitter release by retrograde messenger and Hebbian plasticity in neuronal encoding is presented. Encoding efficiency was measured for different synaptic conditions. In order to gain high encoding efficiency, the spiking pattern of a neuron should be dependent on the intensity of the input and show low levels of noise. In this work, we represent spiking trains as zeros and ones (corresponding to non-spike or spike in a time bin, respectively as words with length equal to three. Then the frequency of each word (here eight words is measured using spiking trains. These frequencies are used to measure neuronal efficiency in different conditions and for different parameter values. Results show that neurons that have synapses acting as band-pass filters show the highest efficiency to encode their input when both Hebbian mechanism and homeostatic regulation of neurotransmitter release exist in synapses. Specifically, the integration of homeostatic regulation of feedback inhibition with Hebbian mechanism and homeostatic regulation of neurotransmitter release in the synapses leads to even higher efficiency when high stimulus intensity is presented to the neurons. However, neurons with synapses acting as high-pass filters show no remarkable increase in encoding efficiency for all simulated synaptic plasticity mechanisms.

  12. Reward-Modulated Hebbian Plasticity as Leverage for Partially Embodied Control in Compliant Robotics

    Science.gov (United States)

    Burms, Jeroen; Caluwaerts, Ken; Dambre, Joni

    2015-01-01

    In embodied computation (or morphological computation), part of the complexity of motor control is offloaded to the body dynamics. We demonstrate that a simple Hebbian-like learning rule can be used to train systems with (partial) embodiment, and can be extended outside of the scope of traditional neural networks. To this end, we apply the learning rule to optimize the connection weights of recurrent neural networks with different topologies and for various tasks. We then apply this learning rule to a simulated compliant tensegrity robot by optimizing static feedback controllers that directly exploit the dynamics of the robot body. This leads to partially embodied controllers, i.e., hybrid controllers that naturally integrate the computations that are performed by the robot body into a neural network architecture. Our results demonstrate the universal applicability of reward-modulated Hebbian learning. Furthermore, they demonstrate the robustness of systems trained with the learning rule. This study strengthens our belief that compliant robots should or can be seen as computational units, instead of dumb hardware that needs a complex controller. This link between compliant robotics and neural networks is also the main reason for our search for simple universal learning rules for both neural networks and robotics. PMID:26347645

  13. International Conference on Robust Rank-Based and Nonparametric Methods

    CERN Document Server

    McKean, Joseph

    2016-01-01

    The contributors to this volume include many of the distinguished researchers in this area. Many of these scholars have collaborated with Joseph McKean to develop underlying theory for these methods, obtain small sample corrections, and develop efficient algorithms for their computation. The papers cover the scope of the area, including robust nonparametric rank-based procedures through Bayesian and big data rank-based analyses. Areas of application include biostatistics and spatial areas. Over the last 30 years, robust rank-based and nonparametric methods have developed considerably. These procedures generalize traditional Wilcoxon-type methods for one- and two-sample location problems. Research into these procedures has culminated in complete analyses for many of the models used in practice including linear, generalized linear, mixed, and nonlinear models. Settings are both multivariate and univariate. With the development of R packages in these areas, computation of these procedures is easily shared with r...

  14. Syntactic sequencing in Hebbian cell assemblies.

    Science.gov (United States)

    Wennekers, Thomas; Palm, Günther

    2009-12-01

    Hebbian cell assemblies provide a theoretical framework for the modeling of cognitive processes that grounds them in the underlying physiological neural circuits. Recently we have presented an extension of cell assemblies by operational components which allows to model aspects of language, rules, and complex behaviour. In the present work we study the generation of syntactic sequences using operational cell assemblies timed by unspecific trigger signals. Syntactic patterns are implemented in terms of hetero-associative transition graphs in attractor networks which cause a directed flow of activity through the neural state space. We provide regimes for parameters that enable an unspecific excitatory control signal to switch reliably between attractors in accordance with the implemented syntactic rules. If several target attractors are possible in a given state, noise in the system in conjunction with a winner-takes-all mechanism can randomly choose a target. Disambiguation can also be guided by context signals or specific additional external signals. Given a permanently elevated level of external excitation the model can enter an autonomous mode, where it generates temporal grammatical patterns continuously.

  15. Binding and segmentation via a neural mass model trained with Hebbian and anti-Hebbian mechanisms.

    Science.gov (United States)

    Cona, Filippo; Zavaglia, Melissa; Ursino, Mauro

    2012-04-01

    Synchronization of neural activity in the gamma band, modulated by a slower theta rhythm, is assumed to play a significant role in binding and segmentation of multiple objects. In the present work, a recent neural mass model of a single cortical column is used to analyze the synaptic mechanisms which can warrant synchronization and desynchronization of cortical columns, during an autoassociation memory task. The model considers two distinct layers communicating via feedforward connections. The first layer receives the external input and works as an autoassociative network in the theta band, to recover a previously memorized object from incomplete information. The second realizes segmentation of different objects in the gamma band. To this end, units within both layers are connected with synapses trained on the basis of previous experience to store objects. The main model assumptions are: (i) recovery of incomplete objects is realized by excitatory synapses from pyramidal to pyramidal neurons in the same object; (ii) binding in the gamma range is realized by excitatory synapses from pyramidal neurons to fast inhibitory interneurons in the same object. These synapses (both at points i and ii) have a few ms dynamics and are trained with a Hebbian mechanism. (iii) Segmentation is realized with faster AMPA synapses, with rise times smaller than 1 ms, trained with an anti-Hebbian mechanism. Results show that the model, with the previous assumptions, can correctly reconstruct and segment three simultaneous objects, starting from incomplete knowledge. Segmentation of more objects is possible but requires an increased ratio between the theta and gamma periods.

  16. Hebbian Plasticity Guides Maturation of Glutamate Receptor Fields In Vivo

    Directory of Open Access Journals (Sweden)

    Dmitrij Ljaschenko

    2013-05-01

    Full Text Available Synaptic plasticity shapes the development of functional neural circuits and provides a basis for cellular models of learning and memory. Hebbian plasticity describes an activity-dependent change in synaptic strength that is input-specific and depends on correlated pre- and postsynaptic activity. Although it is recognized that synaptic activity and synapse development are intimately linked, our mechanistic understanding of the coupling is far from complete. Using Channelrhodopsin-2 to evoke activity in vivo, we investigated synaptic plasticity at the glutamatergic Drosophila neuromuscular junction. Remarkably, correlated pre- and postsynaptic stimulation increased postsynaptic sensitivity by promoting synapse-specific recruitment of GluR-IIA-type glutamate receptor subunits into postsynaptic receptor fields. Conversely, GluR-IIA was rapidly removed from synapses whose activity failed to evoke substantial postsynaptic depolarization. Uniting these results with developmental GluR-IIA dynamics provides a comprehensive physiological concept of how Hebbian plasticity guides synaptic maturation and sparse transmitter release controls the stabilization of the molecular composition of individual synapses.

  17. Cocaine Promotes Coincidence Detection and Lowers Induction Threshold during Hebbian Associative Synaptic Potentiation in Prefrontal Cortex.

    Science.gov (United States)

    Ruan, Hongyu; Yao, Wei-Dong

    2017-01-25

    Addictive drugs usurp neural plasticity mechanisms that normally serve reward-related learning and memory, primarily by evoking changes in glutamatergic synaptic strength in the mesocorticolimbic dopamine circuitry. Here, we show that repeated cocaine exposure in vivo does not alter synaptic strength in the mouse prefrontal cortex during an early period of withdrawal, but instead modifies a Hebbian quantitative synaptic learning rule by broadening the temporal window and lowers the induction threshold for spike-timing-dependent LTP (t-LTP). After repeated, but not single, daily cocaine injections, t-LTP in layer V pyramidal neurons is induced at +30 ms, a normally ineffective timing interval for t-LTP induction in saline-exposed mice. This cocaine-induced, extended-timing t-LTP lasts for ∼1 week after terminating cocaine and is accompanied by an increased susceptibility to potentiation by fewer pre-post spike pairs, indicating a reduced t-LTP induction threshold. Basal synaptic strength and the maximal attainable t-LTP magnitude remain unchanged after cocaine exposure. We further show that the cocaine facilitation of t-LTP induction is caused by sensitized D1-cAMP/protein kinase A dopamine signaling in pyramidal neurons, which then pathologically recruits voltage-gated l-type Ca 2+ channels that synergize with GluN2A-containing NMDA receptors to drive t-LTP at extended timing. Our results illustrate a mechanism by which cocaine, acting on a key neuromodulation pathway, modifies the coincidence detection window during Hebbian plasticity to facilitate associative synaptic potentiation in prefrontal excitatory circuits. By modifying rules that govern activity-dependent synaptic plasticity, addictive drugs can derail the experience-driven neural circuit remodeling process important for executive control of reward and addiction. It is believed that addictive drugs often render an addict's brain reward system hypersensitive, leaving the individual more susceptible to

  18. Evidence from a rare case-study for Hebbian-like changes in structural connectivity induced by long-term deep brain stimulation

    Directory of Open Access Journals (Sweden)

    Tim J Van Hartevelt

    2015-06-01

    Full Text Available It is unclear whether Hebbian-like learning occurs at the level of long-range white matter connections in humans, i.e. where measurable changes in structural connectivity are correlated with changes in functional connectivity. However, the behavioral changes observed after deep brain stimulation (DBS suggest the existence of such Hebbian-like mechanisms occurring at the structural level with functional consequences. In this rare case study, we obtained the full network of white matter connections of one patient with Parkinson's disease before and after long-term DBS and combined it with a computational model of ongoing activity to investigate the effects of DBS-induced long-term structural changes. The results show that the long-term effects of DBS on resting-state functional connectivity is best obtained in the computational model by changing the structural weights from the subthalamic nucleus to the putamen and the thalamus in a Hebbian-like manner. Moreover, long-term DBS also significantly changed the structural connectivity towards normality in terms of model-based measures of segregation and integration of information processing, two key concepts of brain organization. This novel approach using computational models to model the effects of Hebbian-like changes in structural connectivity allowed us to causally identify the possible underlying neural mechanisms of long-term DBS using rare case study data. In time, this could help predict the efficacy of individual DBS targeting and identify novel DBS targets.

  19. An adaptive ES with a ranking based constraint handling strategy

    Directory of Open Access Journals (Sweden)

    Kusakci Ali Osman

    2014-01-01

    Full Text Available To solve a constrained optimization problem, equality constraints can be used to eliminate a problem variable. If it is not feasible, the relations imposed implicitly by the constraints can still be exploited. Most conventional constraint handling methods in Evolutionary Algorithms (EAs do not consider the correlations between problem variables imposed by the constraints. This paper relies on the idea that a proper search operator, which captures mentioned implicit correlations, can improve performance of evolutionary constrained optimization algorithms. To realize this, an Evolution Strategy (ES along with a simplified Covariance Matrix Adaptation (CMA based mutation operator is used with a ranking based constraint-handling method. The proposed algorithm is tested on 13 benchmark problems as well as on a real life design problem. The outperformance of the algorithm is significant when compared with conventional ES-based methods.

  20. Logic Learning in Hopfield Networks

    OpenAIRE

    Sathasivam, Saratha; Abdullah, Wan Ahmad Tajuddin Wan

    2008-01-01

    Synaptic weights for neurons in logic programming can be calculated either by using Hebbian learning or by Wan Abdullah's method. In other words, Hebbian learning for governing events corresponding to some respective program clauses is equivalent with learning using Wan Abdullah's method for the same respective program clauses. In this paper we will evaluate experimentally the equivalence between these two types of learning through computer simulations.

  1. Semiparametric Gaussian copula models : Geometry and efficient rank-based estimation

    NARCIS (Netherlands)

    Segers, J.; van den Akker, R.; Werker, B.J.M.

    2014-01-01

    We propose, for multivariate Gaussian copula models with unknown margins and structured correlation matrices, a rank-based, semiparametrically efficient estimator for the Euclidean copula parameter. This estimator is defined as a one-step update of a rank-based pilot estimator in the direction of

  2. A Computational Agent Model for Hebbian Learning of Social Interaction

    NARCIS (Netherlands)

    Treur, J.

    2011-01-01

    In social interaction between two persons usually a person displays understanding of the other person. This may involve both nonverbal and verbal elements, such as bodily expressing a similar emotion and verbally expressing beliefs about the other person. Such social interaction relates to an

  3. On-line learning through simple perceptron with a margin

    OpenAIRE

    Hara, Kazuyuki; Okada, Masato

    2003-01-01

    We analyze a learning method that uses a margin $\\kappa$ {\\it a la} Gardner for simple perceptron learning. This method corresponds to the perceptron learning when $\\kappa=0$, and to the Hebbian learning when $\\kappa \\to \\infty$. Nevertheless, we found that the generalization ability of the method was superior to that of the perceptron and the Hebbian methods at an early stage of learning. We analyzed the asymptotic property of the learning curve of this method through computer simulation and...

  4. Enhanced detection threshold for in vivo cortical stimulation produced by Hebbian conditioning

    Science.gov (United States)

    Rebesco, James M.; Miller, Lee E.

    2011-02-01

    Normal brain function requires constant adaptation, as an organism learns to associate important sensory stimuli with the appropriate motor actions. Neurological disorders may disrupt these learned associations and require the nervous system to reorganize itself. As a consequence, neural plasticity is a crucial component of normal brain function and a critical mechanism for recovery from injury. Associative, or Hebbian, pairing of pre- and post-synaptic activity has been shown to alter stimulus-evoked responses in vivo; however, to date, such protocols have not been shown to affect the animal's subsequent behavior. We paired stimulus trains separated by a brief time delay to two electrodes in rat sensorimotor cortex, which changed the statistical pattern of spikes during subsequent behavior. These changes were consistent with strengthened functional connections from the leading electrode to the lagging electrode. We then trained rats to respond to a microstimulation cue, and repeated the paradigm using the cue electrode as the leading electrode. This pairing lowered the rat's ICMS-detection threshold, with the same dependence on intra-electrode time lag that we found for the functional connectivity changes. The timecourse of the behavioral effects was very similar to that of the connectivity changes. We propose that the behavioral changes were a consequence of strengthened functional connections from the cue electrode to other regions of sensorimotor cortex. Such paradigms might be used to augment recovery from a stroke, or to promote adaptation in a bidirectional brain-machine interface.

  5. Long-Term Homeostatic Properties Complementary to Hebbian Rules in CuPc-Based Multifunctional Memristor

    Science.gov (United States)

    Wang, Laiyuan; Wang, Zhiyong; Lin, Jinyi; Yang, Jie; Xie, Linghai; Yi, Mingdong; Li, Wen; Ling, Haifeng; Ou, Changjin; Huang, Wei

    2016-10-01

    Most simulations of neuroplasticity in memristors, which are potentially used to develop artificial synapses, are confined to the basic biological Hebbian rules. However, the simplex rules potentially can induce excessive excitation/inhibition, even collapse of neural activities, because they neglect the properties of long-term homeostasis involved in the frameworks of realistic neural networks. Here, we develop organic CuPc-based memristors of which excitatory and inhibitory conductivities can implement both Hebbian rules and homeostatic plasticity, complementary to Hebbian patterns and conductive to the long-term homeostasis. In another adaptive situation for homeostasis, in thicker samples, the overall excitement under periodic moderate stimuli tends to decrease and be recovered under intense inputs. Interestingly, the prototypes can be equipped with bio-inspired habituation and sensitization functions outperforming the conventional simplified algorithms. They mutually regulate each other to obtain the homeostasis. Therefore, we develop a novel versatile memristor with advanced synaptic homeostasis for comprehensive neural functions.

  6. Robotic Assistance for Training Finger Movement Using a Hebbian Model: A Randomized Controlled Trial.

    Science.gov (United States)

    Rowe, Justin B; Chan, Vicky; Ingemanson, Morgan L; Cramer, Steven C; Wolbrecht, Eric T; Reinkensmeyer, David J

    2017-08-01

    Robots that physically assist movement are increasingly used in rehabilitation therapy after stroke, yet some studies suggest robotic assistance discourages effort and reduces motor learning. To determine the therapeutic effects of high and low levels of robotic assistance during finger training. We designed a protocol that varied the amount of robotic assistance while controlling the number, amplitude, and exerted effort of training movements. Participants (n = 30) with a chronic stroke and moderate hemiparesis (average Box and Blocks Test 32 ± 18 and upper extremity Fugl-Meyer score 46 ± 12) actively moved their index and middle fingers to targets to play a musical game similar to GuitarHero 3 h/wk for 3 weeks. The participants were randomized to receive high assistance (causing 82% success at hitting targets) or low assistance (55% success). Participants performed ~8000 movements during 9 training sessions. Both groups improved significantly at the 1-month follow-up on functional and impairment-based motor outcomes, on depression scores, and on self-efficacy of hand function, with no difference between groups in the primary endpoint (change in Box and Blocks). High assistance boosted motivation, as well as secondary motor outcomes (Fugl-Meyer and Lateral Pinch Strength)-particularly for individuals with more severe finger motor deficits. Individuals with impaired finger proprioception at baseline benefited less from the training. Robot-assisted training can promote key psychological outcomes known to modulate motor learning and retention. Furthermore, the therapeutic effectiveness of robotic assistance appears to derive at least in part from proprioceptive stimulation, consistent with a Hebbian plasticity model.

  7. Rank-based Tests of the Cointegrating Rank in Semiparametric Error Correction Models

    NARCIS (Netherlands)

    Hallin, M.; van den Akker, R.; Werker, B.J.M.

    2012-01-01

    Abstract: This paper introduces rank-based tests for the cointegrating rank in an Error Correction Model with i.i.d. elliptical innovations. The tests are asymptotically distribution-free, and their validity does not depend on the actual distribution of the innovations. This result holds despite the

  8. Accelerating parameter identification of proton exchange membrane fuel cell model with ranking-based differential evolution

    International Nuclear Information System (INIS)

    Gong, Wenyin; Cai, Zhihua

    2013-01-01

    Parameter identification of PEM (proton exchange membrane) fuel cell model is a very active area of research. Generally, it can be treated as a numerical optimization problem with complex nonlinear and multi-variable features. DE (differential evolution), which has been successfully used in various fields, is a simple yet efficient evolutionary algorithm for global numerical optimization. In this paper, with the objective of accelerating the process of parameter identification of PEM fuel cell models and reducing the necessary computational efforts, we firstly present a generic and simple ranking-based mutation operator for the DE algorithm. Then, the ranking-based mutation operator is incorporated into five highly-competitive DE variants to solve the PEM fuel cell model parameter identification problems. The main contributions of this work are the proposed ranking-based DE variants and their application to the parameter identification problems of PEM fuel cell models. Experiments have been conducted by using both the simulated voltage–current data and the data obtained from the literature to validate the performance of our approach. The results indicate that the ranking-based DE methods provide better results with respect to the solution quality, the convergence rate, and the success rate compared with their corresponding original DE methods. In addition, the voltage–current characteristics obtained by our approach are in good agreement with the original voltage–current curves in all cases. - Highlights: • A simple and generic ranking-based mutation operator is presented in this paper. • Several DE (differential evolution) variants are used to solve the parameter identification of PEMFC (proton exchange membrane fuel cells) model. • Results show that our method accelerates the process of parameter identification. • The V–I characteristics are in very good agreement with experimental data

  9. Ranking Based Locality Sensitive Hashing Enabled Cancelable Biometrics: Index-of-Max Hashing

    OpenAIRE

    Jin, Zhe; Lai, Yen-Lung; Hwang, Jung-Yeon; Kim, Soohyung; Teoh, Andrew Beng Jin

    2017-01-01

    In this paper, we propose a ranking based locality sensitive hashing inspired two-factor cancelable biometrics, dubbed "Index-of-Max" (IoM) hashing for biometric template protection. With externally generated random parameters, IoM hashing transforms a real-valued biometric feature vector into discrete index (max ranked) hashed code. We demonstrate two realizations from IoM hashing notion, namely Gaussian Random Projection based and Uniformly Random Permutation based hashing schemes. The disc...

  10. Scheduling for Multiuser MIMO Downlink Channels with Ranking-Based Feedback

    Science.gov (United States)

    Kountouris, Marios; Sälzer, Thomas; Gesbert, David

    2008-12-01

    We consider a multi-antenna broadcast channel with more single-antenna receivers than transmit antennas and partial channel state information at the transmitter (CSIT). We propose a novel type of CSIT representation for the purpose of user selection, coined as ranking-based feedback. Each user calculates and feeds back the rank, an integer between 1 and W + 1, of its instantaneous channel quality information (CQI) among a set of W past CQI measurements. Apart from reducing significantly the required feedback load, ranking-based feedback enables the transmitter to select users that are on the highest peak (quantile) with respect to their own channel distribution, independently of the distribution of other users. It can also be shown that this feedback metric can restore temporal fairness in heterogeneous networks, in which users' channels are not identically distributed and mobile terminals experience different average signal-to-noise ratio (SNR). The performance of a system that performs user selection using ranking-based CSIT in the context of random opportunistic beamforming is analyzed, and we provide design guidelines on the number of required past CSIT samples and the impact of finite W on average throughput. Simulation results show that feedback reduction of order of 40-50% can be achieved with negligible decrease in system throughput.

  11. On-line learning through simple perceptron learning with a margin.

    Science.gov (United States)

    Hara, Kazuyuki; Okada, Masato

    2004-03-01

    We analyze a learning method that uses a margin kappa a la Gardner for simple perceptron learning. This method corresponds to the perceptron learning when kappa = 0 and to the Hebbian learning when kappa = infinity. Nevertheless, we found that the generalization ability of the method was superior to that of the perceptron and the Hebbian methods at an early stage of learning. We analyzed the asymptotic property of the learning curve of this method through computer simulation and found that it was the same as for perceptron learning. We also investigated an adaptive margin control method.

  12. Pareto-Ranking Based Quantum-Behaved Particle Swarm Optimization for Multiobjective Optimization

    Directory of Open Access Journals (Sweden)

    Na Tian

    2015-01-01

    Full Text Available A study on pareto-ranking based quantum-behaved particle swarm optimization (QPSO for multiobjective optimization problems is presented in this paper. During the iteration, an external repository is maintained to remember the nondominated solutions, from which the global best position is chosen. The comparison between different elitist selection strategies (preference order, sigma value, and random selection is performed on four benchmark functions and two metrics. The results demonstrate that QPSO with preference order has comparative performance with sigma value according to different number of objectives. Finally, QPSO with sigma value is applied to solve multiobjective flexible job-shop scheduling problems.

  13. Finding differentially expressed genes in high dimensional data: Rank based test statistic via a distance measure.

    Science.gov (United States)

    Mathur, Sunil; Sadana, Ajit

    2015-12-01

    We present a rank-based test statistic for the identification of differentially expressed genes using a distance measure. The proposed test statistic is highly robust against extreme values and does not assume the distribution of parent population. Simulation studies show that the proposed test is more powerful than some of the commonly used methods, such as paired t-test, Wilcoxon signed rank test, and significance analysis of microarray (SAM) under certain non-normal distributions. The asymptotic distribution of the test statistic, and the p-value function are discussed. The application of proposed method is shown using a real-life data set. © The Author(s) 2011.

  14. SQERTSS: Dynamic rank based throttling of transition probabilities in kinetic Monte Carlo simulations

    International Nuclear Information System (INIS)

    Danielson, Thomas; Sutton, Jonathan E.; Hin, Céline; Virginia Polytechnic Institute and State University; Savara, Aditya

    2017-01-01

    Lattice based Kinetic Monte Carlo (KMC) simulations offer a powerful simulation technique for investigating large reaction networks while retaining spatial configuration information, unlike ordinary differential equations. However, large chemical reaction networks can contain reaction processes with rates spanning multiple orders of magnitude. This can lead to the problem of “KMC stiffness” (similar to stiffness in differential equations), where the computational expense has the potential to be overwhelmed by very short time-steps during KMC simulations, with the simulation spending an inordinate amount of KMC steps / cpu-time simulating fast frivolous processes (FFPs) without progressing the system (reaction network). In order to achieve simulation times that are experimentally relevant or desired for predictions, a dynamic throttling algorithm involving separation of the processes into speed-ranks based on event frequencies has been designed and implemented with the intent of decreasing the probability of FFP events, and increasing the probability of slow process events -- allowing rate limiting events to become more likely to be observed in KMC simulations. This Staggered Quasi-Equilibrium Rank-based Throttling for Steady-state (SQERTSS) algorithm designed for use in achieving and simulating steady-state conditions in KMC simulations. Lastly, as shown in this work, the SQERTSS algorithm also works for transient conditions: the correct configuration space and final state will still be achieved if the required assumptions are not violated, with the caveat that the sizes of the time-steps may be distorted during the transient period.

  15. RANWAR: rank-based weighted association rule mining from gene expression and methylation data.

    Science.gov (United States)

    Mallik, Saurav; Mukhopadhyay, Anirban; Maulik, Ujjwal

    2015-01-01

    Ranking of association rules is currently an interesting topic in data mining and bioinformatics. The huge number of evolved rules of items (or, genes) by association rule mining (ARM) algorithms makes confusion to the decision maker. In this article, we propose a weighted rule-mining technique (say, RANWAR or rank-based weighted association rule-mining) to rank the rules using two novel rule-interestingness measures, viz., rank-based weighted condensed support (wcs) and weighted condensed confidence (wcc) measures to bypass the problem. These measures are basically depended on the rank of items (genes). Using the rank, we assign weight to each item. RANWAR generates much less number of frequent itemsets than the state-of-the-art association rule mining algorithms. Thus, it saves time of execution of the algorithm. We run RANWAR on gene expression and methylation datasets. The genes of the top rules are biologically validated by Gene Ontologies (GOs) and KEGG pathway analyses. Many top ranked rules extracted from RANWAR that hold poor ranks in traditional Apriori, are highly biologically significant to the related diseases. Finally, the top rules evolved from RANWAR, that are not in Apriori, are reported.

  16. Detecting determinism with improved sensitivity in time series: rank-based nonlinear predictability score.

    Science.gov (United States)

    Naro, Daniel; Rummel, Christian; Schindler, Kaspar; Andrzejak, Ralph G

    2014-09-01

    The rank-based nonlinear predictability score was recently introduced as a test for determinism in point processes. We here adapt this measure to time series sampled from time-continuous flows. We use noisy Lorenz signals to compare this approach against a classical amplitude-based nonlinear prediction error. Both measures show an almost identical robustness against Gaussian white noise. In contrast, when the amplitude distribution of the noise has a narrower central peak and heavier tails than the normal distribution, the rank-based nonlinear predictability score outperforms the amplitude-based nonlinear prediction error. For this type of noise, the nonlinear predictability score has a higher sensitivity for deterministic structure in noisy signals. It also yields a higher statistical power in a surrogate test of the null hypothesis of linear stochastic correlated signals. We show the high relevance of this improved performance in an application to electroencephalographic (EEG) recordings from epilepsy patients. Here the nonlinear predictability score again appears of higher sensitivity to nonrandomness. Importantly, it yields an improved contrast between signals recorded from brain areas where the first ictal EEG signal changes were detected (focal EEG signals) versus signals recorded from brain areas that were not involved at seizure onset (nonfocal EEG signals).

  17. Social norms and rank-based nudging: Changing willingness to pay for healthy food.

    Science.gov (United States)

    Aldrovandi, Silvio; Brown, Gordon D A; Wood, Alex M

    2015-09-01

    People's evaluations in the domain of healthy eating are at least partly determined by the choice context. We systematically test reference level and rank-based models of relative comparisons against each other and explore their application to social norms nudging, an intervention that aims at influencing consumers' behavior by addressing their inaccurate beliefs about their consumption relative to the consumption of others. Study 1 finds that the rank of a product or behavior among others in the immediate comparison context, rather than its objective attributes, influences its evaluation. Study 2 finds that when a comparator is presented in isolation the same rank-based process occurs based on information retrieved from memory. Study 3 finds that telling people how their consumption ranks within a normative comparison sample increases willingness to pay for a healthy food by over 30% relative to the normal social norms intervention that tells them how they compare to the average. We conclude that social norms interventions should present rank information (e.g., "you are in the most unhealthy 10% of eaters") rather than information relative to the average (e.g., "you consume 500 calories more than the average person"). (c) 2015 APA, all rights reserved).

  18. The Use of Hebbian Cell Assemblies for Nonlinear Computation

    DEFF Research Database (Denmark)

    Tetzlaff, Christian; Dasgupta, Sakyasingha; Kulvicius, Tomas

    2015-01-01

    When learning a complex task our nervous system self-organizes large groups of neurons into coherent dynamic activity patterns. During this, a network with multiple, simultaneously active, and computationally powerful cell assemblies is created. How such ordered structures are formed while preser...... computing complex non-linear transforms and - for execution - must cooperate with each other without interference. This mechanism, thus, permits the self-organization of computationally powerful sub-structures in dynamic networks for behavior control....

  19. Data depth and rank-based tests for covariance and spectral density matrices

    KAUST Repository

    Chau, Joris

    2017-06-26

    In multivariate time series analysis, objects of primary interest to study cross-dependences in the time series are the autocovariance or spectral density matrices. Non-degenerate covariance and spectral density matrices are necessarily Hermitian and positive definite, and our primary goal is to develop new methods to analyze samples of such matrices. The main contribution of this paper is the generalization of the concept of statistical data depth for collections of covariance or spectral density matrices by exploiting the geometric properties of the space of Hermitian positive definite matrices as a Riemannian manifold. This allows one to naturally characterize most central or outlying matrices, but also provides a practical framework for rank-based hypothesis testing in the context of samples of covariance or spectral density matrices. First, the desired properties of a data depth function acting on the space of Hermitian positive definite matrices are presented. Second, we propose two computationally efficient pointwise and integrated data depth functions that satisfy each of these requirements. Several applications of the developed methodology are illustrated by the analysis of collections of spectral matrices in multivariate brain signal time series datasets.

  20. Data depth and rank-based tests for covariance and spectral density matrices

    KAUST Repository

    Chau, Joris; Ombao, Hernando; Sachs, Rainer von

    2017-01-01

    In multivariate time series analysis, objects of primary interest to study cross-dependences in the time series are the autocovariance or spectral density matrices. Non-degenerate covariance and spectral density matrices are necessarily Hermitian and positive definite, and our primary goal is to develop new methods to analyze samples of such matrices. The main contribution of this paper is the generalization of the concept of statistical data depth for collections of covariance or spectral density matrices by exploiting the geometric properties of the space of Hermitian positive definite matrices as a Riemannian manifold. This allows one to naturally characterize most central or outlying matrices, but also provides a practical framework for rank-based hypothesis testing in the context of samples of covariance or spectral density matrices. First, the desired properties of a data depth function acting on the space of Hermitian positive definite matrices are presented. Second, we propose two computationally efficient pointwise and integrated data depth functions that satisfy each of these requirements. Several applications of the developed methodology are illustrated by the analysis of collections of spectral matrices in multivariate brain signal time series datasets.

  1. Fuzzy ranking based non-dominated sorting genetic algorithm-II for network overload alleviation

    Directory of Open Access Journals (Sweden)

    Pandiarajan K.

    2014-09-01

    Full Text Available This paper presents an effective method of network overload management in power systems. The three competing objectives 1 generation cost 2 transmission line overload and 3 real power loss are optimized to provide pareto-optimal solutions. A fuzzy ranking based non-dominated sorting genetic algorithm-II (NSGA-II is used to solve this complex nonlinear optimization problem. The minimization of competing objectives is done by generation rescheduling. Fuzzy ranking method is employed to extract the best compromise solution out of the available non-dominated solutions depending upon its highest rank. N-1 contingency analysis is carried out to identify the most severe lines and those lines are selected for outage. The effectiveness of the proposed approach is demonstrated for different contingency cases in IEEE 30 and IEEE 118 bus systems with smooth cost functions and their results are compared with other single objective evolutionary algorithms like Particle swarm optimization (PSO and Differential evolution (DE. Simulation results show the effectiveness of the proposed approach to generate well distributed pareto-optimal non-dominated solutions of multi-objective problem

  2. A rank-based sequence aligner with applications in phylogenetic analysis.

    Directory of Open Access Journals (Sweden)

    Liviu P Dinu

    Full Text Available Recent tools for aligning short DNA reads have been designed to optimize the trade-off between correctness and speed. This paper introduces a method for assigning a set of short DNA reads to a reference genome, under Local Rank Distance (LRD. The rank-based aligner proposed in this work aims to improve correctness over speed. However, some indexing strategies to speed up the aligner are also investigated. The LRD aligner is improved in terms of speed by storing [Formula: see text]-mer positions in a hash table for each read. Another improvement, that produces an approximate LRD aligner, is to consider only the positions in the reference that are likely to represent a good positional match of the read. The proposed aligner is evaluated and compared to other state of the art alignment tools in several experiments. A set of experiments are conducted to determine the precision and the recall of the proposed aligner, in the presence of contaminated reads. In another set of experiments, the proposed aligner is used to find the order, the family, or the species of a new (or unknown organism, given only a set of short Next-Generation Sequencing DNA reads. The empirical results show that the aligner proposed in this work is highly accurate from a biological point of view. Compared to the other evaluated tools, the LRD aligner has the important advantage of being very accurate even for a very low base coverage. Thus, the LRD aligner can be considered as a good alternative to standard alignment tools, especially when the accuracy of the aligner is of high importance. Source code and UNIX binaries of the aligner are freely available for future development and use at http://lrd.herokuapp.com/aligners. The software is implemented in C++ and Java, being supported on UNIX and MS Windows.

  3. An Efficient VLSI Architecture for Multi-Channel Spike Sorting Using a Generalized Hebbian Algorithm

    Science.gov (United States)

    Chen, Ying-Lun; Hwang, Wen-Jyi; Ke, Chi-En

    2015-01-01

    A novel VLSI architecture for multi-channel online spike sorting is presented in this paper. In the architecture, the spike detection is based on nonlinear energy operator (NEO), and the feature extraction is carried out by the generalized Hebbian algorithm (GHA). To lower the power consumption and area costs of the circuits, all of the channels share the same core for spike detection and feature extraction operations. Each channel has dedicated buffers for storing the detected spikes and the principal components of that channel. The proposed circuit also contains a clock gating system supplying the clock to only the buffers of channels currently using the computation core to further reduce the power consumption. The architecture has been implemented by an application-specific integrated circuit (ASIC) with 90-nm technology. Comparisons to the existing works show that the proposed architecture has lower power consumption and hardware area costs for real-time multi-channel spike detection and feature extraction. PMID:26287193

  4. An Efficient VLSI Architecture for Multi-Channel Spike Sorting Using a Generalized Hebbian Algorithm.

    Science.gov (United States)

    Chen, Ying-Lun; Hwang, Wen-Jyi; Ke, Chi-En

    2015-08-13

    A novel VLSI architecture for multi-channel online spike sorting is presented in this paper. In the architecture, the spike detection is based on nonlinear energy operator (NEO), and the feature extraction is carried out by the generalized Hebbian algorithm (GHA). To lower the power consumption and area costs of the circuits, all of the channels share the same core for spike detection and feature extraction operations. Each channel has dedicated buffers for storing the detected spikes and the principal components of that channel. The proposed circuit also contains a clock gating system supplying the clock to only the buffers of channels currently using the computation core to further reduce the power consumption. The architecture has been implemented by an application-specific integrated circuit (ASIC) with 90-nm technology. Comparisons to the existing works show that the proposed architecture has lower power consumption and hardware area costs for real-time multi-channel spike detection and feature extraction.

  5. A rank based social norms model of how people judge their levels of drunkenness whilst intoxicated

    Directory of Open Access Journals (Sweden)

    Simon C. Moore

    2016-09-01

    Full Text Available Abstract Background A rank based social norms model predicts that drinkers’ judgements about their drinking will be based on the rank of their breath alcohol level amongst that of others in the immediate environment, rather than their actual breath alcohol level, with lower relative rank associated with greater feelings of safety. This study tested this hypothesis and examined how people judge their levels of drunkenness and the health consequences of their drinking whilst they are intoxicated in social drinking environments. Methods Breath alcohol testing of 1,862 people (mean age = 26.96 years; 61.86 % male in drinking environments. A subset (N = 400 also answered four questions asking about their perceptions of their drunkenness and the health consequences of their drinking (plus background measures. Results Perceptions of drunkenness and the health consequences of drinking were regressed on: (a breath alcohol level, (b the rank of the breath alcohol level amongst that of others in the same environment, and (c covariates. Only rank of breath alcohol level predicted perceptions: How drunk they felt (b 3.78, 95 % CI 1.69 5.87, how extreme they regarded their drinking that night (b 3.7, 95 % CI 1.3 6.20, how at risk their long-term health was due to their current level of drinking (b 4.1, 95 % CI 0.2 8.0 and how likely they felt they would experience liver cirrhosis (b 4.8. 95 % CI 0.7 8.8. People were more influenced by more sober others than by more drunk others. Conclusion Whilst intoxicated and in drinking environments, people base judgements regarding their drinking on how their level of intoxication ranks relative to that of others of the same gender around them, not on their actual levels of intoxication. Thus, when in the company of others who are intoxicated, drinkers were found to be more likely to underestimate their own level of drinking, drunkenness and associated risks. The implications of these results, for example

  6. Efficient File Sharing by Multicast - P2P Protocol Using Network Coding and Rank Based Peer Selection

    Science.gov (United States)

    Stoenescu, Tudor M.; Woo, Simon S.

    2009-01-01

    In this work, we consider information dissemination and sharing in a distributed peer-to-peer (P2P highly dynamic communication network. In particular, we explore a network coding technique for transmission and a rank based peer selection method for network formation. The combined approach has been shown to improve information sharing and delivery to all users when considering the challenges imposed by the space network environments.

  7. An Efficient VLSI Architecture for Multi-Channel Spike Sorting Using a Generalized Hebbian Algorithm

    Directory of Open Access Journals (Sweden)

    Ying-Lun Chen

    2015-08-01

    Full Text Available A novel VLSI architecture for multi-channel online spike sorting is presented in this paper. In the architecture, the spike detection is based on nonlinear energy operator (NEO, and the feature extraction is carried out by the generalized Hebbian algorithm (GHA. To lower the power consumption and area costs of the circuits, all of the channels share the same core for spike detection and feature extraction operations. Each channel has dedicated buffers for storing the detected spikes and the principal components of that channel. The proposed circuit also contains a clock gating system supplying the clock to only the buffers of channels currently using the computation core to further reduce the power consumption. The architecture has been implemented by an application-specific integrated circuit (ASIC with 90-nm technology. Comparisons to the existing works show that the proposed architecture has lower power consumption and hardware area costs for real-time multi-channel spike detection and feature extraction.

  8. Hebbian plasticity realigns grid cell activity with external sensory cues in continuous attractor models

    Directory of Open Access Journals (Sweden)

    Marcello eMulas

    2016-02-01

    Full Text Available After the discovery of grid cells, which are an essential component to understand how the mammalian brain encodes spatial information, three main classes of computational models were proposed in order to explain their working principles. Amongst them, the one based on continuous attractor networks (CAN, is promising in terms of biological plausibility and suitable for robotic applications. However, in its current formulation, it is unable to reproduce important electrophysiological findings and cannot be used to perform path integration for long periods of time. In fact, in absence of an appropriate resetting mechanism, the accumulation of errors overtime due to the noise intrinsic in velocity estimation and neural computation prevents CAN models to reproduce stable spatial grid patterns. In this paper, we propose an extension of the CAN model using Hebbian plasticity to anchor grid cell activity to environmental landmarks. To validate our approach we used as input to the neural simulations both artificial data and real data recorded from a robotic setup. The additional neural mechanism can not only anchor grid patterns to external sensory cues but also recall grid patterns generated in previously explored environments. These results might be instrumental for next generation bio-inspired robotic navigation algorithms that take advantage of neural computation in order to cope with complex and dynamic environments.

  9. A Cognitive and Neural Model for Adaptive Emotion Reading by Mirroring Preparation States and Hebbian Learning

    NARCIS (Netherlands)

    Bosse, T.; Memon, Z.A.; Treur, J.

    2012-01-01

    Two types of modelling approaches exist to reading an observed person's emotions: with or without making use of the observing person's own emotions. This paper focuses on an integrated approach that combines both types of approaches in an adaptive manner. The proposed models were inspired by recent

  10. Cooperation-Induced Topological Complexity: A Promising Road to Fault Tolerance and Hebbian Learning

    Science.gov (United States)

    2012-03-16

    Information Science Directorate, United States Army Research Office, Durham, NC, USA 4 Istituto di Fisiologia Clinica del Consiglio Nazionale delle...Vadim Uritsky, Catholic University of America at NASA Goddard Space Flight Center, USA *Correspondence: Paolo Allegrini , Istituto di Fisiologia Clinica

  11. An improved rank based disease prediction using web navigation patterns on bio-medical databases

    Directory of Open Access Journals (Sweden)

    P. Dhanalakshmi

    2017-12-01

    Full Text Available Applying machine learning techniques to on-line biomedical databases is a challenging task, as this data is collected from large number of sources and it is multi-dimensional. Also retrieval of relevant document from large repository such as gene document takes more processing time and an increased false positive rate. Generally, the extraction of biomedical document is based on the stream of prior observations of gene parameters taken at different time periods. Traditional web usage models such as Markov, Bayesian and Clustering models are sensitive to analyze the user navigation patterns and session identification in online biomedical database. Moreover, most of the document ranking models on biomedical database are sensitive to sparsity and outliers. In this paper, a novel user recommendation system was implemented to predict the top ranked biomedical documents using the disease type, gene entities and user navigation patterns. In this recommendation system, dynamic session identification, dynamic user identification and document ranking techniques were used to extract the highly relevant disease documents on the online PubMed repository. To verify the performance of the proposed model, the true positive rate and runtime of the model was compared with that of traditional static models such as Bayesian and Fuzzy rank. Experimental results show that the performance of the proposed ranking model is better than the traditional models.

  12. Rank-based characterization of pollen assemblages collected by honey bees using a multi-locus metabarcoding approach.

    Science.gov (United States)

    Richardson, Rodney T; Lin, Chia-Hua; Quijia, Juan O; Riusech, Natalia S; Goodell, Karen; Johnson, Reed M

    2015-11-01

    Difficulties inherent in microscopic pollen identification have resulted in limited implementation for large-scale studies. Metabarcoding, a relatively novel approach, could make pollen analysis less onerous; however, improved understanding of the quantitative capacity of various plant metabarcode regions and primer sets is needed to ensure that such applications are accurate and precise. We applied metabarcoding, targeting the ITS2, matK, and rbcL loci, to characterize six samples of pollen collected by honey bees, Apis mellifera. Additionally, samples were analyzed by light microscopy. We found significant rank-based associations between the relative abundance of pollen types within our samples as inferred by the two methods. Our findings suggest metabarcoding data from plastid loci, as opposed to the ribosomal locus, are more reliable for quantitative characterization of pollen assemblages. Furthermore, multilocus metabarcoding of pollen may be more reliable than single-locus analyses, underscoring the need for discovering novel barcodes and barcode combinations optimized for molecular palynology.

  13. Rank-based characterization of pollen assemblages collected by honey bees using a multi-locus metabarcoding approach1

    Science.gov (United States)

    Richardson, Rodney T.; Lin, Chia-Hua; Quijia, Juan O.; Riusech, Natalia S.; Goodell, Karen; Johnson, Reed M.

    2015-01-01

    Premise of the study: Difficulties inherent in microscopic pollen identification have resulted in limited implementation for large-scale studies. Metabarcoding, a relatively novel approach, could make pollen analysis less onerous; however, improved understanding of the quantitative capacity of various plant metabarcode regions and primer sets is needed to ensure that such applications are accurate and precise. Methods and Results: We applied metabarcoding, targeting the ITS2, matK, and rbcL loci, to characterize six samples of pollen collected by honey bees, Apis mellifera. Additionally, samples were analyzed by light microscopy. We found significant rank-based associations between the relative abundance of pollen types within our samples as inferred by the two methods. Conclusions: Our findings suggest metabarcoding data from plastid loci, as opposed to the ribosomal locus, are more reliable for quantitative characterization of pollen assemblages. Furthermore, multilocus metabarcoding of pollen may be more reliable than single-locus analyses, underscoring the need for discovering novel barcodes and barcode combinations optimized for molecular palynology. PMID:26649264

  14. An Efficient Normalized Rank Based SVM for Room Level Indoor WiFi Localization with Diverse Devices

    Directory of Open Access Journals (Sweden)

    Yasmine Rezgui

    2017-01-01

    Full Text Available This paper proposes an efficient and effective WiFi fingerprinting-based indoor localization algorithm, which uses the Received Signal Strength Indicator (RSSI of WiFi signals. In practical harsh indoor environments, RSSI variation and hardware variance can significantly degrade the performance of fingerprinting-based localization methods. To address the problem of hardware variance and signal fluctuation in WiFi fingerprinting-based localization, we propose a novel normalized rank based Support Vector Machine classifier (NR-SVM. Moving from RSSI value based analysis to the normalized rank transformation based analysis, the principal features are prioritized and the dimensionalities of signature vectors are taken into account. The proposed method has been tested using sixteen different devices in a shopping mall with 88 shops. The experimental results demonstrate its robustness with no less than 98.75% correct estimation in 93.75% of the tested cases and 100% correct rate in 56.25% of cases. In the experiments, the new method shows better performance over the KNN, Naïve Bayes, Random Forest, and Neural Network algorithms. Furthermore, we have compared the proposed approach with three popular calibration-free transformation based methods, including difference method (DIFF, Signal Strength Difference (SSD, and the Hyperbolic Location Fingerprinting (HLF based SVM. The results show that the NR-SVM outperforms these popular methods.

  15. PRIMAL: Page Rank-Based Indoor Mapping and Localization Using Gene-Sequenced Unlabeled WLAN Received Signal Strength

    Directory of Open Access Journals (Sweden)

    Mu Zhou

    2015-09-01

    Full Text Available Due to the wide deployment of wireless local area networks (WLAN, received signal strength (RSS-based indoor WLAN localization has attracted considerable attention in both academia and industry. In this paper, we propose a novel page rank-based indoor mapping and localization (PRIMAL by using the gene-sequenced unlabeled WLAN RSS for simultaneous localization and mapping (SLAM. Specifically, first of all, based on the observation of the motion patterns of the people in the target environment, we use the Allen logic to construct the mobility graph to characterize the connectivity among different areas of interest. Second, the concept of gene sequencing is utilized to assemble the sporadically-collected RSS sequences into a signal graph based on the transition relations among different RSS sequences. Third, we apply the graph drawing approach to exhibit both the mobility graph and signal graph in a more readable manner. Finally, the page rank (PR algorithm is proposed to construct the mapping from the signal graph into the mobility graph. The experimental results show that the proposed approach achieves satisfactory localization accuracy and meanwhile avoids the intensive time and labor cost involved in the conventional location fingerprinting-based indoor WLAN localization.

  16. PRIMAL: Page Rank-Based Indoor Mapping and Localization Using Gene-Sequenced Unlabeled WLAN Received Signal Strength.

    Science.gov (United States)

    Zhou, Mu; Zhang, Qiao; Xu, Kunjie; Tian, Zengshan; Wang, Yanmeng; He, Wei

    2015-09-25

    Due to the wide deployment of wireless local area networks (WLAN), received signal strength (RSS)-based indoor WLAN localization has attracted considerable attention in both academia and industry. In this paper, we propose a novel page rank-based indoor mapping and localization (PRIMAL) by using the gene-sequenced unlabeled WLAN RSS for simultaneous localization and mapping (SLAM). Specifically, first of all, based on the observation of the motion patterns of the people in the target environment, we use the Allen logic to construct the mobility graph to characterize the connectivity among different areas of interest. Second, the concept of gene sequencing is utilized to assemble the sporadically-collected RSS sequences into a signal graph based on the transition relations among different RSS sequences. Third, we apply the graph drawing approach to exhibit both the mobility graph and signal graph in a more readable manner. Finally, the page rank (PR) algorithm is proposed to construct the mapping from the signal graph into the mobility graph. The experimental results show that the proposed approach achieves satisfactory localization accuracy and meanwhile avoids the intensive time and labor cost involved in the conventional location fingerprinting-based indoor WLAN localization.

  17. A rank-based approach for correcting systematic biases in spatial disaggregation of coarse-scale climate simulations

    Science.gov (United States)

    Nahar, Jannatun; Johnson, Fiona; Sharma, Ashish

    2017-07-01

    Use of General Circulation Model (GCM) precipitation and evapotranspiration sequences for hydrologic modelling can result in unrealistic simulations due to the coarse scales at which GCMs operate and the systematic biases they contain. The Bias Correction Spatial Disaggregation (BCSD) method is a popular statistical downscaling and bias correction method developed to address this issue. The advantage of BCSD is its ability to reduce biases in the distribution of precipitation totals at the GCM scale and then introduce more realistic variability at finer scales than simpler spatial interpolation schemes. Although BCSD corrects biases at the GCM scale before disaggregation; at finer spatial scales biases are re-introduced by the assumptions made in the spatial disaggregation process. Our study focuses on this limitation of BCSD and proposes a rank-based approach that aims to reduce the spatial disaggregation bias especially for both low and high precipitation extremes. BCSD requires the specification of a multiplicative bias correction anomaly field that represents the ratio of the fine scale precipitation to the disaggregated precipitation. It is shown that there is significant temporal variation in the anomalies, which is masked when a mean anomaly field is used. This can be improved by modelling the anomalies in rank-space. Results from the application of the rank-BCSD procedure improve the match between the distributions of observed and downscaled precipitation at the fine scale compared to the original BCSD approach. Further improvements in the distribution are identified when a scaling correction to preserve mass in the disaggregation process is implemented. An assessment of the approach using a single GCM over Australia shows clear advantages especially in the simulation of particularly low and high downscaled precipitation amounts.

  18. Circuit mechanisms of sensorimotor learning

    Science.gov (United States)

    Makino, Hiroshi; Hwang, Eun Jung; Hedrick, Nathan G.; Komiyama, Takaki

    2016-01-01

    SUMMARY The relationship between the brain and the environment is flexible, forming the foundation for our ability to learn. Here we review the current state of our understanding of the modifications in the sensorimotor pathway related to sensorimotor learning. We divide the process in three hierarchical levels with distinct goals: 1) sensory perceptual learning, 2) sensorimotor associative learning, and 3) motor skill learning. Perceptual learning optimizes the representations of important sensory stimuli. Associative learning and the initial phase of motor skill learning are ensured by feedback-based mechanisms that permit trial-and-error learning. The later phase of motor skill learning may primarily involve feedback-independent mechanisms operating under the classic Hebbian rule. With these changes under distinct constraints and mechanisms, sensorimotor learning establishes dedicated circuitry for the reproduction of stereotyped neural activity patterns and behavior. PMID:27883902

  19. Learning-induced pattern classification in a chaotic neural network

    International Nuclear Information System (INIS)

    Li, Yang; Zhu, Ping; Xie, Xiaoping; He, Guoguang; Aihara, Kazuyuki

    2012-01-01

    In this Letter, we propose a Hebbian learning rule with passive forgetting (HLRPF) for use in a chaotic neural network (CNN). We then define the indices based on the Euclidean distance to investigate the evolution of the weights in a simplified way. Numerical simulations demonstrate that, under suitable external stimulations, the CNN with the proposed HLRPF acts as a fuzzy-like pattern classifier that performs much better than an ordinary CNN. The results imply relationship between learning and recognition. -- Highlights: ► Proposing a Hebbian learning rule with passive forgetting (HLRPF). ► Defining indices to investigate the evolution of the weights simply. ► The chaotic neural network with HLRPF acts as a fuzzy-like pattern classifier. ► The pattern classifier ability of the network is improved much.

  20. Bidirectional Hebbian Plasticity Induced by Low-Frequency Stimulation in Basal Dendrites of Rat Barrel Cortex Layer 5 Pyramidal Neurons.

    Science.gov (United States)

    Díez-García, Andrea; Barros-Zulaica, Natali; Núñez, Ángel; Buño, Washington; Fernández de Sevilla, David

    2017-01-01

    According to Hebb's original hypothesis (Hebb, 1949), synapses are reinforced when presynaptic activity triggers postsynaptic firing, resulting in long-term potentiation (LTP) of synaptic efficacy. Long-term depression (LTD) is a use-dependent decrease in synaptic strength that is thought to be due to synaptic input causing a weak postsynaptic effect. Although the mechanisms that mediate long-term synaptic plasticity have been investigated for at least three decades not all question have as yet been answered. Therefore, we aimed at determining the mechanisms that generate LTP or LTD with the simplest possible protocol. Low-frequency stimulation of basal dendrite inputs in Layer 5 pyramidal neurons of the rat barrel cortex induces LTP. This stimulation triggered an EPSP, an action potential (AP) burst, and a Ca 2+ spike. The same stimulation induced LTD following manipulations that reduced the Ca 2+ spike and Ca 2+ signal or the AP burst. Low-frequency whisker deflections induced similar bidirectional plasticity of action potential evoked responses in anesthetized rats. These results suggest that both in vitro and in vivo similar mechanisms regulate the balance between LTP and LTD. This simple induction form of bidirectional hebbian plasticity could be present in the natural conditions to regulate the detection, flow, and storage of sensorimotor information.

  1. Bidirectional Hebbian Plasticity Induced by Low-Frequency Stimulation in Basal Dendrites of Rat Barrel Cortex Layer 5 Pyramidal Neurons

    Science.gov (United States)

    Díez-García, Andrea; Barros-Zulaica, Natali; Núñez, Ángel; Buño, Washington; Fernández de Sevilla, David

    2017-01-01

    According to Hebb's original hypothesis (Hebb, 1949), synapses are reinforced when presynaptic activity triggers postsynaptic firing, resulting in long-term potentiation (LTP) of synaptic efficacy. Long-term depression (LTD) is a use-dependent decrease in synaptic strength that is thought to be due to synaptic input causing a weak postsynaptic effect. Although the mechanisms that mediate long-term synaptic plasticity have been investigated for at least three decades not all question have as yet been answered. Therefore, we aimed at determining the mechanisms that generate LTP or LTD with the simplest possible protocol. Low-frequency stimulation of basal dendrite inputs in Layer 5 pyramidal neurons of the rat barrel cortex induces LTP. This stimulation triggered an EPSP, an action potential (AP) burst, and a Ca2+ spike. The same stimulation induced LTD following manipulations that reduced the Ca2+ spike and Ca2+ signal or the AP burst. Low-frequency whisker deflections induced similar bidirectional plasticity of action potential evoked responses in anesthetized rats. These results suggest that both in vitro and in vivo similar mechanisms regulate the balance between LTP and LTD. This simple induction form of bidirectional hebbian plasticity could be present in the natural conditions to regulate the detection, flow, and storage of sensorimotor information. PMID:28203145

  2. Burst-induced anti-Hebbian depression acts through short-term synaptic dynamics to cancel redundant sensory signals.

    Science.gov (United States)

    Harvey-Girard, Erik; Lewis, John; Maler, Leonard

    2010-04-28

    Weakly electric fish can enhance the detection and localization of important signals such as those of prey in part by cancellation of redundant spatially diffuse electric signals due to, e.g., their tail bending. The cancellation mechanism is based on descending input, conveyed by parallel fibers emanating from cerebellar granule cells, that produces a negative image of the global low-frequency signals in pyramidal cells within the first-order electrosensory region, the electrosensory lateral line lobe (ELL). Here we demonstrate that the parallel fiber synaptic input to ELL pyramidal cell undergoes long-term depression (LTD) whenever both parallel fiber afferents and their target cells are stimulated to produce paired burst discharges. Paired large bursts (4-4) induce robust LTD over pre-post delays of up to +/-50 ms, whereas smaller bursts (2-2) induce weaker LTD. Single spikes (either presynaptic or postsynaptic) paired with bursts did not induce LTD. Tetanic presynaptic stimulation was also ineffective in inducing LTD. Thus, we have demonstrated a form of anti-Hebbian LTD that depends on the temporal correlation of burst discharge. We then demonstrated that the burst-induced LTD is postsynaptic and requires the NR2B subunit of the NMDA receptor, elevation of postsynaptic Ca(2+), and activation of CaMKIIbeta. A model incorporating local inhibitory circuitry and previously identified short-term presynaptic potentiation of the parallel fiber synapses further suggests that the combination of burst-induced LTD, presynaptic potentiation, and local inhibition may be sufficient to explain the generation of the negative image and cancellation of redundant sensory input by ELL pyramidal cells.

  3. A Hebbian learning rule gives rise to mirror neurons and links them to control theoretic inverse models

    OpenAIRE

    Hanuschkin, A.; Ganguli, S.; Hahnloser, R. H. R.

    2013-01-01

    Mirror neurons are neurons whose responses to the observation of a motor act resemble responses measured during production of that act. Computationally, mirror neurons have been viewed as evidence for the existence of internal inverse models. Such models, rooted within control theory, map-desired sensory targets onto the motor commands required to generate those targets. To jointly explore both the formation of mirrored responses and their functional contribution to inverse models, we develop...

  4. Delta Learning Rule for the Active Sites Model

    OpenAIRE

    Lingashetty, Krishna Chaithanya

    2010-01-01

    This paper reports the results on methods of comparing the memory retrieval capacity of the Hebbian neural network which implements the B-Matrix approach, by using the Widrow-Hoff rule of learning. We then, extend the recently proposed Active Sites model by developing a delta rule to increase memory capacity. Also, this paper extends the binary neural network to a multi-level (non-binary) neural network.

  5. Neuromodulated Spike-Timing-Dependent Plasticity and Theory of Three-Factor Learning Rules

    Directory of Open Access Journals (Sweden)

    Wulfram eGerstner

    2016-01-01

    Full Text Available Classical Hebbian learning puts the emphasis on joint pre- and postsynaptic activity, but neglects the potential role of neuromodulators. Since neuromodulators convey information about novelty or reward, the influence of neuromodulatorson synaptic plasticity is useful not just for action learning in classical conditioning, but also to decide 'when' to create new memories in response to a flow of sensory stimuli.In this review, we focus on timing requirements for pre- and postsynaptic activity in conjunction with one or several phasic neuromodulatory signals. While the emphasis of the text is on conceptual models and mathematical theories, we also discusssome experimental evidence for neuromodulation of Spike-Timing-Dependent Plasticity.We highlight the importance of synaptic mechanisms in bridging the temporal gap between sensory stimulation and neuromodulatory signals, and develop a framework for a class of neo-Hebbian three-factor learning rules that depend on presynaptic activity, postsynaptic variables as well as the influence of neuromodulators.

  6. A Reinforcement Learning Framework for Spiking Networks with Dynamic Synapses

    Directory of Open Access Journals (Sweden)

    Karim El-Laithy

    2011-01-01

    Full Text Available An integration of both the Hebbian-based and reinforcement learning (RL rules is presented for dynamic synapses. The proposed framework permits the Hebbian rule to update the hidden synaptic model parameters regulating the synaptic response rather than the synaptic weights. This is performed using both the value and the sign of the temporal difference in the reward signal after each trial. Applying this framework, a spiking network with spike-timing-dependent synapses is tested to learn the exclusive-OR computation on a temporally coded basis. Reward values are calculated with the distance between the output spike train of the network and a reference target one. Results show that the network is able to capture the required dynamics and that the proposed framework can reveal indeed an integrated version of Hebbian and RL. The proposed framework is tractable and less computationally expensive. The framework is applicable to a wide class of synaptic models and is not restricted to the used neural representation. This generality, along with the reported results, supports adopting the introduced approach to benefit from the biologically plausible synaptic models in a wide range of intuitive signal processing.

  7. Theta coordinated error-driven learning in the hippocampus.

    Directory of Open Access Journals (Sweden)

    Nicholas Ketz

    Full Text Available The learning mechanism in the hippocampus has almost universally been assumed to be Hebbian in nature, where individual neurons in an engram join together with synaptic weight increases to support facilitated recall of memories later. However, it is also widely known that Hebbian learning mechanisms impose significant capacity constraints, and are generally less computationally powerful than learning mechanisms that take advantage of error signals. We show that the differential phase relationships of hippocampal subfields within the overall theta rhythm enable a powerful form of error-driven learning, which results in significantly greater capacity, as shown in computer simulations. In one phase of the theta cycle, the bidirectional connectivity between CA1 and entorhinal cortex can be trained in an error-driven fashion to learn to effectively encode the cortical inputs in a compact and sparse form over CA1. In a subsequent portion of the theta cycle, the system attempts to recall an existing memory, via the pathway from entorhinal cortex to CA3 and CA1. Finally the full theta cycle completes when a strong target encoding representation of the current input is imposed onto the CA1 via direct projections from entorhinal cortex. The difference between this target encoding and the attempted recall of the same representation on CA1 constitutes an error signal that can drive the learning of CA3 to CA1 synapses. This CA3 to CA1 pathway is critical for enabling full reinstatement of recalled hippocampal memories out in cortex. Taken together, these new learning dynamics enable a much more robust, high-capacity model of hippocampal learning than was available previously under the classical Hebbian model.

  8. A re-examination of Hebbian-covariance rules and spike timing-dependent plasticity in cat visual cortex in vivo

    Directory of Open Access Journals (Sweden)

    Yves Frégnac

    2010-12-01

    Full Text Available Spike-Timing-Dependent Plasticity (STDP is considered as an ubiquitous rule for associative plasticity in cortical networks in vitro. However, limited supporting evidence for its functional role has been provided in vivo. In particular, there are very few studies demonstrating the co-occurence of synaptic efficiency changes and alteration of sensory responses in adult cortex during Hebbian or STDP protocols. We addressed this issue by reviewing and comparing the functional effects of two types of cellular conditioning in cat visual cortex. The first one, referred to as the covariance protocol, obeys a generalized Hebbian framework, by imposing, for different stimuli, supervised positive and negative changes in covariance between postsynaptic and presynaptic activity rates. The second protocol, based on intracellular recordings, replicated in vivo variants of the theta-burst paradigm (TBS, proven successful in inducing long-term potentiation (LTP in vitro. Since it was shown to impose a precise correlation delay between the electrically activated thalamic input and the TBS-induced postsynaptic spike, this protocol can be seen as a probe of causal (pre-before-post STDP. By choosing a thalamic region where the visual field representation was in retinotopic overlap with the intracellularly recorded cortical receptive field as the afferent site for supervised electrical stimulation, this protocol allowed to look for possible correlates between STDP and functional reorganization of the conditioned cortical receptive field. The rate-based covariance protocol induced significant and large amplitude changes in receptive field properties, in both kitten and adult V1 cortex. The TBS STDP-like protocol produced in the adult significant changes in the synaptic gain of the electrically activated thalamic pathway, but the statistical significance of the functional correlates was detectable mostly at the population level. Comparison of our observations with the

  9. The effect of STDP temporal kernel structure on the learning dynamics of single excitatory and inhibitory synapses.

    Directory of Open Access Journals (Sweden)

    Yotam Luz

    Full Text Available Spike-Timing Dependent Plasticity (STDP is characterized by a wide range of temporal kernels. However, much of the theoretical work has focused on a specific kernel - the "temporally asymmetric Hebbian" learning rules. Previous studies linked excitatory STDP to positive feedback that can account for the emergence of response selectivity. Inhibitory plasticity was associated with negative feedback that can balance the excitatory and inhibitory inputs. Here we study the possible computational role of the temporal structure of the STDP. We represent the STDP as a superposition of two processes: potentiation and depression. This allows us to model a wide range of experimentally observed STDP kernels, from Hebbian to anti-Hebbian, by varying a single parameter. We investigate STDP dynamics of a single excitatory or inhibitory synapse in purely feed-forward architecture. We derive a mean-field-Fokker-Planck dynamics for the synaptic weight and analyze the effect of STDP structure on the fixed points of the mean field dynamics. We find a phase transition along the Hebbian to anti-Hebbian parameter from a phase that is characterized by a unimodal distribution of the synaptic weight, in which the STDP dynamics is governed by negative feedback, to a phase with positive feedback characterized by a bimodal distribution. The critical point of this transition depends on general properties of the STDP dynamics and not on the fine details. Namely, the dynamics is affected by the pre-post correlations only via a single number that quantifies its overlap with the STDP kernel. We find that by manipulating the STDP temporal kernel, negative feedback can be induced in excitatory synapses and positive feedback in inhibitory. Moreover, there is an exact symmetry between inhibitory and excitatory plasticity, i.e., for every STDP rule of inhibitory synapse there exists an STDP rule for excitatory synapse, such that their dynamics is identical.

  10. Optical implementations of associative networks with versatile adaptive learning capabilities.

    Science.gov (United States)

    Fisher, A D; Lippincott, W L; Lee, J N

    1987-12-01

    Optical associative, parallel-processing architectures are being developed using a multimodule approach, where a number of smaller, adaptive, associative modules are nonlinearly interconnected and cascaded under the guidance of a variety of organizational principles to structure larger architectures for solving specific problems. A number of novel optical implementations with versatile adaptive learning capabilities are presented for the individual associative modules, including holographic configurations and five specific electrooptic configurations. The practical issues involved in real optical architectures are analyzed, and actual laboratory optical implementations of associative modules based on Hebbian and Widrow-Hoff learning rules are discussed, including successful experimental demonstrations of their operation.

  11. Competitive STDP Learning of Overlapping Spatial Patterns.

    Science.gov (United States)

    Krunglevicius, Dalius

    2015-08-01

    Spike-timing-dependent plasticity (STDP) is a set of Hebbian learning rules firmly based on biological evidence. It has been demonstrated that one of the STDP learning rules is suited for learning spatiotemporal patterns. When multiple neurons are organized in a simple competitive spiking neural network, this network is capable of learning multiple distinct patterns. If patterns overlap significantly (i.e., patterns are mutually inclusive), however, competition would not preclude trained neuron's responding to a new pattern and adjusting synaptic weights accordingly. This letter presents a simple neural network that combines vertical inhibition and Euclidean distance-dependent synaptic strength factor. This approach helps to solve the problem of pattern size-dependent parameter optimality and significantly reduces the probability of a neuron's forgetting an already learned pattern. For demonstration purposes, the network was trained for the first ten letters of the Braille alphabet.

  12. ERP evidence for conflict in contingency learning.

    Science.gov (United States)

    Whitehead, Peter S; Brewer, Gene A; Blais, Chris

    2017-07-01

    The proportion congruency effect refers to the observation that the magnitude of the Stroop effect increases as the proportion of congruent trials in a block increases. Contemporary work shows that proportion effects can be driven by both context and individual items, and are referred to as context-specific proportion congruency (CSPC) and item-specific proportion congruency (ISPC) effects, respectively. The conflict-modulated Hebbian learning account posits that these effects manifest from the same mechanism, while the parallel episodic processing model posits that the ISPC can occur by simple associative learning. Our prior work showed that the neural correlates of the CSPC is an N2 over frontocentral electrode sites approximately 300 ms after stimulus onset that predicts behavioral performance. There is strong consensus in the field that this N2 signal is associated with conflict detection in the medial frontal cortex. The experiment reported here assesses whether the same qualitative electrophysiological pattern of results holds for the ISPC. We find that the spatial topography of the N2 is similar but slightly delayed with a peak onset of approximately 300 ms after stimulus onset. We argue that this provides strong evidence that a single common mechanism-conflict-modulated Hebbian learning-drives both the ISPC and CSPC. © 2017 Society for Psychophysiological Research.

  13. Domain-specific and domain-general constraints on word and sequence learning.

    Science.gov (United States)

    Archibald, Lisa M D; Joanisse, Marc F

    2013-02-01

    The relative influences of language-related and memory-related constraints on the learning of novel words and sequences were examined by comparing individual differences in performance of children with and without specific deficits in either language or working memory. Children recalled lists of words in a Hebbian learning protocol in which occasional lists repeated, yielding improved recall over the course of the task on the repeated lists. The task involved presentation of pictures of common nouns followed immediately by equivalent presentations of the spoken names. The same participants also completed a paired-associate learning task involving word-picture and nonword-picture pairs. Hebbian learning was observed for all groups. Domain-general working memory constrained immediate recall, whereas language abilities impacted recall in the auditory modality only. In addition, working memory constrained paired-associate learning generally, whereas language abilities disproportionately impacted novel word learning. Overall, all of the learning tasks were highly correlated with domain-general working memory. The learning of nonwords was additionally related to general intelligence, phonological short-term memory, language abilities, and implicit learning. The results suggest that distinct associations between language- and memory-related mechanisms support learning of familiar and unfamiliar phonological forms and sequences.

  14. Adaptive WTA with an analog VLSI neuromorphic learning chip.

    Science.gov (United States)

    Häfliger, Philipp

    2007-03-01

    In this paper, we demonstrate how a particular spike-based learning rule (where exact temporal relations between input and output spikes of a spiking model neuron determine the changes of the synaptic weights) can be tuned to express rate-based classical Hebbian learning behavior (where the average input and output spike rates are sufficient to describe the synaptic changes). This shift in behavior is controlled by the input statistic and by a single time constant. The learning rule has been implemented in a neuromorphic very large scale integration (VLSI) chip as part of a neurally inspired spike signal image processing system. The latter is the result of the European Union research project Convolution AER Vision Architecture for Real-Time (CAVIAR). Since it is implemented as a spike-based learning rule (which is most convenient in the overall spike-based system), even if it is tuned to show rate behavior, no explicit long-term average signals are computed on the chip. We show the rule's rate-based Hebbian learning ability in a classification task in both simulation and chip experiment, first with artificial stimuli and then with sensor input from the CAVIAR system.

  15. Learning tinnitus

    Science.gov (United States)

    van Hemmen, J. Leo

    Tinnitus, implying the perception of sound without the presence of any acoustical stimulus, is a chronic and serious problem for about 2% of the human population. In many cases, tinnitus is a pitch-like sensation associated with a hearing loss that confines the tinnitus frequency to an interval of the tonotopic axis. Even in patients with a normal audiogram the presence of tinnitus may be associated with damage of hair-cell function in this interval. It has been suggested that homeostatic regulation and, hence, increase of activity leads to the emergence of tinnitus. For patients with hearing loss, we present spike-timing-dependent Hebbian plasticity (STDP) in conjunction with homeostasis as a mechanism for ``learning'' tinnitus in a realistic neuronal network with tonotopically arranged synaptic excitation and inhibition. In so doing we use both dynamical scaling of the synaptic strengths and altering the resting potential of the cells. The corresponding simulations are robust to parameter changes. Understanding the mechanisms of tinnitus induction, such as here, may help improving therapy. Work done in collaboration with Julie Goulet and Michael Schneider. JLvH has been supported partially by BCCN - Munich.

  16. "The seven sins" of the Hebbian synapse: can the hypothesis of synaptic plasticity explain long-term memory consolidation?

    Science.gov (United States)

    Arshavsky, Yuri I

    2006-10-01

    Memorizing new facts and events means that entering information produces specific physical changes within the brain. According to the commonly accepted view, traces of memory are stored through the structural modifications of synaptic connections, which result in changes of synaptic efficiency and, therefore, in formations of new patterns of neural activity (the hypothesis of synaptic plasticity). Most of the current knowledge on learning and initial stages of memory consolidation ("synaptic consolidation") is based on this hypothesis. However, the hypothesis of synaptic plasticity faces a number of conceptual and experimental difficulties when it deals with potentially permanent consolidation of declarative memory ("system consolidation"). These difficulties are rooted in the major intrinsic self-contradiction of the hypothesis: stable declarative memory is unlikely to be based on such a non-stable foundation as synaptic plasticity. Memory that can last throughout an entire lifespan should be "etched in stone." The only "stone-like" molecules within living cells are DNA molecules. Therefore, I advocate an alternative, genomic hypothesis of memory, which suggests that acquired information is persistently stored within individual neurons through modifications of DNA, and that these modifications serve as the carriers of elementary memory traces.

  17. Associative Learning in Invertebrates

    Science.gov (United States)

    Hawkins, Robert D.; Byrne, John H.

    2015-01-01

    This work reviews research on neural mechanisms of two types of associative learning in the marine mollusk Aplysia, classical conditioning of the gill- and siphon-withdrawal reflex and operant conditioning of feeding behavior. Basic classical conditioning is caused in part by activity-dependent facilitation at sensory neuron–motor neuron (SN–MN) synapses and involves a hybrid combination of activity-dependent presynaptic facilitation and Hebbian potentiation, which are coordinated by trans-synaptic signaling. Classical conditioning also shows several higher-order features, which might be explained by the known circuit connections in Aplysia. Operant conditioning is caused in part by a different type of mechanism, an intrinsic increase in excitability of an identified neuron in the central pattern generator (CPG) for feeding. However, for both classical and operant conditioning, adenylyl cyclase is a molecular site of convergence of the two signals that are associated. Learning in other invertebrate preparations also involves many of the same mechanisms, which may contribute to learning in vertebrates as well. PMID:25877219

  18. Learning with incomplete information in the committee machine.

    Science.gov (United States)

    Bergmann, Urs M; Kühn, Reimer; Stamatescu, Ion-Olimpiu

    2009-12-01

    We study the problem of learning with incomplete information in a student-teacher setup for the committee machine. The learning algorithm combines unsupervised Hebbian learning of a series of associations with a delayed reinforcement step, in which the set of previously learnt associations is partly and indiscriminately unlearnt, to an extent that depends on the success rate of the student on these previously learnt associations. The relevant learning parameter lambda represents the strength of Hebbian learning. A coarse-grained analysis of the system yields a set of differential equations for overlaps of student and teacher weight vectors, whose solutions provide a complete description of the learning behavior. It reveals complicated dynamics showing that perfect generalization can be obtained if the learning parameter exceeds a threshold lambda ( c ), and if the initial value of the overlap between student and teacher weights is non-zero. In case of convergence, the generalization error exhibits a power law decay as a function of the number of examples used in training, with an exponent that depends on the parameter lambda. An investigation of the system flow in a subspace with broken permutation symmetry between hidden units reveals a bifurcation point lambda* above which perfect generalization does not depend on initial conditions. Finally, we demonstrate that cases of a complexity mismatch between student and teacher are optimally resolved in the sense that an over-complex student can emulate a less complex teacher rule, while an under-complex student reaches a state which realizes the minimal generalization error compatible with the complexity mismatch.

  19. Learning invariance from natural images inspired by observations in the primary visual cortex.

    Science.gov (United States)

    Teichmann, Michael; Wiltschut, Jan; Hamker, Fred

    2012-05-01

    The human visual system has the remarkable ability to largely recognize objects invariant of their position, rotation, and scale. A good interpretation of neurobiological findings involves a computational model that simulates signal processing of the visual cortex. In part, this is likely achieved step by step from early to late areas of visual perception. While several algorithms have been proposed for learning feature detectors, only few studies at hand cover the issue of biologically plausible learning of such invariance. In this study, a set of Hebbian learning rules based on calcium dynamics and homeostatic regulations of single neurons is proposed. Their performance is verified within a simple model of the primary visual cortex to learn so-called complex cells, based on a sequence of static images. As a result, the learned complex-cell responses are largely invariant to phase and position.

  20. Analysis of ensemble learning using simple perceptrons based on online learning theory

    Science.gov (United States)

    Miyoshi, Seiji; Hara, Kazuyuki; Okada, Masato

    2005-03-01

    Ensemble learning of K nonlinear perceptrons, which determine their outputs by sign functions, is discussed within the framework of online learning and statistical mechanics. One purpose of statistical learning theory is to theoretically obtain the generalization error. This paper shows that ensemble generalization error can be calculated by using two order parameters, that is, the similarity between a teacher and a student, and the similarity among students. The differential equations that describe the dynamical behaviors of these order parameters are derived in the case of general learning rules. The concrete forms of these differential equations are derived analytically in the cases of three well-known rules: Hebbian learning, perceptron learning, and AdaTron (adaptive perceptron) learning. Ensemble generalization errors of these three rules are calculated by using the results determined by solving their differential equations. As a result, these three rules show different characteristics in their affinity for ensemble learning, that is “maintaining variety among students.” Results show that AdaTron learning is superior to the other two rules with respect to that affinity.

  1. Learning

    Directory of Open Access Journals (Sweden)

    Mohsen Laabidi

    2014-01-01

    Full Text Available Nowadays learning technologies transformed educational systems with impressive progress of Information and Communication Technologies (ICT. Furthermore, when these technologies are available, affordable and accessible, they represent more than a transformation for people with disabilities. They represent real opportunities with access to an inclusive education and help to overcome the obstacles they met in classical educational systems. In this paper, we will cover basic concepts of e-accessibility, universal design and assistive technologies, with a special focus on accessible e-learning systems. Then, we will present recent research works conducted in our research Laboratory LaTICE toward the development of an accessible online learning environment for persons with disabilities from the design and specification step to the implementation. We will present, in particular, the accessible version “MoodleAcc+” of the well known e-learning platform Moodle as well as new elaborated generic models and a range of tools for authoring and evaluating accessible educational content.

  2. Thermodynamic efficiency of learning a rule in neural networks

    Science.gov (United States)

    Goldt, Sebastian; Seifert, Udo

    2017-11-01

    Biological systems have to build models from their sensory input data that allow them to efficiently process previously unseen inputs. Here, we study a neural network learning a binary classification rule for these inputs from examples provided by a teacher. We analyse the ability of the network to apply the rule to new inputs, that is to generalise from past experience. Using stochastic thermodynamics, we show that the thermodynamic costs of the learning process provide an upper bound on the amount of information that the network is able to learn from its teacher for both batch and online learning. This allows us to introduce a thermodynamic efficiency of learning. We analytically compute the dynamics and the efficiency of a noisy neural network performing online learning in the thermodynamic limit. In particular, we analyse three popular learning algorithms, namely Hebbian, Perceptron and AdaTron learning. Our work extends the methods of stochastic thermodynamics to a new type of learning problem and might form a suitable basis for investigating the thermodynamics of decision-making.

  3. Learning in AN Oscillatory Cortical Model

    Science.gov (United States)

    Scarpetta, Silvia; Li, Zhaoping; Hertz, John

    We study a model of generalized-Hebbian learning in asymmetric oscillatory neural networks modeling cortical areas such as hippocampus and olfactory cortex. The learning rule is based on the synaptic plasticity observed experimentally, in particular long-term potentiation and long-term depression of the synaptic efficacies depending on the relative timing of the pre- and postsynaptic activities during learning. The learned memory or representational states can be encoded by both the amplitude and the phase patterns of the oscillating neural populations, enabling more efficient and robust information coding than in conventional models of associative memory or input representation. Depending on the class of nonlinearity of the activation function, the model can function as an associative memory for oscillatory patterns (nonlinearity of class II) or can generalize from or interpolate between the learned states, appropriate for the function of input representation (nonlinearity of class I). In the former case, simulations of the model exhibits a first order transition between the "disordered state" and the "ordered" memory state.

  4. A Learning Model for L/M Specificity in Ganglion Cells

    Science.gov (United States)

    Ahumada, Albert J.

    2016-01-01

    An unsupervised learning model for developing LM specific wiring at the ganglion cell level would support the research indicating LM specific wiring at the ganglion cell level (Reid and Shapley, 2002). Removing the contributions to the surround from cells of the same cone type improves the signal-to-noise ratio of the chromatic signals. The unsupervised learning model used is Hebbian associative learning, which strengthens the surround input connections according to the correlation of the output with the input. Since the surround units of the same cone type as the center are redundant with the center, their weights end up disappearing. This process can be thought of as a general mechanism for eliminating unnecessary cells in the nervous system.

  5. Habit learning and brain-machine interfaces (BMI): a tribute to Valentino Braitenberg's "Vehicles".

    Science.gov (United States)

    Birbaumer, Niels; Hummel, Friedhelm C

    2014-10-01

    Brain-Machine Interfaces (BMI) allow manipulation of external devices and computers directly with brain activity without involvement of overt motor actions. The neurophysiological principles of such robotic brain devices and BMIs follow Hebbian learning rules as described and realized by Valentino Braitenberg in his book "Vehicles," in the concept of a "thought pump" residing in subcortical basal ganglia structures. We describe here the application of BMIs for brain communication in totally locked-in patients and argue that the thought pump may extinguish-at least partially-in those people because of extinction of instrumentally learned cognitive responses and brain responses. We show that Pavlovian semantic conditioning may allow brain communication even in the completely paralyzed who does not show response-effect contingencies. Principles of skill learning and habit acquisition as formulated by Braitenberg are the building blocks of BMIs and neuroprostheses.

  6. MOLECULAR MECHANISMS OF FEAR LEARNING AND MEMORY

    Science.gov (United States)

    Johansen, Joshua P.; Cain, Christopher K.; Ostroff, Linnaea E.; LeDoux, Joseph E.

    2011-01-01

    Pavlovian fear conditioning is a useful behavioral paradigm for exploring the molecular mechanisms of learning and memory because a well-defined response to a specific environmental stimulus is produced through associative learning processes. Synaptic plasticity in the lateral nucleus of the amygdala (LA) underlies this form of associative learning. Here we summarize the molecular mechanisms that contribute to this synaptic plasticity in the context of auditory fear conditioning, the form of fear conditioning best understood at the molecular level. We discuss the neurotransmitter systems and signaling cascades that contribute to three phases of auditory fear conditioning: acquisition, consolidation, and reconsolidation. These studies suggest that multiple intracellular signaling pathways, including those triggered by activation of Hebbian processes and neuromodulatory receptors, interact to produce neural plasticity in the LA and behavioral fear conditioning. Together, this research illustrates the power of fear conditioning as a model system for characterizing the mechanisms of learning and memory in mammals, and potentially for understanding fear related disorders, such as PTSD and phobias. PMID:22036561

  7. Molecular mechanisms of fear learning and memory.

    Science.gov (United States)

    Johansen, Joshua P; Cain, Christopher K; Ostroff, Linnaea E; LeDoux, Joseph E

    2011-10-28

    Pavlovian fear conditioning is a particularly useful behavioral paradigm for exploring the molecular mechanisms of learning and memory because a well-defined response to a specific environmental stimulus is produced through associative learning processes. Synaptic plasticity in the lateral nucleus of the amygdala (LA) underlies this form of associative learning. Here, we summarize the molecular mechanisms that contribute to this synaptic plasticity in the context of auditory fear conditioning, the form of fear conditioning best understood at the molecular level. We discuss the neurotransmitter systems and signaling cascades that contribute to three phases of auditory fear conditioning: acquisition, consolidation, and reconsolidation. These studies suggest that multiple intracellular signaling pathways, including those triggered by activation of Hebbian processes and neuromodulatory receptors, interact to produce neural plasticity in the LA and behavioral fear conditioning. Collectively, this body of research illustrates the power of fear conditioning as a model system for characterizing the mechanisms of learning and memory in mammals and potentially for understanding fear-related disorders, such as PTSD and phobias. Copyright © 2011 Elsevier Inc. All rights reserved.

  8. Rapid motor learning in the translational vestibulo-ocular reflex

    Science.gov (United States)

    Zhou, Wu; Weldon, Patrick; Tang, Bingfeng; King, W. M.; Shelhamer, M. J. (Principal Investigator)

    2003-01-01

    Motor learning was induced in the translational vestibulo-ocular reflex (TVOR) when monkeys were repeatedly subjected to a brief (0.5 sec) head translation while they tried to maintain binocular fixation on a visual target for juice rewards. If the target was world-fixed, the initial eye speed of the TVOR gradually increased; if the target was head-fixed, the initial eye speed of the TVOR gradually decreased. The rate of learning acquisition was very rapid, with a time constant of approximately 100 trials, which was equivalent to or=1 d without any reinforcement, indicating induction of long-term synaptic plasticity. Although the learning generalized to targets with different viewing distances and to head translations with different accelerations, it was highly specific for the particular combination of head motion and evoked eye movement associated with the training. For example, it was specific to the modality of the stimulus (translation vs rotation) and the direction of the evoked eye movement in the training. Furthermore, when one eye was aligned with the heading direction so that it remained motionless during training, learning was not expressed in this eye, but only in the other nonaligned eye. These specificities show that the learning sites are neither in the sensory nor the motor limb of the reflex but in the sensory-motor transformation stage of the reflex. The dependence of the learning on both head motion and evoked eye movement suggests that Hebbian learning may be one of the underlying cellular mechanisms.

  9. Topological self-organization and prediction learning support both action and lexical chains in the brain.

    Science.gov (United States)

    Chersi, Fabian; Ferro, Marcello; Pezzulo, Giovanni; Pirrelli, Vito

    2014-07-01

    A growing body of evidence in cognitive psychology and neuroscience suggests a deep interconnection between sensory-motor and language systems in the brain. Based on recent neurophysiological findings on the anatomo-functional organization of the fronto-parietal network, we present a computational model showing that language processing may have reused or co-developed organizing principles, functionality, and learning mechanisms typical of premotor circuit. The proposed model combines principles of Hebbian topological self-organization and prediction learning. Trained on sequences of either motor or linguistic units, the network develops independent neuronal chains, formed by dedicated nodes encoding only context-specific stimuli. Moreover, neurons responding to the same stimulus or class of stimuli tend to cluster together to form topologically connected areas similar to those observed in the brain cortex. Simulations support a unitary explanatory framework reconciling neurophysiological motor data with established behavioral evidence on lexical acquisition, access, and recall. Copyright © 2014 Cognitive Science Society, Inc.

  10. Incremental learning of perceptual and conceptual representations and the puzzle of neural repetition suppression.

    Science.gov (United States)

    Gotts, Stephen J

    2016-08-01

    Incremental learning models of long-term perceptual and conceptual knowledge hold that neural representations are gradually acquired over many individual experiences via Hebbian-like activity-dependent synaptic plasticity across cortical connections of the brain. In such models, variation in task relevance of information, anatomic constraints, and the statistics of sensory inputs and motor outputs lead to qualitative alterations in the nature of representations that are acquired. Here, the proposal that behavioral repetition priming and neural repetition suppression effects are empirical markers of incremental learning in the cortex is discussed, and research results that both support and challenge this position are reviewed. Discussion is focused on a recent fMRI-adaptation study from our laboratory that shows decoupling of experience-dependent changes in neural tuning, priming, and repetition suppression, with representational changes that appear to work counter to the explicit task demands. Finally, critical experiments that may help to clarify and resolve current challenges are outlined.

  11. AHaH Computing–From Metastable Switches to Attractors to Machine Learning

    Science.gov (United States)

    Nugent, Michael Alexander; Molter, Timothy Wesley

    2014-01-01

    Modern computing architecture based on the separation of memory and processing leads to a well known problem called the von Neumann bottleneck, a restrictive limit on the data bandwidth between CPU and RAM. This paper introduces a new approach to computing we call AHaH computing where memory and processing are combined. The idea is based on the attractor dynamics of volatile dissipative electronics inspired by biological systems, presenting an attractive alternative architecture that is able to adapt, self-repair, and learn from interactions with the environment. We envision that both von Neumann and AHaH computing architectures will operate together on the same machine, but that the AHaH computing processor may reduce the power consumption and processing time for certain adaptive learning tasks by orders of magnitude. The paper begins by drawing a connection between the properties of volatility, thermodynamics, and Anti-Hebbian and Hebbian (AHaH) plasticity. We show how AHaH synaptic plasticity leads to attractor states that extract the independent components of applied data streams and how they form a computationally complete set of logic functions. After introducing a general memristive device model based on collections of metastable switches, we show how adaptive synaptic weights can be formed from differential pairs of incremental memristors. We also disclose how arrays of synaptic weights can be used to build a neural node circuit operating AHaH plasticity. By configuring the attractor states of the AHaH node in different ways, high level machine learning functions are demonstrated. This includes unsupervised clustering, supervised and unsupervised classification, complex signal prediction, unsupervised robotic actuation and combinatorial optimization of procedures–all key capabilities of biological nervous systems and modern machine learning algorithms with real world application. PMID:24520315

  12. AHaH computing-from metastable switches to attractors to machine learning.

    Directory of Open Access Journals (Sweden)

    Michael Alexander Nugent

    Full Text Available Modern computing architecture based on the separation of memory and processing leads to a well known problem called the von Neumann bottleneck, a restrictive limit on the data bandwidth between CPU and RAM. This paper introduces a new approach to computing we call AHaH computing where memory and processing are combined. The idea is based on the attractor dynamics of volatile dissipative electronics inspired by biological systems, presenting an attractive alternative architecture that is able to adapt, self-repair, and learn from interactions with the environment. We envision that both von Neumann and AHaH computing architectures will operate together on the same machine, but that the AHaH computing processor may reduce the power consumption and processing time for certain adaptive learning tasks by orders of magnitude. The paper begins by drawing a connection between the properties of volatility, thermodynamics, and Anti-Hebbian and Hebbian (AHaH plasticity. We show how AHaH synaptic plasticity leads to attractor states that extract the independent components of applied data streams and how they form a computationally complete set of logic functions. After introducing a general memristive device model based on collections of metastable switches, we show how adaptive synaptic weights can be formed from differential pairs of incremental memristors. We also disclose how arrays of synaptic weights can be used to build a neural node circuit operating AHaH plasticity. By configuring the attractor states of the AHaH node in different ways, high level machine learning functions are demonstrated. This includes unsupervised clustering, supervised and unsupervised classification, complex signal prediction, unsupervised robotic actuation and combinatorial optimization of procedures-all key capabilities of biological nervous systems and modern machine learning algorithms with real world application.

  13. Learning by stimulation avoidance: A principle to control spiking neural networks dynamics.

    Science.gov (United States)

    Sinapayen, Lana; Masumori, Atsushi; Ikegami, Takashi

    2017-01-01

    Learning based on networks of real neurons, and learning based on biologically inspired models of neural networks, have yet to find general learning rules leading to widespread applications. In this paper, we argue for the existence of a principle allowing to steer the dynamics of a biologically inspired neural network. Using carefully timed external stimulation, the network can be driven towards a desired dynamical state. We term this principle "Learning by Stimulation Avoidance" (LSA). We demonstrate through simulation that the minimal sufficient conditions leading to LSA in artificial networks are also sufficient to reproduce learning results similar to those obtained in biological neurons by Shahaf and Marom, and in addition explains synaptic pruning. We examined the underlying mechanism by simulating a small network of 3 neurons, then scaled it up to a hundred neurons. We show that LSA has a higher explanatory power than existing hypotheses about the response of biological neural networks to external simulation, and can be used as a learning rule for an embodied application: learning of wall avoidance by a simulated robot. In other works, reinforcement learning with spiking networks can be obtained through global reward signals akin simulating the dopamine system; we believe that this is the first project demonstrating sensory-motor learning with random spiking networks through Hebbian learning relying on environmental conditions without a separate reward system.

  14. Grounded understanding of abstract concepts: The case of STEM learning.

    Science.gov (United States)

    Hayes, Justin C; Kraemer, David J M

    2017-01-01

    Characterizing the neural implementation of abstract conceptual representations has long been a contentious topic in cognitive science. At the heart of the debate is whether the "sensorimotor" machinery of the brain plays a central role in representing concepts, or whether the involvement of these perceptual and motor regions is merely peripheral or epiphenomenal. The domain of science, technology, engineering, and mathematics (STEM) learning provides an important proving ground for sensorimotor (or grounded) theories of cognition, as concepts in science and engineering courses are often taught through laboratory-based and other hands-on methodologies. In this review of the literature, we examine evidence suggesting that sensorimotor processes strengthen learning associated with the abstract concepts central to STEM pedagogy. After considering how contemporary theories have defined abstraction in the context of semantic knowledge, we propose our own explanation for how body-centered information, as computed in sensorimotor brain regions and visuomotor association cortex, can form a useful foundation upon which to build an understanding of abstract scientific concepts, such as mechanical force. Drawing from theories in cognitive neuroscience, we then explore models elucidating the neural mechanisms involved in grounding intangible concepts, including Hebbian learning, predictive coding, and neuronal recycling. Empirical data on STEM learning through hands-on instruction are considered in light of these neural models. We conclude the review by proposing three distinct ways in which the field of cognitive neuroscience can contribute to STEM learning by bolstering our understanding of how the brain instantiates abstract concepts in an embodied fashion.

  15. Continuous Online Sequence Learning with an Unsupervised Neural Network Model.

    Science.gov (United States)

    Cui, Yuwei; Ahmad, Subutar; Hawkins, Jeff

    2016-09-14

    The ability to recognize and predict temporal sequences of sensory inputs is vital for survival in natural environments. Based on many known properties of cortical neurons, hierarchical temporal memory (HTM) sequence memory recently has been proposed as a theoretical framework for sequence learning in the cortex. In this letter, we analyze properties of HTM sequence memory and apply it to sequence learning and prediction problems with streaming data. We show the model is able to continuously learn a large number of variableorder temporal sequences using an unsupervised Hebbian-like learning rule. The sparse temporal codes formed by the model can robustly handle branching temporal sequences by maintaining multiple predictions until there is sufficient disambiguating evidence. We compare the HTM sequence memory with other sequence learning algorithms, including statistical methods: autoregressive integrated moving average; feedforward neural networks-time delay neural network and online sequential extreme learning machine; and recurrent neural networks-long short-term memory and echo-state networks on sequence prediction problems with both artificial and real-world data. The HTM model achieves comparable accuracy to other state-of-the-art algorithms. The model also exhibits properties that are critical for sequence learning, including continuous online learning, the ability to handle multiple predictions and branching sequences with high-order statistics, robustness to sensor noise and fault tolerance, and good performance without task-specific hyperparameter tuning. Therefore, the HTM sequence memory not only advances our understanding of how the brain may solve the sequence learning problem but is also applicable to real-world sequence learning problems from continuous data streams.

  16. Bayesian Inference and Online Learning in Poisson Neuronal Networks.

    Science.gov (United States)

    Huang, Yanping; Rao, Rajesh P N

    2016-08-01

    Motivated by the growing evidence for Bayesian computation in the brain, we show how a two-layer recurrent network of Poisson neurons can perform both approximate Bayesian inference and learning for any hidden Markov model. The lower-layer sensory neurons receive noisy measurements of hidden world states. The higher-layer neurons infer a posterior distribution over world states via Bayesian inference from inputs generated by sensory neurons. We demonstrate how such a neuronal network with synaptic plasticity can implement a form of Bayesian inference similar to Monte Carlo methods such as particle filtering. Each spike in a higher-layer neuron represents a sample of a particular hidden world state. The spiking activity across the neural population approximates the posterior distribution over hidden states. In this model, variability in spiking is regarded not as a nuisance but as an integral feature that provides the variability necessary for sampling during inference. We demonstrate how the network can learn the likelihood model, as well as the transition probabilities underlying the dynamics, using a Hebbian learning rule. We present results illustrating the ability of the network to perform inference and learning for arbitrary hidden Markov models.

  17. Methods for reducing interference in the Complementary Learning Systems model: oscillating inhibition and autonomous memory rehearsal.

    Science.gov (United States)

    Norman, Kenneth A; Newman, Ehren L; Perotte, Adler J

    2005-11-01

    The stability-plasticity problem (i.e. how the brain incorporates new information into its model of the world, while at the same time preserving existing knowledge) has been at the forefront of computational memory research for several decades. In this paper, we critically evaluate how well the Complementary Learning Systems theory of hippocampo-cortical interactions addresses the stability-plasticity problem. We identify two major challenges for the model: Finding a learning algorithm for cortex and hippocampus that enacts selective strengthening of weak memories, and selective punishment of competing memories; and preventing catastrophic forgetting in the case of non-stationary environments (i.e. when items are temporarily removed from the training set). We then discuss potential solutions to these problems: First, we describe a recently developed learning algorithm that leverages neural oscillations to find weak parts of memories (so they can be strengthened) and strong competitors (so they can be punished), and we show how this algorithm outperforms other learning algorithms (CPCA Hebbian learning and Leabra at memorizing overlapping patterns. Second, we describe how autonomous re-activation of memories (separately in cortex and hippocampus) during REM sleep, coupled with the oscillating learning algorithm, can reduce the rate of forgetting of input patterns that are no longer present in the environment. We then present a simple demonstration of how this process can prevent catastrophic interference in an AB-AC learning paradigm.

  18. Sensorimotor learning biases choice behavior: a learning neural field model for decision making.

    Directory of Open Access Journals (Sweden)

    Christian Klaes

    Full Text Available According to a prominent view of sensorimotor processing in primates, selection and specification of possible actions are not sequential operations. Rather, a decision for an action emerges from competition between different movement plans, which are specified and selected in parallel. For action choices which are based on ambiguous sensory input, the frontoparietal sensorimotor areas are considered part of the common underlying neural substrate for selection and specification of action. These areas have been shown capable of encoding alternative spatial motor goals in parallel during movement planning, and show signatures of competitive value-based selection among these goals. Since the same network is also involved in learning sensorimotor associations, competitive action selection (decision making should not only be driven by the sensory evidence and expected reward in favor of either action, but also by the subject's learning history of different sensorimotor associations. Previous computational models of competitive neural decision making used predefined associations between sensory input and corresponding motor output. Such hard-wiring does not allow modeling of how decisions are influenced by sensorimotor learning or by changing reward contingencies. We present a dynamic neural field model which learns arbitrary sensorimotor associations with a reward-driven Hebbian learning algorithm. We show that the model accurately simulates the dynamics of action selection with different reward contingencies, as observed in monkey cortical recordings, and that it correctly predicted the pattern of choice errors in a control experiment. With our adaptive model we demonstrate how network plasticity, which is required for association learning and adaptation to new reward contingencies, can influence choice behavior. The field model provides an integrated and dynamic account for the operations of sensorimotor integration, working memory and action

  19. Word learning emerges from the interaction of online referent selection and slow associative learning

    Science.gov (United States)

    McMurray, Bob; Horst, Jessica S.; Samuelson, Larissa K.

    2013-01-01

    Classic approaches to word learning emphasize the problem of referential ambiguity: in any naming situation the referent of a novel word must be selected from many possible objects, properties, actions, etc. To solve this problem, researchers have posited numerous constraints, and inference strategies, but assume that determining the referent of a novel word is isomorphic to learning. We present an alternative model in which referent selection is an online process that is independent of long-term learning. This two timescale approach creates significant power in the developing system. We illustrate this with a dynamic associative model in which referent selection is simulated as dynamic competition between competing referents, and learning is simulated using associative (Hebbian) learning. This model can account for a range of findings including the delay in expressive vocabulary relative to receptive vocabulary, learning under high degrees of referential ambiguity using cross-situational statistics, accelerating (vocabulary explosion) and decelerating (power-law) learning rates, fast-mapping by mutual exclusivity (and differences in bilinguals), improvements in familiar word recognition with development, and correlations between individual differences in speed of processing and learning. Five theoretical points are illustrated. 1) Word learning does not require specialized processes – general association learning buttressed by dynamic competition can account for much of the literature. 2) The processes of recognizing familiar words are not different than those that support novel words (e.g., fast-mapping). 3) Online competition may allow the network (or child) to leverage information available in the task to augment performance or behavior despite what might be relatively slow learning or poor representations. 4) Even associative learning is more complex than previously thought – a major contributor to performance is the pruning of incorrect associations

  20. Autonomous learning in gesture recognition by using lobe component analysis

    Science.gov (United States)

    Lu, Jian; Weng, Juyang

    2007-02-01

    Gesture recognition is a new human-machine interface method implemented by pattern recognition(PR).In order to assure robot safety when gesture is used in robot control, it is required to implement the interface reliably and accurately. Similar with other PR applications, 1) feature selection (or model establishment) and 2) training from samples, affect the performance of gesture recognition largely. For 1), a simple model with 6 feature points at shoulders, elbows, and hands, is established. The gestures to be recognized are restricted to still arm gestures, and the movement of arms is not considered. These restrictions are to reduce the misrecognition, but are not so unreasonable. For 2), a new biological network method, called lobe component analysis(LCA), is used in unsupervised learning. Lobe components, corresponding to high-concentrations in probability of the neuronal input, are orientation selective cells follow Hebbian rule and lateral inhibition. Due to the advantage of LCA method for balanced learning between global and local features, large amount of samples can be used in learning efficiently.

  1. Cerebellar supervised learning revisited: biophysical modeling and degrees-of-freedom control.

    Science.gov (United States)

    Kawato, Mitsuo; Kuroda, Shinya; Schweighofer, Nicolas

    2011-10-01

    The biophysical models of spike-timing-dependent plasticity have explored dynamics with molecular basis for such computational concepts as coincidence detection, synaptic eligibility trace, and Hebbian learning. They overall support different learning algorithms in different brain areas, especially supervised learning in the cerebellum. Because a single spine is physically very small, chemical reactions at it are essentially stochastic, and thus sensitivity-longevity dilemma exists in the synaptic memory. Here, the cascade of excitable and bistable dynamics is proposed to overcome this difficulty. All kinds of learning algorithms in different brain regions confront with difficult generalization problems. For resolution of this issue, the control of the degrees-of-freedom can be realized by changing synchronicity of neural firing. Especially, for cerebellar supervised learning, the triangle closed-loop circuit consisting of Purkinje cells, the inferior olive nucleus, and the cerebellar nucleus is proposed as a circuit to optimally control synchronous firing and degrees-of-freedom in learning. Copyright © 2011 Elsevier Ltd. All rights reserved.

  2. Selection of suitable e-learning approach using TOPSIS technique with best ranked criteria weights

    Science.gov (United States)

    Mohammed, Husam Jasim; Kasim, Maznah Mat; Shaharanee, Izwan Nizal Mohd

    2017-11-01

    This paper compares the performances of four rank-based weighting assessment techniques, Rank Sum (RS), Rank Reciprocal (RR), Rank Exponent (RE), and Rank Order Centroid (ROC) on five identified e-learning criteria to select the best weights method. A total of 35 experts in a public university in Malaysia were asked to rank the criteria and to evaluate five e-learning approaches which include blended learning, flipped classroom, ICT supported face to face learning, synchronous learning, and asynchronous learning. The best ranked criteria weights are defined as weights that have the least total absolute differences with the geometric mean of all weights, were then used to select the most suitable e-learning approach by using TOPSIS method. The results show that RR weights are the best, while flipped classroom approach implementation is the most suitable approach. This paper has developed a decision framework to aid decision makers (DMs) in choosing the most suitable weighting method for solving MCDM problems.

  3. Unsupervised learning in neural networks with short range synapses

    Science.gov (United States)

    Brunnet, L. G.; Agnes, E. J.; Mizusaki, B. E. P.; Erichsen, R., Jr.

    2013-01-01

    Different areas of the brain are involved in specific aspects of the information being processed both in learning and in memory formation. For example, the hippocampus is important in the consolidation of information from short-term memory to long-term memory, while emotional memory seems to be dealt by the amygdala. On the microscopic scale the underlying structures in these areas differ in the kind of neurons involved, in their connectivity, or in their clustering degree but, at this level, learning and memory are attributed to neuronal synapses mediated by longterm potentiation and long-term depression. In this work we explore the properties of a short range synaptic connection network, a nearest neighbor lattice composed mostly by excitatory neurons and a fraction of inhibitory ones. The mechanism of synaptic modification responsible for the emergence of memory is Spike-Timing-Dependent Plasticity (STDP), a Hebbian-like rule, where potentiation/depression is acquired when causal/non-causal spikes happen in a synapse involving two neurons. The system is intended to store and recognize memories associated to spatial external inputs presented as simple geometrical forms. The synaptic modifications are continuously applied to excitatory connections, including a homeostasis rule and STDP. In this work we explore the different scenarios under which a network with short range connections can accomplish the task of storing and recognizing simple connected patterns.

  4. Embedding responses in spontaneous neural activity shaped through sequential learning.

    Directory of Open Access Journals (Sweden)

    Tomoki Kurikawa

    Full Text Available Recent experimental measurements have demonstrated that spontaneous neural activity in the absence of explicit external stimuli has remarkable spatiotemporal structure. This spontaneous activity has also been shown to play a key role in the response to external stimuli. To better understand this role, we proposed a viewpoint, "memories-as-bifurcations," that differs from the traditional "memories-as-attractors" viewpoint. Memory recall from the memories-as-bifurcations viewpoint occurs when the spontaneous neural activity is changed to an appropriate output activity upon application of an input, known as a bifurcation in dynamical systems theory, wherein the input modifies the flow structure of the neural dynamics. Learning, then, is a process that helps create neural dynamical systems such that a target output pattern is generated as an attractor upon a given input. Based on this novel viewpoint, we introduce in this paper an associative memory model with a sequential learning process. Using a simple hebbian-type learning, the model is able to memorize a large number of input/output mappings. The neural dynamics shaped through the learning exhibit different bifurcations to make the requested targets stable upon an increase in the input, and the neural activity in the absence of input shows chaotic dynamics with occasional approaches to the memorized target patterns. These results suggest that these dynamics facilitate the bifurcations to each target attractor upon application of the corresponding input, which thus increases the capacity for learning. This theoretical finding about the behavior of the spontaneous neural activity is consistent with recent experimental observations in which the neural activity without stimuli wanders among patterns evoked by previously applied signals. In addition, the neural networks shaped by learning properly reflect the correlations of input and target-output patterns in a similar manner to those designed in

  5. Criterion learning in rule-based categorization: simulation of neural mechanism and new data.

    Science.gov (United States)

    Helie, Sebastien; Ell, Shawn W; Filoteo, J Vincent; Maddox, W Todd

    2015-04-01

    In perceptual categorization, rule selection consists of selecting one or several stimulus-dimensions to be used to categorize the stimuli (e.g., categorize lines according to their length). Once a rule has been selected, criterion learning consists of defining how stimuli will be grouped using the selected dimension(s) (e.g., if the selected rule is line length, define 'long' and 'short'). Very little is known about the neuroscience of criterion learning, and most existing computational models do not provide a biological mechanism for this process. In this article, we introduce a new model of rule learning called Heterosynaptic Inhibitory Criterion Learning (HICL). HICL includes a biologically-based explanation of criterion learning, and we use new category-learning data to test key aspects of the model. In HICL, rule selective cells in prefrontal cortex modulate stimulus-response associations using pre-synaptic inhibition. Criterion learning is implemented by a new type of heterosynaptic error-driven Hebbian learning at inhibitory synapses that uses feedback to drive cell activation above/below thresholds representing ionic gating mechanisms. The model is used to account for new human categorization data from two experiments showing that: (1) changing rule criterion on a given dimension is easier if irrelevant dimensions are also changing (Experiment 1), and (2) showing that changing the relevant rule dimension and learning a new criterion is more difficult, but also facilitated by a change in the irrelevant dimension (Experiment 2). We conclude with a discussion of some of HICL's implications for future research on rule learning. Copyright © 2015 Elsevier Inc. All rights reserved.

  6. Ranking-based Method for News Stance Detection

    KAUST Repository

    Zhang, Qiang; Yilmaz, Emine; Liang, Shangsong

    2018-01-01

    A valuable step towards news veracity assessment is to understand stance from different information sources, and the process is known as the stance detection. Specifically, the stance detection is to detect four kinds of stances (

  7. Ranking-based Method for News Stance Detection

    KAUST Repository

    Zhang, Qiang

    2018-04-18

    A valuable step towards news veracity assessment is to understand stance from different information sources, and the process is known as the stance detection. Specifically, the stance detection is to detect four kinds of stances (

  8. Rank-based model selection for multiple ions quantum tomography

    International Nuclear Information System (INIS)

    Guţă, Mădălin; Kypraios, Theodore; Dryden, Ian

    2012-01-01

    The statistical analysis of measurement data has become a key component of many quantum engineering experiments. As standard full state tomography becomes unfeasible for large dimensional quantum systems, one needs to exploit prior information and the ‘sparsity’ properties of the experimental state in order to reduce the dimensionality of the estimation problem. In this paper we propose model selection as a general principle for finding the simplest, or most parsimonious explanation of the data, by fitting different models and choosing the estimator with the best trade-off between likelihood fit and model complexity. We apply two well established model selection methods—the Akaike information criterion (AIC) and the Bayesian information criterion (BIC)—two models consisting of states of fixed rank and datasets such as are currently produced in multiple ions experiments. We test the performance of AIC and BIC on randomly chosen low rank states of four ions, and study the dependence of the selected rank with the number of measurement repetitions for one ion states. We then apply the methods to real data from a four ions experiment aimed at creating a Smolin state of rank 4. By applying the two methods together with the Pearson χ 2 test we conclude that the data can be suitably described with a model whose rank is between 7 and 9. Additionally we find that the mean square error of the maximum likelihood estimator for pure states is close to that of the optimal over all possible measurements. (paper)

  9. Nanotechnology strength indicators: international rankings based on US patents

    Science.gov (United States)

    Marinova, Dora; McAleer, Michael

    2003-01-01

    Technological strength indicators (TSIs) based on patent statistics for 1975-2000 are used to analyse patenting of nanotechnology in the USA, and to compile international rankings for the top 12 foreign patenting countries (namely Australia, Canada, France, Germany, Great Britain, Italy, Japan, Korea, the Netherlands, Sweden, Switzerland and Taiwan). As the indicators are not directly observable, various proxy variables are used, namely the technological specialization index for national priorities, patent shares for international presence, citation rate for the contribution of patents to knowledge development and rate of assigned patents for potential commercial benefits. The best performing country is France, followed by Japan and Canada. It is shown that expertise and strength in nanotechnology are not evenly distributed among the technologically advanced countries, with the TSIs revealing different emphases in the development of nanotechnology.

  10. "Environmental Technology Strengths: International Rankings Based on US Patent Data"

    OpenAIRE

    Dora Marinova; Michael McAleer

    2003-01-01

    Patent information has been used by economists and researchers in the field of innovation to analyse current and forecast future technological directions. The recent surge in patenting activities in developed countries reaffirms the strong position of the patent system in a globalised world dominated by market mechanisms. This paper analyses the technological position of the top twelve foreign patenting countries in the USA, namely Australia, Canada, France, Germany, Italy, Japan, Korea, the ...

  11. Image Re-Ranking Based on Topic Diversity.

    Science.gov (United States)

    Qian, Xueming; Lu, Dan; Wang, Yaxiong; Zhu, Li; Tang, Yuan Yan; Wang, Meng

    2017-08-01

    Social media sharing Websites allow users to annotate images with free tags, which significantly contribute to the development of the web image retrieval. Tag-based image search is an important method to find images shared by users in social networks. However, how to make the top ranked result relevant and with diversity is challenging. In this paper, we propose a topic diverse ranking approach for tag-based image retrieval with the consideration of promoting the topic coverage performance. First, we construct a tag graph based on the similarity between each tag. Then, the community detection method is conducted to mine the topic community of each tag. After that, inter-community and intra-community ranking are introduced to obtain the final retrieved results. In the inter-community ranking process, an adaptive random walk model is employed to rank the community based on the multi-information of each topic community. Besides, we build an inverted index structure for images to accelerate the searching process. Experimental results on Flickr data set and NUS-Wide data sets show the effectiveness of the proposed approach.

  12. A BCM theory of meta-plasticity for online self-reorganizing fuzzy-associative learning.

    Science.gov (United States)

    Tan, Javan; Quek, Chai

    2010-06-01

    Self-organizing neurofuzzy approaches have matured in their online learning of fuzzy-associative structures under time-invariant conditions. To maximize their operative value for online reasoning, these self-sustaining mechanisms must also be able to reorganize fuzzy-associative knowledge in real-time dynamic environments. Hence, it is critical to recognize that they would require self-reorganizational skills to rebuild fluid associative structures when their existing organizations fail to respond well to changing circumstances. In this light, while Hebbian theory (Hebb, 1949) is the basic computational framework for associative learning, it is less attractive for time-variant online learning because it suffers from stability limitations that impedes unlearning. Instead, this paper adopts the Bienenstock-Cooper-Munro (BCM) theory of neurological learning via meta-plasticity principles (Bienenstock et al., 1982) that provides for both online associative and dissociative learning. For almost three decades, BCM theory has been shown to effectively brace physiological evidence of synaptic potentiation (association) and depression (dissociation) into a sound mathematical framework for computational learning. This paper proposes an interpretation of the BCM theory of meta-plasticity for an online self-reorganizing fuzzy-associative learning system to realize online-reasoning capabilities. Experimental findings are twofold: 1) the analysis using S&P-500 stock index illustrated that the self-reorganizing approach could follow the trajectory shifts in the time-variant S&P-500 index for about 60 years, and 2) the benchmark profiles showed that the fuzzy-associative approach yielded comparable results with other fuzzy-precision models with similar online objectives.

  13. Learning How to Learn

    DEFF Research Database (Denmark)

    Lauridsen, Karen M.; Lauridsen, Ole

    Ole Lauridsen, Aarhus School of Business and Social Sciences, Aarhus University, Denmark Karen M. Lauridsen, Aarhus School of Business and Social Sciences, Aarhus University, Denmark Learning Styles in Higher Education – Learning How to Learn Applying learning styles (LS) in higher education...... by Constructivist learning theory and current basic knowledge of how the brain learns. The LS concept will thus be placed in a broader learning theoretical context as a strong learning and teaching tool. Participants will be offered the opportunity to have their own LS preferences established before...... teaching leads to positive results and enhanced student learning. However, learning styles should not only be considered a didactic matter for the teacher, but also a tool for the individual students to improve their learning capabilities – not least in contexts where information is not necessarily...

  14. Brain-like associative learning using a nanoscale non-volatile phase change synaptic device array

    Directory of Open Access Journals (Sweden)

    Sukru Burc Eryilmaz

    2014-07-01

    Full Text Available Recent advances in neuroscience together with nanoscale electronic device technology have resulted in huge interests in realizing brain-like computing hardwares using emerging nanoscale memory devices as synaptic elements. Although there has been experimental work that demonstrated the operation of nanoscale synaptic element at the single device level, network level studies have been limited to simulations. In this work, we demonstrate, using experiments, array level associative learning using phase change synaptic devices connected in a grid like configuration similar to the organization of the biological brain. Implementing Hebbian learning with phase change memory cells, the synaptic grid was able to store presented patterns and recall missing patterns in an associative brain-like fashion. We found that the system is robust to device variations, and large variations in cell resistance states can be accommodated by increasing the number of training epochs. We illustrated the tradeoff between variation tolerance of the network and the overall energy consumption, and found that energy consumption is decreased significantly for lower variation tolerance.

  15. Learning to Learn.

    Science.gov (United States)

    Weiss, Helen; Weiss, Martin

    1988-01-01

    The article reviews theories of learning (e.g., stimulus-response, trial and error, operant conditioning, cognitive), considers the role of motivation, and summarizes nine research-supported rules of effective learning. Suggestions are applied to teaching learning strategies to learning-disabled students. (DB)

  16. Learning Styles.

    Science.gov (United States)

    Missouri Univ., Columbia. Coll. of Education.

    Information is provided regarding major learning styles and other factors important to student learning. Several typically asked questions are presented regarding different learning styles (visual, auditory, tactile and kinesthetic, and multisensory learning), associated considerations, determining individuals' learning styles, and appropriate…

  17. A Theory of How Columns in the Neocortex Enable Learning the Structure of the World

    Directory of Open Access Journals (Sweden)

    Jeff Hawkins

    2017-10-01

    Full Text Available Neocortical regions are organized into columns and layers. Connections between layers run mostly perpendicular to the surface suggesting a columnar functional organization. Some layers have long-range excitatory lateral connections suggesting interactions between columns. Similar patterns of connectivity exist in all regions but their exact role remain a mystery. In this paper, we propose a network model composed of columns and layers that performs robust object learning and recognition. Each column integrates its changing input over time to learn complete predictive models of observed objects. Excitatory lateral connections across columns allow the network to more rapidly infer objects based on the partial knowledge of adjacent columns. Because columns integrate input over time and space, the network learns models of complex objects that extend well beyond the receptive field of individual cells. Our network model introduces a new feature to cortical columns. We propose that a representation of location relative to the object being sensed is calculated within the sub-granular layers of each column. The location signal is provided as an input to the network, where it is combined with sensory data. Our model contains two layers and one or more columns. Simulations show that using Hebbian-like learning rules small single-column networks can learn to recognize hundreds of objects, with each object containing tens of features. Multi-column networks recognize objects with significantly fewer movements of the sensory receptors. Given the ubiquity of columnar and laminar connectivity patterns throughout the neocortex, we propose that columns and regions have more powerful recognition and modeling capabilities than previously assumed.

  18. Learning Networks, Networked Learning

    NARCIS (Netherlands)

    Sloep, Peter; Berlanga, Adriana

    2010-01-01

    Sloep, P. B., & Berlanga, A. J. (2011). Learning Networks, Networked Learning [Redes de Aprendizaje, Aprendizaje en Red]. Comunicar, XIX(37), 55-63. Retrieved from http://dx.doi.org/10.3916/C37-2011-02-05

  19. Ciliates learn to diagnose and correct classical error syndromes in mating strategies.

    Science.gov (United States)

    Clark, Kevin B

    2013-01-01

    Preconjugal ciliates learn classical repetition error-correction codes to safeguard mating messages and replies from corruption by "rivals" and local ambient noise. Because individual cells behave as memory channels with Szilárd engine attributes, these coding schemes also might be used to limit, diagnose, and correct mating-signal errors due to noisy intracellular information processing. The present study, therefore, assessed whether heterotrich ciliates effect fault-tolerant signal planning and execution by modifying engine performance, and consequently entropy content of codes, during mock cell-cell communication. Socially meaningful serial vibrations emitted from an ambiguous artificial source initiated ciliate behavioral signaling performances known to advertise mating fitness with varying courtship strategies. Microbes, employing calcium-dependent Hebbian-like decision making, learned to diagnose then correct error syndromes by recursively matching Boltzmann entropies between signal planning and execution stages via "power" or "refrigeration" cycles. All eight serial contraction and reversal strategies incurred errors in entropy magnitude by the execution stage of processing. Absolute errors, however, subtended expected threshold values for single bit-flip errors in three-bit replies, indicating coding schemes protected information content throughout signal production. Ciliate preparedness for vibrations selectively and significantly affected the magnitude and valence of Szilárd engine performance during modal and non-modal strategy corrective cycles. But entropy fidelity for all replies mainly improved across learning trials as refinements in engine efficiency. Fidelity neared maximum levels for only modal signals coded in resilient three-bit repetition error-correction sequences. Together, these findings demonstrate microbes can elevate survival/reproductive success by learning to implement classical fault-tolerant information processing in social

  20. Concurrent TMS to the primary motor cortex augments slow motor learning

    Science.gov (United States)

    Narayana, Shalini; Zhang, Wei; Rogers, William; Strickland, Casey; Franklin, Crystal; Lancaster, Jack L.; Fox, Peter T.

    2013-01-01

    Transcranial magnetic stimulation (TMS) has shown promise as a treatment tool, with one FDA approved use. While TMS alone is able to up- (or down-) regulate a targeted neural system, we argue that TMS applied as an adjuvant is more effective for repetitive physical, behavioral and cognitive therapies, that is, therapies which are designed to alter the network properties of neural systems through Hebbian learning. We tested this hypothesis in the context of a slow motor learning paradigm. Healthy right-handed individuals were assigned to receive 5 Hz TMS (TMS group) or sham TMS (sham group) to the right primary motor cortex (M1) as they performed daily motor practice of a digit sequence task with their non-dominant hand for 4 weeks. Resting cerebral blood flow (CBF) was measured by H215O PET at baseline and after 4 weeks of practice. Sequence performance was measured daily as the number of correct sequences performed, and modeled using a hyperbolic function. Sequence performance increased significantly at 4 weeks relative to baseline in both groups. The TMS group had a significant additional improvement in performance, specifically, in the rate of skill acquisition. In both groups, an improvement in sequence timing and transfer of skills to non-trained motor domains was also found. Compared to the sham group, the TMS group demonstrated increases in resting CBF specifically in regions known to mediate skill learning namely, the M1, cingulate cortex, putamen, hippocampus, and cerebellum. These results indicate that TMS applied concomitantly augments behavioral effects of motor practice, with corresponding neural plasticity in motor sequence learning network. These findings are the first demonstration of the behavioral and neural enhancing effects of TMS on slow motor practice and have direct application in neurorehabilitation where TMS could be applied in conjunction with physical therapy. PMID:23867557

  1. Learning of Precise Spike Times with Homeostatic Membrane Potential Dependent Synaptic Plasticity.

    Directory of Open Access Journals (Sweden)

    Christian Albers

    Full Text Available Precise spatio-temporal patterns of neuronal action potentials underly e.g. sensory representations and control of muscle activities. However, it is not known how the synaptic efficacies in the neuronal networks of the brain adapt such that they can reliably generate spikes at specific points in time. Existing activity-dependent plasticity rules like Spike-Timing-Dependent Plasticity are agnostic to the goal of learning spike times. On the other hand, the existing formal and supervised learning algorithms perform a temporally precise comparison of projected activity with the target, but there is no known biologically plausible implementation of this comparison. Here, we propose a simple and local unsupervised synaptic plasticity mechanism that is derived from the requirement of a balanced membrane potential. Since the relevant signal for synaptic change is the postsynaptic voltage rather than spike times, we call the plasticity rule Membrane Potential Dependent Plasticity (MPDP. Combining our plasticity mechanism with spike after-hyperpolarization causes a sensitivity of synaptic change to pre- and postsynaptic spike times which can reproduce Hebbian spike timing dependent plasticity for inhibitory synapses as was found in experiments. In addition, the sensitivity of MPDP to the time course of the voltage when generating a spike allows MPDP to distinguish between weak (spurious and strong (teacher spikes, which therefore provides a neuronal basis for the comparison of actual and target activity. For spatio-temporal input spike patterns our conceptually simple plasticity rule achieves a surprisingly high storage capacity for spike associations. The sensitivity of the MPDP to the subthreshold membrane potential during training allows robust memory retrieval after learning even in the presence of activity corrupted by noise. We propose that MPDP represents a biophysically plausible mechanism to learn temporal target activity patterns.

  2. Learning of Precise Spike Times with Homeostatic Membrane Potential Dependent Synaptic Plasticity.

    Science.gov (United States)

    Albers, Christian; Westkott, Maren; Pawelzik, Klaus

    2016-01-01

    Precise spatio-temporal patterns of neuronal action potentials underly e.g. sensory representations and control of muscle activities. However, it is not known how the synaptic efficacies in the neuronal networks of the brain adapt such that they can reliably generate spikes at specific points in time. Existing activity-dependent plasticity rules like Spike-Timing-Dependent Plasticity are agnostic to the goal of learning spike times. On the other hand, the existing formal and supervised learning algorithms perform a temporally precise comparison of projected activity with the target, but there is no known biologically plausible implementation of this comparison. Here, we propose a simple and local unsupervised synaptic plasticity mechanism that is derived from the requirement of a balanced membrane potential. Since the relevant signal for synaptic change is the postsynaptic voltage rather than spike times, we call the plasticity rule Membrane Potential Dependent Plasticity (MPDP). Combining our plasticity mechanism with spike after-hyperpolarization causes a sensitivity of synaptic change to pre- and postsynaptic spike times which can reproduce Hebbian spike timing dependent plasticity for inhibitory synapses as was found in experiments. In addition, the sensitivity of MPDP to the time course of the voltage when generating a spike allows MPDP to distinguish between weak (spurious) and strong (teacher) spikes, which therefore provides a neuronal basis for the comparison of actual and target activity. For spatio-temporal input spike patterns our conceptually simple plasticity rule achieves a surprisingly high storage capacity for spike associations. The sensitivity of the MPDP to the subthreshold membrane potential during training allows robust memory retrieval after learning even in the presence of activity corrupted by noise. We propose that MPDP represents a biophysically plausible mechanism to learn temporal target activity patterns.

  3. Ciliates learn to diagnose and correct classical error syndromes in mating strategies

    Directory of Open Access Journals (Sweden)

    Kevin Bradley Clark

    2013-08-01

    Full Text Available Preconjugal ciliates learn classical repetition error-correction codes to safeguard mating messages and replies from corruption by rivals and local ambient noise. Because individual cells behave as memory channels with Szilárd engine attributes, these coding schemes also might be used to limit, diagnose, and correct mating-signal errors due to noisy intracellular information processing. The present study, therefore, assessed whether heterotrich ciliates effect fault-tolerant signal planning and execution by modifying engine performance, and consequently entropy content of codes, during mock cell-cell communication. Socially meaningful serial vibrations emitted from an ambiguous artificial source initiated ciliate behavioral signaling performances known to advertise mating fitness with varying courtship strategies. Microbes, employing calcium-dependent Hebbian-like decision making, learned to diagnose then correct error syndromes by recursively matching Boltzmann entropies between signal planning and execution stages via power or refrigeration cycles. All eight serial contraction and reversal strategies incurred errors in entropy magnitude by the execution stage of processing. Absolute errors, however, subtended expected threshold values for single bit-flip errors in three-bit replies, indicating coding schemes protected information content throughout signal production. Ciliate preparedness for vibrations selectively and significantly affected the magnitude and valence of Szilárd engine performance during modal and nonmodal strategy corrective cycles. But entropy fidelity for all replies mainly improved across learning trials as refinements in engine efficiency. Fidelity neared maximum levels for only modal signals coded in resilient three-bit repetition error-correction sequences. Together, these findings demonstrate microbes can elevate survival/reproductive success by learning to implement classical fault-tolerant information processing in

  4. Learning Problems

    Science.gov (United States)

    ... Staying Safe Videos for Educators Search English Español Learning Problems KidsHealth / For Kids / Learning Problems What's in ... for how to make it better. What Are Learning Disabilities? Learning disabilities aren't contagious, but they ...

  5. Learning about Learning

    Science.gov (United States)

    Siegler, Robert S.

    2004-01-01

    The field of children's learning was thriving when the Merrill-Palmer Quarterly was launched; the field later went into eclipse and now is in the midst of a resurgence. This commentary examines reasons for these trends, and describes the emerging field of children's learning. In particular, the new field is seen as differing from the old in its…

  6. Learning to Learn Differently

    Science.gov (United States)

    Olsen, Trude Høgvold; Glad, Tone; Filstad, Cathrine

    2018-01-01

    Purpose: This paper aims to investigate whether the formal and informal learning patterns of community health-care nurses changed in the wake of a reform that altered their work by introducing new patient groups, and to explore whether conditions in the new workplaces facilitated or impeded shifts in learning patterns. Design/methodology/approach:…

  7. An investigation of Hebbian phase sequences as assembly graphs

    Directory of Open Access Journals (Sweden)

    Daniel Gomes Almeida Filho

    2014-04-01

    Full Text Available Hebb proposed that synapses between neurons that fire synchronously are strengthened, forming cell assemblies and phase sequences. The former, on a shorter scale, are ensembles of synchronized cells that function transiently as a closed processing system; the latter, on a larger scale, correspond to the sequential activation of cell assemblies able to represent percepts and behaviors. Nowadays, the recording of large neuronal populations allows for the detection of multiple cell assemblies. Within Hebb’s theory, the next logical step is the analysis of phase sequences. Here we detected phase sequences as consecutive assembly activation patterns, and then analyzed their graph attributes in relation to behavior. We investigated action potentials recorded from the adult rat hippocampus and neocortex before, during and after novel object exploration (experimental periods. Within assembly graphs, each assembly corresponded to a node, and each edge corresponded to the temporal sequence of consecutive node activations. The sum of all assembly activations was proportional to firing rates, but the activity of individual assemblies was not. Assembly repertoire was stable across experimental periods, suggesting that novel experience does not create new assemblies in the adult rat. Assembly graph attributes, on the other hand, varied significantly across behavioral states and experimental periods, and were separable enough to correctly classify experimental periods (Naïve Bayes classifier; maximum AUROCs ranging from 0.55 to 0.99 and behavioral states (waking, slow wave sleep, and rapid eye movement sleep; maximum AUROCs s ranging from 0.64 to 0.98. Our findings agree with Hebb’s view that assemblies correspond to primitive building blocks of representation, nearly unchanged in the adult, while phase sequences are labile across behavioral states and change after novel experience. The results are compatible with a role for phase sequences in behavior and cognition.

  8. Distance Learning

    National Research Council Canada - National Science Library

    Braddock, Joseph

    1997-01-01

    A study reviewing the existing Army Distance Learning Plan (ADLP) and current Distance Learning practices, with a focus on the Army's training and educational challenges and the benefits of applying Distance Learning techniques...

  9. Blended learning

    DEFF Research Database (Denmark)

    Dau, Susanne

    2016-01-01

    Blended Learning has been implemented, evaluated and researched for the last decades within different educational areas and levels. Blended learning has been coupled with different epistemological understandings and learning theories, but the fundamental character and dimensions of learning...... in blended learning are still insufficient. Moreover, blended learning is a misleading concept described as learning, despite the fact that it fundamentally is an instructional and didactic approach (Oliver & Trigwell, 2005) addressing the learning environment (Inglis, Palipoana, Trenhom & Ward, 2011......) instead of the learning processes behind. Much of the existing research within the field seems to miss this perspective. The consequence is a lack of acknowledgement of the driven forces behind the context and the instructional design limiting the knowledge foundation of learning in blended learning. Thus...

  10. Self-organizing adaptive map: autonomous learning of curves and surfaces from point samples.

    Science.gov (United States)

    Piastra, Marco

    2013-05-01

    Competitive Hebbian Learning (CHL) (Martinetz, 1993) is a simple and elegant method for estimating the topology of a manifold from point samples. The method has been adopted in a number of self-organizing networks described in the literature and has given rise to related studies in the fields of geometry and computational topology. Recent results from these fields have shown that a faithful reconstruction can be obtained using the CHL method only for curves and surfaces. Within these limitations, these findings constitute a basis for defining a CHL-based, growing self-organizing network that produces a faithful reconstruction of an input manifold. The SOAM (Self-Organizing Adaptive Map) algorithm adapts its local structure autonomously in such a way that it can match the features of the manifold being learned. The adaptation process is driven by the defects arising when the network structure is inadequate, which cause a growth in the density of units. Regions of the network undergo a phase transition and change their behavior whenever a simple, local condition of topological regularity is met. The phase transition is eventually completed across the entire structure and the adaptation process terminates. In specific conditions, the structure thus obtained is homeomorphic to the input manifold. During the adaptation process, the network also has the capability to focus on the acquisition of input point samples in critical regions, with a substantial increase in efficiency. The behavior of the network has been assessed experimentally with typical data sets for surface reconstruction, including suboptimal conditions, e.g. with undersampling and noise. Copyright © 2012 Elsevier Ltd. All rights reserved.

  11. Distributed Bayesian Computation and Self-Organized Learning in Sheets of Spiking Neurons with Local Lateral Inhibition.

    Directory of Open Access Journals (Sweden)

    Johannes Bill

    Full Text Available During the last decade, Bayesian probability theory has emerged as a framework in cognitive science and neuroscience for describing perception, reasoning and learning of mammals. However, our understanding of how probabilistic computations could be organized in the brain, and how the observed connectivity structure of cortical microcircuits supports these calculations, is rudimentary at best. In this study, we investigate statistical inference and self-organized learning in a spatially extended spiking network model, that accommodates both local competitive and large-scale associative aspects of neural information processing, under a unified Bayesian account. Specifically, we show how the spiking dynamics of a recurrent network with lateral excitation and local inhibition in response to distributed spiking input, can be understood as sampling from a variational posterior distribution of a well-defined implicit probabilistic model. This interpretation further permits a rigorous analytical treatment of experience-dependent plasticity on the network level. Using machine learning theory, we derive update rules for neuron and synapse parameters which equate with Hebbian synaptic and homeostatic intrinsic plasticity rules in a neural implementation. In computer simulations, we demonstrate that the interplay of these plasticity rules leads to the emergence of probabilistic local experts that form distributed assemblies of similarly tuned cells communicating through lateral excitatory connections. The resulting sparse distributed spike code of a well-adapted network carries compressed information on salient input features combined with prior experience on correlations among them. Our theory predicts that the emergence of such efficient representations benefits from network architectures in which the range of local inhibition matches the spatial extent of pyramidal cells that share common afferent input.

  12. Distributed Bayesian Computation and Self-Organized Learning in Sheets of Spiking Neurons with Local Lateral Inhibition

    Science.gov (United States)

    Bill, Johannes; Buesing, Lars; Habenschuss, Stefan; Nessler, Bernhard; Maass, Wolfgang; Legenstein, Robert

    2015-01-01

    During the last decade, Bayesian probability theory has emerged as a framework in cognitive science and neuroscience for describing perception, reasoning and learning of mammals. However, our understanding of how probabilistic computations could be organized in the brain, and how the observed connectivity structure of cortical microcircuits supports these calculations, is rudimentary at best. In this study, we investigate statistical inference and self-organized learning in a spatially extended spiking network model, that accommodates both local competitive and large-scale associative aspects of neural information processing, under a unified Bayesian account. Specifically, we show how the spiking dynamics of a recurrent network with lateral excitation and local inhibition in response to distributed spiking input, can be understood as sampling from a variational posterior distribution of a well-defined implicit probabilistic model. This interpretation further permits a rigorous analytical treatment of experience-dependent plasticity on the network level. Using machine learning theory, we derive update rules for neuron and synapse parameters which equate with Hebbian synaptic and homeostatic intrinsic plasticity rules in a neural implementation. In computer simulations, we demonstrate that the interplay of these plasticity rules leads to the emergence of probabilistic local experts that form distributed assemblies of similarly tuned cells communicating through lateral excitatory connections. The resulting sparse distributed spike code of a well-adapted network carries compressed information on salient input features combined with prior experience on correlations among them. Our theory predicts that the emergence of such efficient representations benefits from network architectures in which the range of local inhibition matches the spatial extent of pyramidal cells that share common afferent input. PMID:26284370

  13. Learn, how to learn

    Science.gov (United States)

    Narayanan, M.

    2002-12-01

    Ernest L. Boyer, in his 1990 book, "Scholarship Reconsidered: Priorities of the Professorate" cites some ground breaking studies and offers a new paradigm that identifies the need to recognize the growing conversation about teaching, scholarship and research in the Universities. The use of `ACORN' model suggested by Hawkins and Winter to conquer and mastering change, may offer some helpful hints for the novice professor, whose primary objective might be to teach students to `learn how to learn'. Action : It is possible to effectively change things only when a teaching professor actually tries out a new idea. Communication : Changes are successful only when the new ideas effectively communicated and implemented. Ownership : Support for change is extremely important and is critical. Only strong commitment for accepting changes demonstrates genuine leadership. Reflection : Feedback helps towards thoughtful evaluation of the changes implemented. Only reflection can provide a tool for continuous improvement. Nurture : Implemented changes deliver results only when nurtured and promoted with necessary support systems, documentation and infrastructures. Inspired by the ACORN model, the author experimented on implementing certain principles of `Total Quality Management' in the classroom. The author believes that observing the following twenty principles would indeed help the student learners how to learn, on their own towards achieving the goal of `Lifelong Learning'. The author uses an acronym : QUOTES : Quality Underscored On Teaching Excellence Strategy, to describe his methods for improving classroom teacher-learner participation. 1. Break down all barriers. 2. Create consistency of purpose with a plan. 3. Adopt the new philosophy of quality. 4. Establish high Standards. 5. Establish Targets / Goals. 6. Reduce dependence on Lectures. 7. Employ Modern Methods. 8. Control the Process. 9. Organize to reach goals. 10. Prevention vs. Correction. 11. Periodic Improvements. 12

  14. Intentional Learning Vs Incidental Learning

    OpenAIRE

    Shahbaz Ahmed

    2017-01-01

    This study is conducted to demonstrate the knowledge of intentional learning and incidental learning. Hypothesis of this experiment is intentional learning is better than incidental learning, participants were demonstrated and were asked to learn the 10 non sense syllables in a specific sequence from the colored cards in the end they were asked to recall the background color of each card instead of non-sense syllables. Independent variables of the experiment are the colored cards containing n...

  15. Posthuman learning

    DEFF Research Database (Denmark)

    Hasse, Cathrine

    This book shall explore the concept of learning from the new perspective of the posthuman. The vast majority of cognitive, behavioral and part of the constructionist learning theories operate with an autonomous individual who learn in a world of separate objects. Technology is (if mentioned at all......) understood as separate from the individual learner and perceived as tools. Learning theory has in general not been acknowledging materiality in their theorizing about what learning is. A new posthuman learning theory is needed to keep up with the transformations of human learning resulting from new...... technological experiences. One definition of learning is that it is a relatively permanent change in behavior as the result of experience. During the first half of the twentieth century, two theoretical approaches dominated the domain of learning theory: the schools of thought commonly known as behaviorism...

  16. Learning e-Learning

    Directory of Open Access Journals (Sweden)

    Gabriel ZAMFIR

    2009-01-01

    Full Text Available What You Understand Is What Your Cognitive Integrates. Scientific research develops, as a native environment, knowledge. This environment consists of two interdependent divisions: theory and technology. First division occurs as a recursive research, while the second one becomes an application of the research activity. Over time, theories integrate methodologies and technology extends as infrastructure. The engine of this environment is learning, as the human activity of knowledge work. The threshold term of this model is the concepts map; it is based on Bloom’ taxonomy for the cognitive domain and highlights the notion of software scaffolding which is grounded in Vygotsky’s Social Development Theory with its major theme, Zone of Proximal Development. This article is designed as a conceptual paper, which analyzes specific structures of this type of educational research: the model reflects a foundation for a theory and finally, the theory evolves as groundwork for a system. The outcomes of this kind of approach are the examples, which are, theoretically, learning outcomes, and practically exist as educational objects, so-called e-learning.

  17. Blended Learning

    NARCIS (Netherlands)

    Van der Baaren, John

    2009-01-01

    Van der Baaren, J. (2009). Blended Learning. Presentation given at the Mini symposium 'Blended Learning the way to go?'. November, 5, 2009, The Hague, The Netherlands: Netherlands Defence Academy (NDLA).

  18. Interface learning

    DEFF Research Database (Denmark)

    Thorhauge, Sally

    2014-01-01

    "Interface learning - New goals for museum and upper secondary school collaboration" investigates and analyzes the learning that takes place when museums and upper secondary schools in Denmark work together in local partnerships to develop and carry out school-related, museum-based coursework...... for students. The research focuses on the learning that the students experience in the interface of the two learning environments: The formal learning environment of the upper secondary school and the informal learning environment of the museum. Focus is also on the learning that the teachers and museum...... professionals experience as a result of their collaboration. The dissertation demonstrates how a given partnership’s collaboration affects the students’ learning experiences when they are doing the coursework. The dissertation presents findings that museum-school partnerships can use in order to develop...

  19. Learning Disabilities

    Science.gov (United States)

    ... books. While his friends were meeting for pickup soccer games after school, he was back home in ... sometimes thought to contribute to learning disabilities. Poor nutrition early in life also may lead to learning ...

  20. Workplace learning

    DEFF Research Database (Denmark)

    Warring, Niels

    2005-01-01

    In November 2004 the Research Consortium on workplace learning under Learning Lab Denmark arranged the international conference “Workplace Learning – from the learner’s perspective”. The conference’s aim was to bring together researchers from different countries and institutions to explore...... and discuss recent developments in our understanding of workplace and work-related learning. The conference had nearly 100 participants with 59 papers presented, and among these five have been selected for presentation is this Special Issue....

  1. Children's Learning

    Science.gov (United States)

    Siegler, Robert S.

    2005-01-01

    A new field of children's learning is emerging. This new field differs from the old in recognizing that children's learning includes active as well as passive mechanisms and qualitative as well as quantitative changes. Children's learning involves substantial variability of representations and strategies within individual children as well as…

  2. Blended Learning

    Science.gov (United States)

    Imbriale, Ryan

    2013-01-01

    Teachers always have been and always will be the essential element in the classroom. They can create magic inside four walls, but they have never been able to create learning environments outside the classroom like they can today, thanks to blended learning. Blended learning allows students and teachers to break free of the isolation of the…

  3. Transformative Learning

    Science.gov (United States)

    Wang, Victor C. X.; Cranton, Patricia

    2011-01-01

    The theory of transformative learning has been explored by different theorists and scholars. However, few scholars have made an attempt to make a comparison between transformative learning and Confucianism or between transformative learning and andragogy. The authors of this article address these comparisons to develop new and different insights…

  4. Blended Learning

    OpenAIRE

    Bauerová, Andrea

    2013-01-01

    This thesis is focused on a new approach of education called blended learning. The history and developement of Blended Learning is described in the first part. Then the methods and tools of Blended Learning are evaluated and compared to the traditional methods of education. At the final part an efficient developement of the educational programs is emphasized.

  5. Just Learning

    Science.gov (United States)

    Larsen-Freeman, Diane

    2017-01-01

    In this "First Person Singular" essay, the author describes her education, teaching experience, and interest in understanding the learning of language. Anyone reading this essay will not be surprised to learn that the author's questions about language learning and optimal teaching methods were only met with further questions, and no…

  6. Learning Networks for Lifelong Learning

    OpenAIRE

    Sloep, Peter

    2009-01-01

    Presentation in a seminar organized by Christopher Hoadley at Penn State University, October 2004.Contains general introduction into the Learning Network Programme and a demonstration of the Netlogo Simulation of a Learning Network.

  7. Learning organisations

    Directory of Open Access Journals (Sweden)

    Sabina Jelenc Krašovec

    2000-12-01

    Full Text Available A vast array of economical, social, political, cultural and other factors influences the transformed role of learning and education in the society, as well as the functioning of local community and its social and communication patterns. The influences which are manifested as global problems can only be successfully solved on the level of local community. Analogously with the society in general, there is a great need of transforming a local community into a learning, flexible and interconnected environment which takes into account different interests, wishes and needs regarding learning and being active. The fundamental answer to changes is the strategy of lifelong learning and education which requires reorganisation of all walks of life (work, free time, family, mass media, culture, sport, education and transforming of organisations into learning organisations. With learning society based on networks of knowledge individuals are turning into learning individuals, and organisations into learning organisations; people who learn take the responsibility of their progress, learning denotes partnership among learning people, teachers, parents, employers and local community, so that they work together to achieve better results.

  8. Learning Opportunities for Group Learning

    Science.gov (United States)

    Gil, Alfonso J.; Mataveli, Mara

    2017-01-01

    Purpose: This paper aims to analyse the impact of organizational learning culture and learning facilitators in group learning. Design/methodology/approach: This study was conducted using a survey method applied to a statistically representative sample of employees from Rioja wine companies in Spain. A model was tested using a structural equation…

  9. Mimetic Learning

    Directory of Open Access Journals (Sweden)

    Christoph Wulf

    2008-03-01

    Full Text Available Mimetic learning, learning by imitation, constitutes one of the most important forms of learning. Mimetic learning does not, however, just denote mere imitation or copying: Rather, it is a process by which the act of relating to other persons and worlds in a mimetic way leads to an en-hancement of one’s own world view, action, and behaviour. Mimetic learning is productive; it is related to the body, and it establishes a connection between the individual and the world as well as other persons; it creates practical knowledge, which is what makes it constitutive of social, artistic, and practical action. Mimetic learning is cultural learning, and as such it is crucial to teaching and education (Wulf, 2004; 2005.

  10. Deep learning

    CERN Document Server

    Goodfellow, Ian; Courville, Aaron

    2016-01-01

    Deep learning is a form of machine learning that enables computers to learn from experience and understand the world in terms of a hierarchy of concepts. Because the computer gathers knowledge from experience, there is no need for a human computer operator to formally specify all the knowledge that the computer needs. The hierarchy of concepts allows the computer to learn complicated concepts by building them out of simpler ones; a graph of these hierarchies would be many layers deep. This book introduces a broad range of topics in deep learning. The text offers mathematical and conceptual background, covering relevant concepts in linear algebra, probability theory and information theory, numerical computation, and machine learning. It describes deep learning techniques used by practitioners in industry, including deep feedforward networks, regularization, optimization algorithms, convolutional networks, sequence modeling, and practical methodology; and it surveys such applications as natural language proces...

  11. Learning Disabilities and ADHD

    Science.gov (United States)

    ... of illnesses and disabilities Learning disabilities and ADHD Learning disabilities and ADHD Learning disabilities affect how you ... ADHD. Learning disabilities Attention deficit hyperactivity disorder (ADHD) Learning disabilities top Having a learning disability does not ...

  12. Informal learning.

    Science.gov (United States)

    Callanan, Maureen; Cervantes, Christi; Loomis, Molly

    2011-11-01

    We consider research and theory relevant to the notion of informal learning. Beginning with historical and definitional issues, we argue that learning happens not just in schools or in school-aged children. Many theorists have contrasted informal learning with formal learning. Moving beyond this dichotomy, and away from a focus on where learning occurs, we discuss five dimensions of informal learning that are drawn from the literature: (1) non-didactive, (2) highly socially collaborative, (3) embedded in meaningful activity, (4) initiated by learner's interest or choice, and (5) removed from external assessment. We consider these dimensions in the context of four sample domains: learning a first language, learning about the mind and emotions within families and communities, learning about science in family conversations and museum settings, and workplace learning. Finally, we conclude by considering convergences and divergences across the different literatures and suggesting areas for future research. WIREs Cogni Sci 2011 2 646-655 DOI: 10.1002/wcs.143 For further resources related to this article, please visit the WIREs website. Copyright © 2011 John Wiley & Sons, Ltd.

  13. Machine Learning

    CERN Multimedia

    CERN. Geneva

    2017-01-01

    Machine learning, which builds on ideas in computer science, statistics, and optimization, focuses on developing algorithms to identify patterns and regularities in data, and using these learned patterns to make predictions on new observations. Boosted by its industrial and commercial applications, the field of machine learning is quickly evolving and expanding. Recent advances have seen great success in the realms of computer vision, natural language processing, and broadly in data science. Many of these techniques have already been applied in particle physics, for instance for particle identification, detector monitoring, and the optimization of computer resources. Modern machine learning approaches, such as deep learning, are only just beginning to be applied to the analysis of High Energy Physics data to approach more and more complex problems. These classes will review the framework behind machine learning and discuss recent developments in the field.

  14. Doing learning

    DEFF Research Database (Denmark)

    Mathiasen, John Bang; Koch, Christian

    2014-01-01

    Purpose: To investigate how learning occurs in a systems development project, using a company developing wind turbine control systems in collaboration with customers as case. Design/methodology/approach: Dewey’s approach to learning is used, emphasising reciprocity between the individual...... learning processes and that the interchanges between materiality and systems developers block the learning processes due to a customer with imprecise demands and unclear system specifications. In the four cases discussed, learning does occur however. Research limitations/implications: A qualitative study...... focusing on individual systems developers gives limited insight into whether the learning processes found would occur in other systems development processes. Practical implications: Managers should ensure that constitutive means, such as specifications, are available, and that they are sufficiently...

  15. Metric learning

    CERN Document Server

    Bellet, Aurelien; Sebban, Marc

    2015-01-01

    Similarity between objects plays an important role in both human cognitive processes and artificial systems for recognition and categorization. How to appropriately measure such similarities for a given task is crucial to the performance of many machine learning, pattern recognition and data mining methods. This book is devoted to metric learning, a set of techniques to automatically learn similarity and distance functions from data that has attracted a lot of interest in machine learning and related fields in the past ten years. In this book, we provide a thorough review of the metric learnin

  16. Learning to learn in MOOCs

    DEFF Research Database (Denmark)

    Milligan, Sandra; Ringtved, Ulla Lunde

    This paper outlines one way of understanding what it is about learning in MOOCs that is so distinctive, and explores the implications for the design of MOOCs. It draws on an ongoing research study into the nature of learning in MOOCs at the University of Melbourne.......This paper outlines one way of understanding what it is about learning in MOOCs that is so distinctive, and explores the implications for the design of MOOCs. It draws on an ongoing research study into the nature of learning in MOOCs at the University of Melbourne....

  17. Learning Cultures

    DEFF Research Database (Denmark)

    Rasmussen, Lauge Baungaard

    1998-01-01

    the article present different concepts and modelsof learning. It discuss some strutural tendenciesof developing environmental management systemsand point out alternatives to increasing formalization of rules.......the article present different concepts and modelsof learning. It discuss some strutural tendenciesof developing environmental management systemsand point out alternatives to increasing formalization of rules....

  18. Blended learning

    DEFF Research Database (Denmark)

    Staugaard, Hans Jørgen

    2012-01-01

    Forsøg på at indkredse begrebet blended learning i forbindelse med forberedelsen af projekt FlexVid.......Forsøg på at indkredse begrebet blended learning i forbindelse med forberedelsen af projekt FlexVid....

  19. Reflective Learning

    African Journals Online (AJOL)

    dell

    The main intent of this study was to identify the impact of using learning log as a learning strategy on the academic performance of university students. Second year psychology students were included as subjects of this study. In the beginning of the study, the students were divided into two: experimental group (N = 60) and ...

  20. Perceptual learning.

    Science.gov (United States)

    Seitz, Aaron R

    2017-07-10

    Perceptual learning refers to how experience can change the way we perceive sights, sounds, smells, tastes, and touch. Examples abound: music training improves our ability to discern tones; experience with food and wines can refine our pallet (and unfortunately more quickly empty our wallet), and with years of training radiologists learn to save lives by discerning subtle details of images that escape the notice of untrained viewers. We often take perceptual learning for granted, but it has a profound impact on how we perceive the world. In this Primer, I will explain how perceptual learning is transformative in guiding our perceptual processes, how research into perceptual learning provides insight into fundamental mechanisms of learning and brain processes, and how knowledge of perceptual learning can be used to develop more effective training approaches for those requiring expert perceptual skills or those in need of perceptual rehabilitation (such as individuals with poor vision). I will make a case that perceptual learning is ubiquitous, scientifically interesting, and has substantial practical utility to us all. Copyright © 2017. Published by Elsevier Ltd.

  1. Pervasive Learning

    DEFF Research Database (Denmark)

    Helms, Niels Henrik; Larsen, Lasse Juel

    2009-01-01

    , it is not a specific place where you can access scarce information. Pervasive or ubiquitous communication opens up for taking the organizing and design of learning landscapes a step further. Furthermore it calls for theoretical developments, which can open up for a deeper understanding of the relationship between...... emerging contexts, design of contexts and learning....

  2. Flipped Learning

    DEFF Research Database (Denmark)

    Holmboe, Peter; Hachmann, Roland

    I FLIPPED LEARNING – FLIP MED VIDEO kan du læse om, hvordan du som underviser kommer godt i gang med at implementere video i undervisning, der har afsæt i tankerne omkring flipped learning. Bogen indeholder fire dele: I Del 1 fokuserer vi på det metarefleksive i at tænke video ind i undervisningen...

  3. Flipped Learning

    DEFF Research Database (Denmark)

    Hachmann, Roland; Holmboe, Peter

    arbejde med faglige problemstillinger gennem problembaserede og undersøgende didaktiske designs. Flipped Learning er dermed andet og mere end at distribuere digitale materialer til eleverne forud for undervisning. Flipped Learning er i lige så høj grad et syn på, hvordan undervisning med digitale medier...

  4. Situating learning

    DEFF Research Database (Denmark)

    Ribeiro, Gustavo; Georg, Susse; Finchman, Rob

    2004-01-01

    This paper looks at learning experiences in South Africa and Thailand by highlighting the role of context and culture in the learning process. The authors are based at Danish and South African higher education institutions and have contributed to DUCED's TFS programme in the positions of overall...

  5. Embodied Learning

    Science.gov (United States)

    Stolz, Steven A.

    2015-01-01

    This article argues that psychological discourse fails miserably to provide an account of learning that can explain how humans come to understand, particularly understanding that has been grasped meaningfully. Part of the problem with psychological approaches to learning is that they are disconnected from the integral role embodiment plays in how…

  6. Distance learning

    Directory of Open Access Journals (Sweden)

    Katarina Pucelj

    2006-12-01

    Full Text Available I would like to underline the role and importance of knowledge, which is acquired by individuals as a result of a learning process and experience. I have established that a form of learning, such as distance learning definitely contributes to a higher learning quality and leads to innovative, dynamic and knowledgebased society. Knowledge and skills enable individuals to cope with and manage changes, solve problems and also create new knowledge. Traditional learning practices face new circumstances, new and modern technologies appear, which enable quick and quality-oriented knowledge implementation. The centre of learning process at distance learning is to increase the quality of life of citizens, their competitiveness on the workforce market and ensure higher economic growth. Intellectual capital is the one, which represents the biggest capital of each society and knowledge is the key factor for succes of everybody, who are fully aware of this. Flexibility, openness and willingness of people to follow new IT solutions form suitable environment for developing and deciding to take up distance learning.

  7. Legitimate Learning.

    Science.gov (United States)

    Stevenson, John

    1997-01-01

    What is considered legitimate learning is culturally and contextually specific, depending on what values are involved. Different values are engaged depending on whether legitimate learning is considered transformation of the individual in relation to self, in relation to society, or in relation to the workplace. (SK)

  8. Machine Learning.

    Science.gov (United States)

    Kirrane, Diane E.

    1990-01-01

    As scientists seek to develop machines that can "learn," that is, solve problems by imitating the human brain, a gold mine of information on the processes of human learning is being discovered, expert systems are being improved, and human-machine interactions are being enhanced. (SK)

  9. Blended Learning as Transformational Institutional Learning

    Science.gov (United States)

    VanDerLinden, Kim

    2014-01-01

    This chapter reviews institutional approaches to blended learning and the ways in which institutions support faculty in the intentional redesign of courses to produce optimal learning. The chapter positions blended learning as a strategic opportunity to engage in organizational learning.

  10. PageRank-based identification of signaling crosstalk from transcriptomics data: the case of Arabidopsis thaliana.

    Science.gov (United States)

    Omranian, Nooshin; Mueller-Roeber, Bernd; Nikoloski, Zoran

    2012-04-01

    The levels of cellular organization, from gene transcription to translation to protein-protein interaction and metabolism, operate via tightly regulated mutual interactions, facilitating organismal adaptability and various stress responses. Characterizing the mutual interactions between genes, transcription factors, and proteins involved in signaling, termed crosstalk, is therefore crucial for understanding and controlling cells' functionality. We aim at using high-throughput transcriptomics data to discover previously unknown links between signaling networks. We propose and analyze a novel method for crosstalk identification which relies on transcriptomics data and overcomes the lack of complete information for signaling pathways in Arabidopsis thaliana. Our method first employs a network-based transformation of the results from the statistical analysis of differential gene expression in given groups of experiments under different signal-inducing conditions. The stationary distribution of a random walk (similar to the PageRank algorithm) on the constructed network is then used to determine the putative transcripts interrelating different signaling pathways. With the help of the proposed method, we analyze a transcriptomics data set including experiments from four different stresses/signals: nitrate, sulfur, iron, and hormones. We identified promising gene candidates, downstream of the transcription factors (TFs), associated to signaling crosstalk, which were validated through literature mining. In addition, we conduct a comparative analysis with the only other available method in this field which used a biclustering-based approach. Surprisingly, the biclustering-based approach fails to robustly identify any candidate genes involved in the crosstalk of the analyzed signals. We demonstrate that our proposed method is more robust in identifying gene candidates involved downstream of the signaling crosstalk for species for which large transcriptomics data sets, normalized with the same techniques, are available. Moreover, unlike approaches based on biclustering, our approach does not rely on any hidden parameters.

  11. Rank-based permutation approaches for non-parametric factorial designs.

    Science.gov (United States)

    Umlauft, Maria; Konietschke, Frank; Pauly, Markus

    2017-11-01

    Inference methods for null hypotheses formulated in terms of distribution functions in general non-parametric factorial designs are studied. The methods can be applied to continuous, ordinal or even ordered categorical data in a unified way, and are based only on ranks. In this set-up Wald-type statistics and ANOVA-type statistics are the current state of the art. The first method is asymptotically exact but a rather liberal statistical testing procedure for small to moderate sample size, while the latter is only an approximation which does not possess the correct asymptotic α level under the null. To bridge these gaps, a novel permutation approach is proposed which can be seen as a flexible generalization of the Kruskal-Wallis test to all kinds of factorial designs with independent observations. It is proven that the permutation principle is asymptotically correct while keeping its finite exactness property when data are exchangeable. The results of extensive simulation studies foster these theoretical findings. A real data set exemplifies its applicability. © 2017 The British Psychological Society.

  12. Dual channel rank-based intensity weighting for quantitative co-localization of microscopy images

    LENUS (Irish Health Repository)

    Singan, Vasanth R

    2011-10-21

    Abstract Background Accurate quantitative co-localization is a key parameter in the context of understanding the spatial co-ordination of molecules and therefore their function in cells. Existing co-localization algorithms consider either the presence of co-occurring pixels or correlations of intensity in regions of interest. Depending on the image source, and the algorithm selected, the co-localization coefficients determined can be highly variable, and often inaccurate. Furthermore, this choice of whether co-occurrence or correlation is the best approach for quantifying co-localization remains controversial. Results We have developed a novel algorithm to quantify co-localization that improves on and addresses the major shortcomings of existing co-localization measures. This algorithm uses a non-parametric ranking of pixel intensities in each channel, and the difference in ranks of co-localizing pixel positions in the two channels is used to weight the coefficient. This weighting is applied to co-occurring pixels thereby efficiently combining both co-occurrence and correlation. Tests with synthetic data sets show that the algorithm is sensitive to both co-occurrence and correlation at varying levels of intensity. Analysis of biological data sets demonstrate that this new algorithm offers high sensitivity, and that it is capable of detecting subtle changes in co-localization, exemplified by studies on a well characterized cargo protein that moves through the secretory pathway of cells. Conclusions This algorithm provides a novel way to efficiently combine co-occurrence and correlation components in biological images, thereby generating an accurate measure of co-localization. This approach of rank weighting of intensities also eliminates the need for manual thresholding of the image, which is often a cause of error in co-localization quantification. We envisage that this tool will facilitate the quantitative analysis of a wide range of biological data sets, including high resolution confocal images, live cell time-lapse recordings, and high-throughput screening data sets.

  13. A simple rank-based Markov chain with self-organized criticality

    Czech Academy of Sciences Publication Activity Database

    Swart, Jan M.

    2017-01-01

    Roč. 23, č. 1 (2017), s. 87-102 ISSN 1024-2953 R&D Projects: GA ČR GAP201/12/2613; GA ČR(CZ) GA15-08819S Institutional support: RVO:67985556 Keywords : self-reinforcement * self-organized criticality * canyon Subject RIV: BA - General Mathematics OBOR OECD: Pure mathematics Impact factor: 0.397, year: 2016 http://library.utia.cas.cz/separaty/2017/SI/swart-0476009.pdf

  14. Rank-Based miRNA Signatures for Early Cancer Detection

    Directory of Open Access Journals (Sweden)

    Mario Lauria

    2014-01-01

    Full Text Available We describe a new signature definition and analysis method to be used as biomarker for early cancer detection. Our new approach is based on the construction of a reference map of transcriptional signatures of both healthy and cancer affected individuals using circulating miRNA from a large number of subjects. Once such a map is available, the diagnosis for a new patient can be performed by observing the relative position on the map of his/her transcriptional signature. To demonstrate its efficacy for this specific application we report the results of the application of our method to published datasets of circulating miRNA, and we quantify its performance compared to current state-of-the-art methods. A number of additional features make this method an ideal candidate for large-scale use, for example, as a mass screening tool for early cancer detection or for at-home diagnostics. Specifically, our method is minimally invasive (because it works well with circulating miRNA, it is robust with respect to lab-to-lab protocol variability and batch effects (it requires that only the relative ranking of expression value of miRNA in a profile be accurate not their absolute values, and it is scalable to a large number of subjects. Finally we discuss the need for HPC capability in a widespread application of our or similar methods.

  15. Discovering urban mobility patterns with PageRank based traffic modeling and prediction

    Science.gov (United States)

    Wang, Minjie; Yang, Su; Sun, Yi; Gao, Jun

    2017-11-01

    Urban transportation system can be viewed as complex network with time-varying traffic flows as links to connect adjacent regions as networked nodes. By computing urban traffic evolution on such temporal complex network with PageRank, it is found that for most regions, there exists a linear relation between the traffic congestion measure at present time and the PageRank value of the last time. Since the PageRank measure of a region does result from the mutual interactions of the whole network, it implies that the traffic state of a local region does not evolve independently but is affected by the evolution of the whole network. As a result, the PageRank values can act as signatures in predicting upcoming traffic congestions. We observe the aforementioned laws experimentally based on the trajectory data of 12000 taxies in Beijing city for one month.

  16. Wilcoxon signed-rank-based technique for the pulse-shape analysis of HPGe detectors

    Science.gov (United States)

    Martín, S.; Quintana, B.; Barrientos, D.

    2016-07-01

    The characterization of the electric response of segmented-contact high-purity germanium detectors requires scanning systems capable of accurately associating each pulse with the position of the interaction that generated it. This process requires an algorithm sensitive to changes above the electronic noise in the pulse shapes produced at different positions, depending on the resolution of the Ge crystal. In this work, a pulse-shape comparison technique based on the Wilcoxon signed-rank test has been developed. It provides a method to distinguish pulses coming from different interaction points in the germanium crystal. Therefore, this technique is a necessary step for building a reliable pulse-shape database that can be used later for the determination of the position of interaction for γ-ray tracking spectrometry devices such as AGATA, GRETA or GERDA. The method was validated by comparison with a χ2 test using simulated and experimental pulses corresponding to a Broad Energy germanium detector (BEGe).

  17. Wilcoxon signed-rank-based technique for the pulse-shape analysis of HPGe detectors

    Energy Technology Data Exchange (ETDEWEB)

    Martín, S., E-mail: sergiomr@usal.es; Quintana, B.; Barrientos, D.

    2016-07-01

    The characterization of the electric response of segmented-contact high-purity germanium detectors requires scanning systems capable of accurately associating each pulse with the position of the interaction that generated it. This process requires an algorithm sensitive to changes above the electronic noise in the pulse shapes produced at different positions, depending on the resolution of the Ge crystal. In this work, a pulse-shape comparison technique based on the Wilcoxon signed-rank test has been developed. It provides a method to distinguish pulses coming from different interaction points in the germanium crystal. Therefore, this technique is a necessary step for building a reliable pulse-shape database that can be used later for the determination of the position of interaction for γ-ray tracking spectrometry devices such as AGATA, GRETA or GERDA. The method was validated by comparison with a χ{sup 2} test using simulated and experimental pulses corresponding to a Broad Energy germanium detector (BEGe).

  18. Wilcoxon signed-rank-based technique for the pulse-shape analysis of HPGe detectors

    International Nuclear Information System (INIS)

    Martín, S.; Quintana, B.; Barrientos, D.

    2016-01-01

    The characterization of the electric response of segmented-contact high-purity germanium detectors requires scanning systems capable of accurately associating each pulse with the position of the interaction that generated it. This process requires an algorithm sensitive to changes above the electronic noise in the pulse shapes produced at different positions, depending on the resolution of the Ge crystal. In this work, a pulse-shape comparison technique based on the Wilcoxon signed-rank test has been developed. It provides a method to distinguish pulses coming from different interaction points in the germanium crystal. Therefore, this technique is a necessary step for building a reliable pulse-shape database that can be used later for the determination of the position of interaction for γ-ray tracking spectrometry devices such as AGATA, GRETA or GERDA. The method was validated by comparison with a χ"2 test using simulated and experimental pulses corresponding to a Broad Energy germanium detector (BEGe).

  19. "Learned Helplessness" or "Learned Incompetence"?

    Science.gov (United States)

    Sergent, Justine; Lambert, Wallace E.

    Studies in the past have shown that reinforcements independent of the subjects actions may induce a feeling of helplessness. Most experiments on learned helplessness have led researchers to believe that uncontrollability (non-contingency of feedback upon response) was the determining feature of learned helplessness, although in most studies…

  20. Teacher learning as workplace learning

    NARCIS (Netherlands)

    Imants, J.; Van Veen, K.

    2010-01-01

    Against the background of increasing attention in teacher professional development programs for situating teacher learning in the workplace, an overview is given of what is known in general and in educational workplace learning literature on the characteristics and conditions of the workplace.

  1. Learning, Learning Organisations and the Global Enterprise

    Science.gov (United States)

    Manikutty, Sankaran

    2009-01-01

    The steadily increasing degree of globalisation of enterprises implies development of many skills, among which the skills to learn are among the most important. Learning takes place at the individual level, but collective learning and organisational learning are also important. Learning styles of individuals are different and learning styles are…

  2. Can machine learning explain human learning?

    NARCIS (Netherlands)

    Vahdat, M.; Oneto, L.; Anguita, D.; Funk, M.; Rauterberg, G.W.M.

    2016-01-01

    Learning Analytics (LA) has a major interest in exploring and understanding the learning process of humans and, for this purpose, benefits from both Cognitive Science, which studies how humans learn, and Machine Learning, which studies how algorithms learn from data. Usually, Machine Learning is

  3. Evaluation of learning materials

    DEFF Research Database (Denmark)

    Bundsgaard, Jeppe; Hansen, Thomas Illum

    2011-01-01

    This paper presents a holistic framework for evaluating learning materials and designs for learning. A holistic evaluation comprises investigations of the potential learning potential, the actualized learning potential, and the actual learning. Each aspect is explained and exemplified through...

  4. Learning Spaces

    CERN Document Server

    Falmagne, Jean-Claude

    2011-01-01

    Learning spaces offer a rigorous mathematical foundation for practical systems of educational technology. Learning spaces generalize partially ordered sets and are special cases of knowledge spaces. The various structures are investigated from the standpoints of combinatorial properties and stochastic processes. Leaning spaces have become the essential structures to be used in assessing students' competence of various topics. A practical example is offered by ALEKS, a Web-based, artificially intelligent assessment and learning system in mathematics and other scholarly fields. At the heart of A

  5. Supportive Learning: Linear Learning and Collaborative Learning

    Science.gov (United States)

    Lee, Bih Ni; Abdullah, Sopiah; Kiu, Su Na

    2016-01-01

    This is a conceptual paper which is trying to look at the educational technology is not limited to high technology. However, electronic educational technology, also known as e-learning, has become an important part of today's society, which consists of a wide variety of approaches to digitization, components and methods of delivery. In the…

  6. Learning to learn: self-managed learning

    Directory of Open Access Journals (Sweden)

    Jesús Miranda Izquierdo

    2006-09-01

    Full Text Available Thi is article analyzes the potentialities and weaknesses that non directive Pedagogy presents, an example of the so called self managed pedagogy, whose postulates are good to analyze for the contributions that this position can make to the search of new ways of learning.

  7. Machine Learning

    Energy Technology Data Exchange (ETDEWEB)

    Chikkagoudar, Satish; Chatterjee, Samrat; Thomas, Dennis G.; Carroll, Thomas E.; Muller, George

    2017-04-21

    The absence of a robust and unified theory of cyber dynamics presents challenges and opportunities for using machine learning based data-driven approaches to further the understanding of the behavior of such complex systems. Analysts can also use machine learning approaches to gain operational insights. In order to be operationally beneficial, cybersecurity machine learning based models need to have the ability to: (1) represent a real-world system, (2) infer system properties, and (3) learn and adapt based on expert knowledge and observations. Probabilistic models and Probabilistic graphical models provide these necessary properties and are further explored in this chapter. Bayesian Networks and Hidden Markov Models are introduced as an example of a widely used data driven classification/modeling strategy.

  8. Learning Leadership

    DEFF Research Database (Denmark)

    Hertel, Frederik; Fast, Alf Michael

    2018-01-01

    Is leadership a result of inheritance or is it something one learns during formal learning in e.g. business schools? This is the essential question addressed in this article. The article is based on a case study involving a new leader in charge of a group of profession practitioners. The leader...... promotes his leadership as a profession comparable to the professions of practitioners. This promotion implies that leadership is something one can and probably must learn during formal learning. The practitioners on the other hand reject this comprehension of leadership and long for a fellow practitioner...... to lead the organization. While asked they are unable to describe how, where and when they think a practitioner develops leadership skills necessary for leading fellows. In the following we will start analysing the case in order to comprehend and discuss both the professional leaders and the practitioners...

  9. Group learning

    DEFF Research Database (Denmark)

    Pimentel, Ricardo; Noguira, Eloy Eros da Silva; Elkjær, Bente

    The article presents a study that aims at the apprehension of the group learning in a top management team composed by teachers in a Brazilian Waldorf school whose management is collective. After deciding to extend the school, they had problems recruiting teachers who were already trained based...... on the Steiner´s ideas, which created practical problems for conducting management activities. The research seeks to understand how that group of teachers collectively manage the school, facing the lack of resources, a significant heterogeneity in the relationships, and the conflicts and contradictions......, and they are interrelated to the group learning as the construction, maintenance and reconstruction of the intelligibility of practices. From this perspective, it can be said that learning is a practice and not an exceptional phenomenon. Building, maintaining and rebuilding the intelligibility is the group learning...

  10. Learning Disabilities

    Science.gov (United States)

    ... NICHD) See all related organizations Publications Problemas de aprendizaje Order NINDS Publications Patient Organizations CHADD - Children and ... NICHD) See all related organizations Publications Problemas de aprendizaje Order NINDS Publications Definition Learning disabilities are disorders ...

  11. Reflective Learning

    African Journals Online (AJOL)

    dell

    The experimental group students used learning log on a weekly basis while the control group did not. ... The term “memory” in psychology usually denotes an interest in the retention ... activities that contribute to information being remembered.

  12. Support Vector Hazards Machine: A Counting Process Framework for Learning Risk Scores for Censored Outcomes.

    Science.gov (United States)

    Wang, Yuanjia; Chen, Tianle; Zeng, Donglin

    2016-01-01

    Learning risk scores to predict dichotomous or continuous outcomes using machine learning approaches has been studied extensively. However, how to learn risk scores for time-to-event outcomes subject to right censoring has received little attention until recently. Existing approaches rely on inverse probability weighting or rank-based regression, which may be inefficient. In this paper, we develop a new support vector hazards machine (SVHM) approach to predict censored outcomes. Our method is based on predicting the counting process associated with the time-to-event outcomes among subjects at risk via a series of support vector machines. Introducing counting processes to represent time-to-event data leads to a connection between support vector machines in supervised learning and hazards regression in standard survival analysis. To account for different at risk populations at observed event times, a time-varying offset is used in estimating risk scores. The resulting optimization is a convex quadratic programming problem that can easily incorporate non-linearity using kernel trick. We demonstrate an interesting link from the profiled empirical risk function of SVHM to the Cox partial likelihood. We then formally show that SVHM is optimal in discriminating covariate-specific hazard function from population average hazard function, and establish the consistency and learning rate of the predicted risk using the estimated risk scores. Simulation studies show improved prediction accuracy of the event times using SVHM compared to existing machine learning methods and standard conventional approaches. Finally, we analyze two real world biomedical study data where we use clinical markers and neuroimaging biomarkers to predict age-at-onset of a disease, and demonstrate superiority of SVHM in distinguishing high risk versus low risk subjects.

  13. Interorganizational learning systems

    DEFF Research Database (Denmark)

    Hjalager, Anne-Mette

    1999-01-01

    The occurrence of organizational and interorganizational learning processes is not only the result of management endeavors. Industry structures and market related issues have substantial spill-over effects. The article reviews literature, and it establishes a learning model in which elements from...... organizational environments are included into a systematic conceptual framework. The model allows four types of learning to be identified: P-learning (professional/craft systems learning), T-learning (technology embedded learning), D-learning (dualistic learning systems, where part of the labor force is exclude...... from learning), and S-learning (learning in social networks or clans). The situation related to service industries illustrates the typology....

  14. Lifelong Learning

    DEFF Research Database (Denmark)

    Krogh, Lone; Jensen, Annie Aarup

    2010-01-01

    Master education for adults has become a strategy for Lifelong Learning among many well-educated people in Denmark. This type of master education is part of the ‘parallel education system' in Denmark. As one of the first Danish universities who offered this type of Master education, Aalborg...... the intended as well as the unintended effects (personal and professional) of the master education. The data have been gathered among graduates from a specific master education, Master in Learning Processes, and the paper will draw on results from a quantitative survey based on a questionnaire answered by 120...

  15. Learning SPARQL

    CERN Document Server

    DuCharme, Bob

    2011-01-01

    Get hands-on experience with SPARQL, the RDF query language that's become a key component of the semantic web. With this concise book, you will learn how to use the latest version of this W3C standard to retrieve and manipulate the increasing amount of public and private data available via SPARQL endpoints. Several open source and commercial tools already support SPARQL, and this introduction gets you started right away. Begin with how to write and run simple SPARQL 1.1 queries, then dive into the language's powerful features and capabilities for manipulating the data you retrieve. Learn wha

  16. Deep Learning in Open Source Learning Streams

    DEFF Research Database (Denmark)

    Kjærgaard, Thomas

    2016-01-01

    This chapter presents research on deep learning in a digital learning environment and raises the question if digital instructional designs can catalyze deeper learning than traditional classroom teaching. As a theoretical point of departure the notion of ‘situated learning’ is utilized...... and contrasted to the notion of functionalistic learning in a digital context. The mechanism that enables deep learning in this context is ‘The Open Source Learning Stream’. ‘The Open Source Learning Stream’ is the notion of sharing ‘learning instances’ in a digital space (discussion board, Facebook group......, unistructural, multistructural or relational learning. The research concludes that ‘The Open Source Learning Stream’ can catalyze deep learning and that there are four types of ‘Open Source Learning streams’; individual/ asynchronous, individual/synchronous, shared/asynchronous and shared...

  17. Mastering machine learning with scikit-learn

    CERN Document Server

    Hackeling, Gavin

    2014-01-01

    If you are a software developer who wants to learn how machine learning models work and how to apply them effectively, this book is for you. Familiarity with machine learning fundamentals and Python will be helpful, but is not essential.

  18. Transforming learning?

    Science.gov (United States)

    1999-09-01

    A new Learning and Skills Council for post-16 learning is the latest proposal from the UK Government in its attempt to ensure a highly skilled workforce for the next century. Other aims will be to reduce the variability in standards of the existing post-16 system, coordination and coherence between further education and training, and a reduction in the duplication and layers in contracting and funding. The proposals include: a national Learning and Skills Council, with 40-50 local Learning and Skills Councils to develop local plans; a strengthened strategic role for business in education and training, influencing a budget of #5bn a radical new youth programme entitled `Connexions', with dedicated personal advisors for young people; greater cooperation between sixth forms and colleges; and the establishment of an independent inspectorate covering all work-related learning and training, to include a new role for Ofsted in inspecting the provision for 16-19 year-olds in schools and colleges. It is hoped that this programme will build on the successes of the previous systems and that savings of at least #50m can be achieved through streamlining and the reduction in bureaucracy. The intentions are set out in a White Paper, Learning to Succeed, which is available from the Stationery Office and bookshops, as well as on the website www.dfee.gov.uk/post16. Published in addition to the White Paper was `School Sixth form funding: a consultation paper' (available from DfEE publications, Prolog, PO Box 5050, Sherwood Park, Annesley, Nottingham NG15 0DJ) and `Transition plan for the post-16 education and training and for local delivery of support for small firms' (available from Trevor Tucknutt, TECSOP Division, Level 3, Department for Education and Employment, Moorfoot, Sheffield S1 4PQ). The deadline for comments on both the sixth form consultation document and the White Paper is 15 October 1999. Almost simultaneously with the announcement of the above proposals came the

  19. Deepening Learning through Learning-by-Inventing

    OpenAIRE

    Apiola, Mikko; Tedre, Matti

    2013-01-01

    It has been shown that deep approaches to learning, intrinsic motivation, and self-regulated learning have strong positive effects on learning. How those pedagogical theories can be integrated in computing curricula is, however, still lacking empirically grounded analyses. This study integrated, in a robotics-based programming class, a method of learning-by-inventing, and studied its qualitative effects on students’ learning through 144 interviews. Five findings were related with learning the...

  20. Learning and Behavior

    Science.gov (United States)

    ... List About PPMD Events News Login By Area Learning & Behavior Attention, Listening & Learning Autism Spectrum Disorder (ASD) ... Care Guidelines ❯ By Area ❯ Learning & Behavior Share Print Learning & Behavior Facts to Remember People with Duchenne may ...

  1. Learning via Query Synthesis

    KAUST Repository

    Alabdulmohsin, Ibrahim

    2017-01-01

    Active learning is a subfield of machine learning that has been successfully used in many applications. One of the main branches of active learning is query synthe- sis, where the learning agent constructs artificial queries from scratch in order

  2. Managing Learning for Performance.

    Science.gov (United States)

    Kuchinke, K. Peter

    1995-01-01

    Presents findings of organizational learning literature that could substantiate claims of learning organization proponents. Examines four learning processes and their contribution to performance-based learning management: knowledge acquisition, information distribution, information interpretation, and organizational memory. (SK)

  3. Learning Object Repositories

    Science.gov (United States)

    Lehman, Rosemary

    2007-01-01

    This chapter looks at the development and nature of learning objects, meta-tagging standards and taxonomies, learning object repositories, learning object repository characteristics, and types of learning object repositories, with type examples. (Contains 1 table.)

  4. Blocking in Category Learning

    OpenAIRE

    Bott, Lewis; Hoffman, Aaron B.; Murphy, Gregory L.

    2007-01-01

    Many theories of category learning assume that learning is driven by a need to minimize classification error. When there is no classification error, therefore, learning of individual features should be negligible. We tested this hypothesis by conducting three category learning experiments adapted from an associative learning blocking paradigm. Contrary to an error-driven account of learning, participants learned a wide range of information when they learned about categories, and blocking effe...

  5. Learned Helplessness

    Science.gov (United States)

    Hooker, Carol E.

    1976-01-01

    Learned helplessness--the belief that a person's actions have no influence on the outcome of an event--is similar in many respects to the crisis state and depression. The author shows how this impaired social and psychological functioning occurs and identifies techniques that the social worker can use to prevent it. (Author)

  6. Learning Disabilities.

    Science.gov (United States)

    Neuwirth, Sharyn

    This booklet uses hypothetical case examples to illustrate the definition, causal theories, and specific types of learning disabilities (LD). The cognitive and language performance of students with LD is compared to standard developmental milestones, and common approaches to the identification and education of children with LD are outlined.…

  7. Learning Together

    Science.gov (United States)

    Kaufman, Sherry

    2014-01-01

    In spring 2012, Sherry Kaufman, a consultant at Francis W. Parker School in Chicago, was asked to support kindergarten teachers in deepening their practice of constructivism and exploring the Reggio Emilia approach to early childhood education. Central to such an approach is the belief that all learning is socially constructed through interaction…

  8. Learning Mongoid

    CERN Document Server

    Rege, Gautam

    2013-01-01

    A step-by-step tutorial with focused examples that will help you build scalable, high performance Rails web applications with Mongoid.If you are an application developer who wants to learn how to use Mongoid in a Rails application, this book will be great for you. You are expected to be familiar with MongoDB and Ruby.

  9. Learning Lichens

    Science.gov (United States)

    Thorne, Sarah

    2017-01-01

    The lichen is an ideal subject for student study because it is omnipresent in school yards, easily collected and observed year-round, a pioneer of evolution on land, and a bioindicator of air pollution. After doing fieldwork on this unusual composite organism as an apprentice with a team of lichenologists, Sarah Thorne developed Learning Lichens.…

  10. Learning Ionic

    CERN Document Server

    Ravulavaru, Arvind

    2015-01-01

    This book is intended for those who want to learn how to build hybrid mobile applications using Ionic. It is also ideal for people who want to explore theming for Ionic apps. Prior knowledge of AngularJS is essential to complete this book successfully.

  11. Supervised Learning

    Science.gov (United States)

    Rokach, Lior; Maimon, Oded

    This chapter summarizes the fundamental aspects of supervised methods. The chapter provides an overview of concepts from various interrelated fields used in subsequent chapters. It presents basic definitions and arguments from the supervised machine learning literature and considers various issues, such as performance evaluation techniques and challenges for data mining tasks.

  12. Learning Analytics

    Directory of Open Access Journals (Sweden)

    Erik Duval

    2012-06-01

    Full Text Available This paper provides a brief introduction to the domain of ‘learning analytics’. We first explain the background and idea behind the concept. Then we give a brief overview of current research issues. We briefly list some more controversial issues before concluding.

  13. Learning Ansible

    CERN Document Server

    Mohaan, Madhurranjan

    2014-01-01

    If you want to learn how to use Ansible to automate an infrastructure, either from scratch or to augment your current tooling with Ansible, then this is the book for you. It has plenty of practical examples to help you get to grips with Ansible.

  14. Learning Physics

    International Nuclear Information System (INIS)

    Cohen, E.

    2005-01-01

    Full Text:The issue of Teaching physics vs Learning physics in our institutions of higher learning will be discussed. Physics is taught mainly by frontal lectures an old (and proven) method. The great advancements of the Information Age are introduced by exposing the students to vast amounts of computerized information and directing them to numerical problem solving by interacting with the computer. These modern methods have several drawbacks: 1. Students get the impression of easy material acquisition while in fact it becomes superficial. 2. There is little integration of topics that are taught in different courses. 3. Insufficient interest is built among undergraduate students to pursue studies that involve deeper thinking and independent research (namely, studies towards a doctoral degree). Learning physics is a formative process in the education of physicists, natural scientists and engineers. It must be based on discussions and exchange of ideas among the students, since understanding the studied material means being able to explain it to a colleague. Some universities in the US initiated programs of learning physics by creating an environment in which small groups of students are engaged in discussing material, jointly solving problems and jointly conducting simulated experiments. This is done under the supervision of a mentor. Suggestions for implementing this method in Israel will be discussed

  15. Learning Gene Regulatory Networks Computationally from Gene Expression Data Using Weighted Consensus

    KAUST Repository

    Fujii, Chisato

    2015-04-16

    Gene regulatory networks analyze the relationships between genes allowing us to un- derstand the gene regulatory interactions in systems biology. Gene expression data from the microarray experiments is used to obtain the gene regulatory networks. How- ever, the microarray data is discrete, noisy and non-linear which makes learning the networks a challenging problem and existing gene network inference methods do not give consistent results. Current state-of-the-art study uses the average-ranking-based consensus method to combine and average the ranked predictions from individual methods. However each individual method has an equal contribution to the consen- sus prediction. We have developed a linear programming-based consensus approach which uses learned weights from linear programming among individual methods such that the methods have di↵erent weights depending on their performance. Our result reveals that assigning di↵erent weights to individual methods rather than giving them equal weights improves the performance of the consensus. The linear programming- based consensus method is evaluated and it had the best performance on in silico and Saccharomyces cerevisiae networks, and the second best on the Escherichia coli network outperformed by Inferelator Pipeline method which gives inconsistent results across a wide range of microarray data sets.

  16. Influences of Formal Learning, Personal Learning Orientation, and Supportive Learning Environment on Informal Learning

    Science.gov (United States)

    Choi, Woojae; Jacobs, Ronald L.

    2011-01-01

    While workplace learning includes formal and informal learning, the relationship between the two has been overlooked, because they have been viewed as separate entities. This study investigated the effects of formal learning, personal learning orientation, and supportive learning environment on informal learning among 203 middle managers in Korean…

  17. From learning objects to learning activities

    DEFF Research Database (Denmark)

    Dalsgaard, Christian

    2005-01-01

    This paper discusses and questions the current metadata standards for learning objects from a pedagogical point of view. From a social constructivist approach, the paper discusses how learning objects can support problem based, self-governed learning activities. In order to support this approach......, it is argued that it is necessary to focus on learning activities rather than on learning objects. Further, it is argued that descriptions of learning objectives and learning activities should be separated from learning objects. The paper presents a new conception of learning objects which supports problem...... based, self-governed activities. Further, a new way of thinking pedagogy into learning objects is introduced. It is argued that a lack of pedagogical thinking in learning objects is not solved through pedagogical metadata. Instead, the paper suggests the concept of references as an alternative...

  18. How we learn

    DEFF Research Database (Denmark)

    Illeris, Knud

    How We Learn, deals with the fundamental issues of the processes of learning, critically assessing different types of learning and obstacles to learning. It also considers a broad range of other important questions in relation to learning such as: modern research into learning and brain functions......, self-perception, motivation and competence development, teaching, intelligence and learning style, learning in relation to gender and life age. The book provides a comprehensive introduction to both traditional learning theory and the newest international research into learning processes, while...... at the same time being an innovative contribution to a new and more holistic understanding of learning including discussion on school-based learning, net-based learning, workplace learning and educational politics. How We Learn examines all the key factors that help to create a holistic understanding of what...

  19. Using Learning Games to Meet Learning Objectives

    DEFF Research Database (Denmark)

    Henriksen, Thomas Duus

    2013-01-01

    This paper addresses the question on how learning games can be used to meet with the different levels in Bloom’s and the SOLO taxonomy, which are commonly used for evaluating the learning outcome of educational activities. The paper discusses the quality of game-based learning outcomes based on a...... on a case study of the learning game 6Styles....

  20. Still to Learn from Vicarious Learning

    Science.gov (United States)

    Mayes, J. T.

    2015-01-01

    The term "vicarious learning" was introduced in the 1960s by Bandura, who demonstrated how learning can occur through observing the behaviour of others. Such social learning is effective without the need for the observer to experience feedback directly. More than twenty years later a series of studies on vicarious learning was undertaken…

  1. Learning Effectiveness of a Strategic Learning Course

    Science.gov (United States)

    Burchard, Melinda S.; Swerdzewski, Peter

    2009-01-01

    The effectiveness of a postsecondary strategic learning course for improving metacognitive awareness and regulation was evaluated through systematic program assessment. The course emphasized students' awareness of personal learning through the study of learning theory and through practical application of specific learning strategies. Students…

  2. Social Media and Seamless Learning: Lessons Learned

    Science.gov (United States)

    Panke, Stefanie; Kohls, Christian; Gaiser, Birgit

    2017-01-01

    The paper discusses best practice approaches and metrics for evaluation that support seamless learning with social media. We draw upon the theoretical frameworks of social learning theory, transfer learning (bricolage), and educational design patterns to elaborate upon different ideas for ways in which social media can support seamless learning.…

  3. Deep Learning

    DEFF Research Database (Denmark)

    Jensen, Morten Bornø; Bahnsen, Chris Holmberg; Nasrollahi, Kamal

    2018-01-01

    I løbet af de sidste 10 år er kunstige neurale netværk gået fra at være en støvet, udstødt tekno-logi til at spille en hovedrolle i udviklingen af kunstig intelligens. Dette fænomen kaldes deep learning og er inspireret af hjernens opbygning.......I løbet af de sidste 10 år er kunstige neurale netværk gået fra at være en støvet, udstødt tekno-logi til at spille en hovedrolle i udviklingen af kunstig intelligens. Dette fænomen kaldes deep learning og er inspireret af hjernens opbygning....

  4. Learning Java

    CERN Document Server

    Niemeyer, Patrick

    2005-01-01

    Version 5.0 of the Java 2 Standard Edition SDK is the most important upgrade since Java first appeared a decade ago. With Java 5.0, you'll not only find substantial changes in the platform, but to the language itself-something that developers of Java took five years to complete. The main goal of Java 5.0 is to make it easier for you to develop safe, powerful code, but none of these improvements makes Java any easier to learn, even if you've programmed with Java for years. And that means our bestselling hands-on tutorial takes on even greater significance. Learning Java is the most widely sou

  5. Learning Raspbian

    CERN Document Server

    Harrington, William

    2015-01-01

    This book is intended for developers who have worked with the Raspberry Pi and who want to learn how to make the most of the Raspbian operating system and their Raspberry Pi. Whether you are a beginner to the Raspberry Pi or a seasoned expert, this book will make you familiar with the Raspbian operating system and teach you how to get your Raspberry Pi up and running.

  6. Guided discovery learning in geometry learning

    Science.gov (United States)

    Khasanah, V. N.; Usodo, B.; Subanti, S.

    2018-03-01

    Geometry is a part of the mathematics that must be learned in school. The purpose of this research was to determine the effect of Guided Discovery Learning (GDL) toward geometry learning achievement. This research had conducted at junior high school in Sukoharjo on academic years 2016/2017. Data collection was done based on student’s work test and documentation. Hypothesis testing used two ways analysis of variance (ANOVA) with unequal cells. The results of this research that GDL gave positive effect towards mathematics learning achievement. GDL gave better mathematics learning achievement than direct learning. There was no difference of mathematics learning achievement between male and female. There was no an interaction between sex differences and learning models toward student’s mathematics learning achievement. GDL can be used to improve students’ mathematics learning achievement in geometry.

  7. Learning to Learn Together with CSCL Tools

    Science.gov (United States)

    Schwarz, Baruch B.; de Groot, Reuma; Mavrikis, Manolis; Dragon, Toby

    2015-01-01

    In this paper, we identify "Learning to Learn Together" (L2L2) as a new and important educational goal. Our view of L2L2 is a substantial extension of "Learning to Learn" (L2L): L2L2 consists of learning to collaborate to successfully face L2L challenges. It is inseparable from L2L, as it emerges when individuals face problems…

  8. Technology, Learning, and Individual Differences

    Science.gov (United States)

    Bear, Anne A. Ghost

    2012-01-01

    The learning needs for adults that result from the constant increase in technology are rooted in the adult learning concepts of (a) andragogy, (b) self-directed learning, (c) learning-how-to-learn, (d) real-life learning, and (e) learning strategies. This study described the learning strategies that adults use in learning to engage in an online…

  9. From sensorimotor learning to memory cells in prefrontal and temporal association cortex: a neurocomputational study of disembodiment.

    Science.gov (United States)

    Pulvermüller, Friedemann; Garagnani, Max

    2014-08-01

    Memory cells, the ultimate neurobiological substrates of working memory, remain active for several seconds and are most commonly found in prefrontal cortex and higher multisensory areas. However, if correlated activity in "embodied" sensorimotor systems underlies the formation of memory traces, why should memory cells emerge in areas distant from their antecedent activations in sensorimotor areas, thus leading to "disembodiment" (movement away from sensorimotor systems) of memory mechanisms? We modelled the formation of memory circuits in six-area neurocomputational architectures, implementing motor and sensory primary, secondary and higher association areas in frontotemporal cortices along with known between-area neuroanatomical connections. Sensorimotor learning driven by Hebbian neuroplasticity led to formation of cell assemblies distributed across the different areas of the network. These action-perception circuits (APCs) ignited fully when stimulated, thus providing a neural basis for long-term memory (LTM) of sensorimotor information linked by learning. Subsequent to ignition, activity vanished rapidly from APC neurons in sensorimotor areas but persisted in those in multimodal prefrontal and temporal areas. Such persistent activity provides a mechanism for working memory for actions, perceptions and symbols, including short-term phonological and semantic storage. Cell assembly ignition and "disembodied" working memory retreat of activity to multimodal areas are documented in the neurocomputational models' activity dynamics, at the level of single cells, circuits, and cortical areas. Memory disembodiment is explained neuromechanistically by APC formation and structural neuroanatomical features of the model networks, especially the central role of multimodal prefrontal and temporal cortices in bridging between sensory and motor areas. These simulations answer the "where" question of cortical working memory in terms of distributed APCs and their inner structure

  10. Human Machine Learning Symbiosis

    Science.gov (United States)

    Walsh, Kenneth R.; Hoque, Md Tamjidul; Williams, Kim H.

    2017-01-01

    Human Machine Learning Symbiosis is a cooperative system where both the human learner and the machine learner learn from each other to create an effective and efficient learning environment adapted to the needs of the human learner. Such a system can be used in online learning modules so that the modules adapt to each learner's learning state both…

  11. Machine-Learning Research

    OpenAIRE

    Dietterich, Thomas G.

    1997-01-01

    Machine-learning research has been making great progress in many directions. This article summarizes four of these directions and discusses some current open problems. The four directions are (1) the improvement of classification accuracy by learning ensembles of classifiers, (2) methods for scaling up supervised learning algorithms, (3) reinforcement learning, and (4) the learning of complex stochastic models.

  12. Targeted Learning

    CERN Document Server

    van der Laan, Mark J

    2011-01-01

    The statistics profession is at a unique point in history. The need for valid statistical tools is greater than ever; data sets are massive, often measuring hundreds of thousands of measurements for a single subject. The field is ready to move towards clear objective benchmarks under which tools can be evaluated. Targeted learning allows (1) the full generalization and utilization of cross-validation as an estimator selection tool so that the subjective choices made by humans are now made by the machine, and (2) targeting the fitting of the probability distribution of the data toward the targe

  13. Learning Vaadin

    CERN Document Server

    Frankel, Nicolas

    2011-01-01

    This book begins with a tutorial on Vaadin 7, followed by a process of planning, analyzing, building, and deploying a fully functional RIA while covering troubleshooting details along the way, making it an invaluable resource for answers to all your Vaadin questions. If you are a Java developer with some experience in Java web development and want to enter the world of Rich Internet Applications this technology and book are ideal for you. Learning Vaadin will be perfect as your next step towards building eye-candy dynamic web applications on a Java-based platform.

  14. Learning Cypher

    CERN Document Server

    Panzarino, Onofrio

    2014-01-01

    An easy-to-follow guide full of tips and examples of real-world applications. In each chapter, a thorough example will show you the concepts in action, followed by a detailed explanation.This book is intended for those who want to learn how to create, query, and maintain a graph database, or who want to migrate to a graph database from SQL. It would be helpful to have some familiarity with Java and/or SQL, but no prior experience is required.

  15. Learning Perl

    CERN Document Server

    Schwartz, Randal; Phoenix, Tom

    2011-01-01

    If you're just getting started with Perl, this is the book you want-whether you're a programmer, system administrator, or web hacker. Nicknamed "the Llama" by two generations of users, this bestseller closely follows the popular introductory Perl course taught by the authors since 1991. This 6th edition covers recent changes to the language up to version 5.14. Perl is suitable for almost any task on almost any platform, from short fixes to complete web applications. Learning Perl teaches you the basics and shows you how to write programs up to 128 lines long-roughly the size of 90% of the Pe

  16. Learning scikit-learn machine learning in Python

    CERN Document Server

    Garreta, Raúl

    2013-01-01

    The book adopts a tutorial-based approach to introduce the user to Scikit-learn.If you are a programmer who wants to explore machine learning and data-based methods to build intelligent applications and enhance your programming skills, this the book for you. No previous experience with machine-learning algorithms is required.

  17. Technology Enhanced Learning

    NARCIS (Netherlands)

    Klemke, Roland; Specht, Marcus

    2013-01-01

    Klemke, R., & Specht, M. (2013, 26-27 September). Technology Enhanced Learning. Presentation at the fourth international conference on eLearning (eLearning 2013), Belgrade, Serbia. http://econference.metropolitan.ac.rs/

  18. Mobile Inquiry Based Learning

    NARCIS (Netherlands)

    Specht, Marcus

    2012-01-01

    Specht, M. (2012, 8 November). Mobile Inquiry Based Learning. Presentation given at the Workshop "Mobile inquiry-based learning" at the Mobile Learning Day 2012 at the Fernuniversität Hagen, Hagen, Germany.

  19. Learning and Memory

    OpenAIRE

    1999-01-01

    Under various circumstances and in different species the outward expression of learning varies considerably, and this has led to the classification of different categories of learning. Just as there is no generally agreed on definition of learning, there is no one system of classification. Types of learning commonly recognized are: Habituation, sensitization, classical conditioning, operant conditioning, trial and error, taste aversion, latent learning, cultural learning, imprinting, insight ...

  20. Toward Learning Teams

    DEFF Research Database (Denmark)

    Hoda, Rashina; Babb, Jeff; Nørbjerg, Jacob

    2013-01-01

    to sacrifice learning-focused practices. Effective learning under pressure involves conscious efforts to implement original agile practices such as retrospectives and adapted strategies such as learning spikes. Teams, their management, and customers must all recognize the importance of creating learning teams......Today's software development challenges require learning teams that can continuously apply new engineering and management practices, new and complex technical skills, cross-functional skills, and experiential lessons learned. The pressure of delivering working software often forces software teams...

  1. Greedy Deep Dictionary Learning

    OpenAIRE

    Tariyal, Snigdha; Majumdar, Angshul; Singh, Richa; Vatsa, Mayank

    2016-01-01

    In this work we propose a new deep learning tool called deep dictionary learning. Multi-level dictionaries are learnt in a greedy fashion, one layer at a time. This requires solving a simple (shallow) dictionary learning problem, the solution to this is well known. We apply the proposed technique on some benchmark deep learning datasets. We compare our results with other deep learning tools like stacked autoencoder and deep belief network; and state of the art supervised dictionary learning t...

  2. Learning Networks for Professional Development & Lifelong Learning

    NARCIS (Netherlands)

    Sloep, Peter

    2009-01-01

    Sloep, P. B. (2009). Learning Networks for Professional Development & Lifelong Learning. Presentation at a NeLLL seminar with Etienne Wenger held at the Open Universiteit Nederland. September, 10, 2009, Heerlen, The Netherlands.

  3. Use of blended learning in workplace learning

    DEFF Research Database (Denmark)

    Georgsen, Marianne; Løvstad, Charlotte Vange

    2014-01-01

    -based teaching materials. This paper presents the experiences of this particular project, and goes on to discuss the following points: • The blended learning design – use of IT for teaching, learning and communication • Digital learning materials – principals of design and use • Work place learning and learning......In 2014, a new system has been put in place for the inspection and approval of social welfare institutions in Denmark. In as little as 10 weeks, 330 new employees in five regional centres participated in an introductory course, designed as work place learning with extensive use of e-learning and IT...... from work – the interplay between experiences of the learner and the curriculum of the program •The approach taken to customising the e-learning design to the needs and demands of a particular case....

  4. Learning Design Development for Blended Learning

    DEFF Research Database (Denmark)

    Hansen, Janne Saltoft

    Learning design development for blended learning We started implementing Blackboard at Aarhus University in 2013. At the Health Faculty Blackboard replaced AULA which was a LMS with functionality for file distribution and only a vague focus on learning tools. Most teachers therefore had...... no experiences with blended leaning and technology supported out-of-class activities. At the pedagogical unit at the Health faculty we wanted to follow the Blackboard implementation with pedagogical tools for learning design to evolve the pedagogical use of the system. We needed to make development of blended...... learning courses easier for the teachers and also ensure quality in the courses. This poster describes the process from development of the learning design to implementation of the learning design at the faculty: 1. How to place demands on a learning design-model and how to develop and use such a model. 2...

  5. Judgments of Learning in Collaborative Learning Environments

    NARCIS (Netherlands)

    Helsdingen, Anne

    2010-01-01

    Helsdingen, A. S. (2010, March). Judgments of Learning in Collaborative Learning Environments. Poster presented at the 1st International Air Transport and Operations Symposium (ATOS 2010), Delft, The Netherlands: Delft University of Technology.

  6. Learning design guided learning analytics in MOOCs

    NARCIS (Netherlands)

    Brouns, Francis; Firssova, Olga

    2016-01-01

    Poster presentation for our paper Brouns, F., & Firssova, O. (2016, October).The role of learning design and learning analytics in MOOCs. Paper presented at 9th EDEN Research Workshop, Oldenburg, Germany.

  7. Networked professional learning

    NARCIS (Netherlands)

    Sloep, Peter

    2013-01-01

    Sloep, P. B. (2013). Networked professional learning. In A. Littlejohn, & A. Margaryan (Eds.), Technology-enhanced Professional Learning: Processes, Practices and Tools (pp. 97–108). London: Routledge.

  8. Resonant learning

    DEFF Research Database (Denmark)

    Lindvang, Charlotte

    2013-01-01

    -experience and personal therapy in training, first and foremost from the students’ perspective. The author focuses on presenting the qualitative part of her research which namely addresses the students’ experiences. Semi-structured qualitative interviews and qualitative music analyses were conducted, using a hermeneutic...... approach. The informants were nine music therapy students from Aalborg University, enrolled in the fifth year of their Master’s degree training programme. They were asked to bring a recording of an improvisation of their own choice to the interview. The qualitative data collection of text and music......The article presents a part of the authors PhD-study in music therapy about self-experiential training and the development of music therapeutic competencies. One of the purposes of the study was to explore and generate understanding and insight into the phenomena of learning through self...

  9. Blended Learning: An Innovative Approach

    Science.gov (United States)

    Lalima; Dangwal, Kiran Lata

    2017-01-01

    Blended learning is an innovative concept that embraces the advantages of both traditional teaching in the classroom and ICT supported learning including both offline learning and online learning. It has scope for collaborative learning; constructive learning and computer assisted learning (CAI). Blended learning needs rigorous efforts, right…

  10. Brain Research: Implications for Learning.

    Science.gov (United States)

    Soares, Louise M.; Soares, Anthony T.

    Brain research has illuminated several areas of the learning process: (1) learning as association; (2) learning as reinforcement; (3) learning as perception; (4) learning as imitation; (5) learning as organization; (6) learning as individual style; and (7) learning as brain activity. The classic conditioning model developed by Pavlov advanced…

  11. Blended Learning in Personalized Assistive Learning Environments

    Science.gov (United States)

    Marinagi, Catherine; Skourlas, Christos

    2013-01-01

    In this paper, the special needs/requirements of disabled students and cost-benefits for applying blended learning in Personalized Educational Learning Environments (PELE) in Higher Education are studied. The authors describe how blended learning can form an attractive and helpful framework for assisting Deaf and Hard-of-Hearing (D-HH) students to…

  12. LEARNING ABOUT LEARNING, A CONFERENCE REPORT.

    Science.gov (United States)

    BRUNER, JEROME

    TO EXPLORE THE NATURE OF THE LEARNING PROCESS, THREE IMPORTANT PROBLEM AREAS WERE STUDIED. STUDIES IN THE FIRST AREA, ATTITUDINAL AND AFFECTIVE SKILLS, ARE CONCERNED WITH INDUCING A CHILD TO LEARN AND SUSTAINING HIS ATTENTION. STUDIES IN THE SECOND AREA, COGNITIVE SKILLS, SOUGHT TO DISCOVER WHETHER GENERAL IDEAS AND SKILLS CAN BE LEARNED IN SUCH A…

  13. When Learning Analytics Meets E-Learning

    Science.gov (United States)

    Czerkawski, Betul C.

    2015-01-01

    While student data systems are nothing new and most educators have been dealing with student data for many years, learning analytics has emerged as a new concept to capture educational big data. Learning analytics is about better understanding of the learning and teaching process and interpreting student data to improve their success and learning…

  14. Learning Networks for Professional Development & Lifelong Learning

    NARCIS (Netherlands)

    Brouns, Francis; Sloep, Peter

    2009-01-01

    Brouns, F., & Sloep, P. B. (2009). Learning Networks for Professional Development & Lifelong Learning. Presentation of the Learning Network Programme for a Korean delegation of Chonnam National University and Dankook University (researchers dr. Jeeheon Ryu and dr. Minjeong Kim and a Group of PhD and

  15. Stimulating Deep Learning Using Active Learning Techniques

    Science.gov (United States)

    Yew, Tee Meng; Dawood, Fauziah K. P.; a/p S. Narayansany, Kannaki; a/p Palaniappa Manickam, M. Kamala; Jen, Leong Siok; Hoay, Kuan Chin

    2016-01-01

    When students and teachers behave in ways that reinforce learning as a spectator sport, the result can often be a classroom and overall learning environment that is mostly limited to transmission of information and rote learning rather than deep approaches towards meaningful construction and application of knowledge. A group of college instructors…

  16. Learning Analytics for Networked Learning Models

    Science.gov (United States)

    Joksimovic, Srecko; Hatala, Marek; Gaševic, Dragan

    2014-01-01

    Teaching and learning in networked settings has attracted significant attention recently. The central topic of networked learning research is human-human and human-information interactions occurring within a networked learning environment. The nature of these interactions is highly complex and usually requires a multi-dimensional approach to…

  17. Facilitating Learning Organizations. Making Learning Count.

    Science.gov (United States)

    Marsick, Victoria J.; Watkins, Karen E.

    This book offers advice to facilitators and change agents who wish to build systems-level learning to create knowledge that can be used to gain a competitive advantage. Chapter 1 describes forces driving companies to build, sustain, and effectively use systems-level learning and presents and links a working definition of the learning organization…

  18. A ranking method for the concurrent learning of compounds with various activity profiles.

    Science.gov (United States)

    Dörr, Alexander; Rosenbaum, Lars; Zell, Andreas

    2015-01-01

    In this study, we present a SVM-based ranking algorithm for the concurrent learning of compounds with different activity profiles and their varying prioritization. To this end, a specific labeling of each compound was elaborated in order to infer virtual screening models against multiple targets. We compared the method with several state-of-the-art SVM classification techniques that are capable of inferring multi-target screening models on three chemical data sets (cytochrome P450s, dehydrogenases, and a trypsin-like protease data set) containing three different biological targets each. The experiments show that ranking-based algorithms show an increased performance for single- and multi-target virtual screening. Moreover, compounds that do not completely fulfill the desired activity profile are still ranked higher than decoys or compounds with an entirely undesired profile, compared to other multi-target SVM methods. SVM-based ranking methods constitute a valuable approach for virtual screening in multi-target drug design. The utilization of such methods is most helpful when dealing with compounds with various activity profiles and the finding of many ligands with an already perfectly matching activity profile is not to be expected.

  19. Online transfer learning with extreme learning machine

    Science.gov (United States)

    Yin, Haibo; Yang, Yun-an

    2017-05-01

    In this paper, we propose a new transfer learning algorithm for online training. The proposed algorithm, which is called Online Transfer Extreme Learning Machine (OTELM), is based on Online Sequential Extreme Learning Machine (OSELM) while it introduces Semi-Supervised Extreme Learning Machine (SSELM) to transfer knowledge from the source to the target domain. With the manifold regularization, SSELM picks out instances from the source domain that are less relevant to those in the target domain to initialize the online training, so as to improve the classification performance. Experimental results demonstrate that the proposed OTELM can effectively use instances in the source domain to enhance the learning performance.

  20. Professional learning versus interprofessional learning

    DEFF Research Database (Denmark)

    Nielsen, Cathrine Sand

    2014-01-01

    to improve quality in the Danish healthcare system (1). Cooperation between patients and professionals is challenged when patients are transferred between department, hospitals or sectors (2). Sharing and developing knowledge inter-professionally and in particular across sectors is inadequate (3......, which is necessary for development of the future undergraduate health professional education programmes. The PhD project intends to generate knowledge of: - the contributions of InterTværs to the quality of future health professional education programmes and to the future healthcare system....... The transition challenges in the healthcare system do not seem to only affect patients and knowledge, but also the students and learning. References: (1) Institute for Quality and Accreditation in Healthcare. 2012. The Danish Healthcare Quality Programme. Accreditation Standards for Hospitals (2) Siemsen IMD...

  1. When does social learning become cultural learning?

    Science.gov (United States)

    Heyes, Cecilia

    2017-03-01

    Developmental research on selective social learning, or 'social learning strategies', is currently a rich source of information about when children copy behaviour, and who they prefer to copy. It also has the potential to tell us when and how human social learning becomes cultural learning; i.e. mediated by psychological mechanisms that are specialized, genetically or culturally, to promote cultural inheritance. However, this review article argues that, to realize its potential, research on the development of selective social learning needs more clearly to distinguish functional from mechanistic explanation; to achieve integration with research on attention and learning in adult humans and 'dumb' animals; and to recognize that psychological mechanisms can be specialized, not only by genetic evolution, but also by associative learning and cultural evolution. © 2015 John Wiley & Sons Ltd.

  2. Records for learning

    DEFF Research Database (Denmark)

    Binder, Thomas

    2005-01-01

    The article present and discuss findings from a participatory development of new learning practices among intensive care nurses, with an emphasize on the role of place making in informal learning activities.......The article present and discuss findings from a participatory development of new learning practices among intensive care nurses, with an emphasize on the role of place making in informal learning activities....

  3. Mobile Learning Platform

    DEFF Research Database (Denmark)

    Annan, Nana Kofi; Ofori-Dwumfou, George; Falch, Morten

    2012-01-01

    on the first experiences gained by both teachers and students by asking the following questions: What are the perceptions of teachers on m-learning? What are the effects of m-learning on students? What does m-learning contribute to face-to-face teaching and learning? Questionnaires were administered...

  4. Students Engaged in Learning

    Science.gov (United States)

    Ismail, Emad A.; Groccia, James E.

    2018-01-01

    Engaging students in learning is a basic principle of effective undergraduate education. Outcomes of engaging students include meaningful learning experiences and enhanced skills in all learning domains. This chapter reviews the influence of engaging students in different forms of active learning on cognitive, psychomotor, and affective skill…

  5. Cultural Learning Redux

    Science.gov (United States)

    Tomasello, Michael

    2016-01-01

    M. Tomasello, A. Kruger, and H. Ratner (1993) proposed a theory of cultural learning comprising imitative learning, instructed learning, and collaborative learning. Empirical and theoretical advances in the past 20 years suggest modifications to the theory; for example, children do not just imitate but overimitate in order to identify and…

  6. Teaching for Deep Learning

    Science.gov (United States)

    Smith, Tracy Wilson; Colby, Susan A.

    2007-01-01

    The authors have been engaged in research focused on students' depth of learning as well as teachers' efforts to foster deep learning. Findings from a study examining the teaching practices and student learning outcomes of sixty-four teachers in seventeen different states (Smith et al. 2005) indicated that most of the learning in these classrooms…

  7. Culture and Organizational Learning

    NARCIS (Netherlands)

    Cook, N.; Yanow, D.

    2011-01-01

    Traditionally, theories of organizational learning have taken one of two approaches that share a common characterization of learning but differ in focus. One approach focuses on learning by individuals in organizational contexts; the other, on individual learning as a model for organizational

  8. Active Learning Methods

    Science.gov (United States)

    Zayapragassarazan, Z.; Kumar, Santosh

    2012-01-01

    Present generation students are primarily active learners with varied learning experiences and lecture courses may not suit all their learning needs. Effective learning involves providing students with a sense of progress and control over their own learning. This requires creating a situation where learners have a chance to try out or test their…

  9. Algorithms for Reinforcement Learning

    CERN Document Server

    Szepesvari, Csaba

    2010-01-01

    Reinforcement learning is a learning paradigm concerned with learning to control a system so as to maximize a numerical performance measure that expresses a long-term objective. What distinguishes reinforcement learning from supervised learning is that only partial feedback is given to the learner about the learner's predictions. Further, the predictions may have long term effects through influencing the future state of the controlled system. Thus, time plays a special role. The goal in reinforcement learning is to develop efficient learning algorithms, as well as to understand the algorithms'

  10. Rethinking e-learning

    DEFF Research Database (Denmark)

    Bang, Jørgen; Dalsgaard, Christian

    2006-01-01

    “Technology alone does not deliver educational success. It only becomes valuable in education if learners and teachers can do something useful with it” (E-Learning: The Partnership Challenge, 2001, p. 24). This quotation could be used as a bon mot for this chapter. Our main goal is to rethink e-learning...... by shifting the focus of attention from learning resources (learning objects) to learning activities, which also implies a refocusing of the pedagogical discussion of the learning process.Firstly, we try to identify why e-learning has not been able to deliver the educational results as expected five years ago...

  11. Lessons Learned

    Directory of Open Access Journals (Sweden)

    Amanda Phelan BNS, MSc, PhD

    2015-03-01

    Full Text Available The public health nurses’ scope of practice explicitly includes child protection within their role, which places them in a prime position to identify child protection concerns. This role compliments that of other professions and voluntary agenices who work with children. Public health nurses are in a privileged position as they form a relationship with the child’s parent(s/guardian(s and are able to see the child in its own environment, which many professionals cannot. Child protection in Ireland, while influenced by other countries, has progressed through a distinct pathway that streamlined protocols and procedures. However, despite the above serious failures have occurred in the Irish system, and inquiries over the past 20 years persistently present similar contributing factors, namely, the lack of standardized and comprehensive service responses. Moreover, poor practice is compounded by the lack of recognition of the various interactional processes taking place within and between the different agencies of child protection, leading to psychological barriers in communication. This article will explore the lessons learned for public health nurses practice in safeguarding children in the Republic of Ireland.

  12. Learning after acquired brain injury. Learning the hard way

    NARCIS (Netherlands)

    Boosman, H.

    2015-01-01

    Background: When the brain has suffered damage, the learning process can be considerably disturbed. Brain damage can influence what is learned, but also how learning takes place. What patients can learn can be viewed in terms of ‘learning ability’ and how patients learn in terms of ‘learning style’.

  13. Interpretable Active Learning

    OpenAIRE

    Phillips, Richard L.; Chang, Kyu Hyun; Friedler, Sorelle A.

    2017-01-01

    Active learning has long been a topic of study in machine learning. However, as increasingly complex and opaque models have become standard practice, the process of active learning, too, has become more opaque. There has been little investigation into interpreting what specific trends and patterns an active learning strategy may be exploring. This work expands on the Local Interpretable Model-agnostic Explanations framework (LIME) to provide explanations for active learning recommendations. W...

  14. e-Learning Mathematics

    OpenAIRE

    Almanasreh, Hasan

    2017-01-01

    This study concerns the use of e-learning in the educational system shedding the light on its advantages and disadvantages, and analyzing its applicability either partially or totally. From mathematical perspectives, theories are developed to test the courses tendency to online transformation. This leads to a new trend of learning, the offline-online-offline learning (fnf-learning), it merges e-learning mode with the traditional orientation of education. The derivation of the new trend is bas...

  15. Learning Theories In Instructional Multimedia For English Learning

    OpenAIRE

    Farani, Rizki

    2016-01-01

    Learning theory is the concept of human learning. This concept is one of the important components in instructional for learning, especially English learning. English subject becomes one of important subjects for students but learning English needs specific strategy since it is not our vernacular. Considering human learning process in English learning is expected to increase students' motivation to understand English better. Nowadays, the application of learning theories in English learning ha...

  16. Designing Learning Resources in Synchronous Learning Environments

    DEFF Research Database (Denmark)

    Christiansen, Rene B

    2015-01-01

    Computer-mediated Communication (CMC) and synchronous learning environments offer new solutions for teachers and students that transcend the singular one-way transmission of content knowledge from teacher to student. CMC makes it possible not only to teach computer mediated but also to design...... and create new learning resources targeted to a specific group of learners. This paper addresses the possibilities of designing learning resources within synchronous learning environments. The empirical basis is a cross-country study involving students and teachers in primary schools in three Nordic...... Countries (Denmark, Sweden and Norway). On the basis of these empirical studies a set of design examples is drawn with the purpose of showing how the design fulfills the dual purpose of functioning as a remote, synchronous learning environment and - using the learning materials used and recordings...

  17. Multimodal sequence learning.

    Science.gov (United States)

    Kemény, Ferenc; Meier, Beat

    2016-02-01

    While sequence learning research models complex phenomena, previous studies have mostly focused on unimodal sequences. The goal of the current experiment is to put implicit sequence learning into a multimodal context: to test whether it can operate across different modalities. We used the Task Sequence Learning paradigm to test whether sequence learning varies across modalities, and whether participants are able to learn multimodal sequences. Our results show that implicit sequence learning is very similar regardless of the source modality. However, the presence of correlated task and response sequences was required for learning to take place. The experiment provides new evidence for implicit sequence learning of abstract conceptual representations. In general, the results suggest that correlated sequences are necessary for implicit sequence learning to occur. Moreover, they show that elements from different modalities can be automatically integrated into one unitary multimodal sequence. Copyright © 2015 Elsevier B.V. All rights reserved.

  18. Transformative learning spaces

    DEFF Research Database (Denmark)

    Maslo, Elina

    Despite rapid development of learning theory in general and language learning theory in particular in the last years, we still cannot provide an unequivocal answer on the question “why do individuals who presumably possess similar cognitive capacities for second language learning achieve such var......, Leo (2010). The ecology of language learning: Practice to theory, theory to practice. Procedia – Social and Behavioral Sciences. Elsevier......., social, personal, cultural, and historical world they live in (van Lier, 2000). People can learn when they discover possibilities for learning, which appear in this complex world – so called affordances (Gibson, 1979). This happens in the interaction between people and their environment on the basis...... to the different ways of interaction of cognitive, affective and social factors by different individuals. Learning stories, where multilingual individuals are telling about their subjective experiences in language learning in particular and learning in general, are constructed by using a special developed...

  19. Scikit-learn: Machine Learning in Python

    OpenAIRE

    Pedregosa, Fabian; Varoquaux, Gaël; Gramfort, Alexandre; Michel, Vincent; Thirion, Bertrand; Grisel, Olivier; Blondel, Mathieu; Prettenhofer, Peter; Weiss, Ron; Dubourg, Vincent; Vanderplas, Jake; Passos, Alexandre; Cournapeau, David; Brucher, Matthieu; Perrot, Matthieu

    2011-01-01

    International audience; Scikit-learn is a Python module integrating a wide range of state-of-the-art machine learning algorithms for medium-scale supervised and unsupervised problems. This package focuses on bringing machine learning to non-specialists using a general-purpose high-level language. Emphasis is put on ease of use, performance, documentation, and API consistency. It has minimal dependencies and is distributed under the simplified BSD license, encouraging its use in both academic ...

  20. Scikit-learn: Machine Learning in Python

    OpenAIRE

    Pedregosa, Fabian; Varoquaux, Gaël; Gramfort, Alexandre; Michel, Vincent; Thirion, Bertrand; Grisel, Olivier; Blondel, Mathieu; Louppe, Gilles; Prettenhofer, Peter; Weiss, Ron; Dubourg, Vincent; Vanderplas, Jake; Passos, Alexandre; Cournapeau, David; Brucher, Matthieu

    2012-01-01

    Scikit-learn is a Python module integrating a wide range of state-of-the-art machine learning algorithms for medium-scale supervised and unsupervised problems. This package focuses on bringing machine learning to non-specialists using a general-purpose high-level language. Emphasis is put on ease of use, performance, documentation, and API consistency. It has minimal dependencies and is distributed under the simplified BSD license, encouraging its use in both academic and commercial settings....

  1. Assessing learning at the workplace

    NARCIS (Netherlands)

    Evers, Arnoud

    2018-01-01

    • Defining learning at the workplace • Assessing learning at the workplace • Facilitating learning at the workplace: - Structure - Culture - Leadership - Personal factors • Conclusions • Discussion

  2. Mobile learning in medicine

    Science.gov (United States)

    Serkan Güllüoüǧlu, Sabri

    2013-03-01

    This paper outlines the main infrastructure for implicating mobile learning in medicine and present a sample mobile learning application for medical learning within the framework of mobile learning systems. Mobile technology is developing nowadays. In this case it will be useful to develop different learning environments using these innovations in internet based distance education. M-learning makes the most of being on location, providing immediate access, being connected, and acknowledges learning that occurs beyond formal learning settings, in places such as the workplace, home, and outdoors. Central to m-learning is the principle that it is the learner who is mobile rather than the device used to deliver m learning. The integration of mobile technologies into training has made learning more accessible and portable. Mobile technologies make it possible for a learner to have access to a computer and subsequently learning material and activities; at any time and in any place. Mobile devices can include: mobile phone, personal digital assistants (PDAs), personal digital media players (eg iPods, MP3 players), portable digital media players, portable digital multimedia players. Mobile learning (m-learning) is particularly important in medical education, and the major users of mobile devices are in the field of medicine. The contexts and environment in which learning occurs necessitates m-learning. Medical students are placed in hospital/clinical settings very early in training and require access to course information and to record and reflect on their experiences while on the move. As a result of this paper, this paper strives to compare and contrast mobile learning with normal learning in medicine from various perspectives and give insights and advises into the essential characteristics of both for sustaining medical education.

  3. Predictive Acoustic Tracking with an Adaptive Neural Mechanism

    DEFF Research Database (Denmark)

    Shaikh, Danish; Manoonpong, Poramate

    2017-01-01

    model of the lizard peripheral auditory system to extract information regarding sound direction. This information is utilised by a neural machinery to learn the acoustic signal’s velocity through fast and unsupervised correlation-based learning adapted from differential Hebbian learning. This approach...

  4. Pervasive Learning Environments

    DEFF Research Database (Denmark)

    Hundebøl, Jesper; Helms, Niels Henrik

    2006-01-01

    The potentials of pervasive communication in learning within industry and education are right now being explored through different R&D projects. This paper outlines the background for and the possible learning potentials in what we describe as pervasive learning environments (PLE). PLE?s differ...... from virtual learning environments (VLE) primarily because in PLE?s the learning content is very much related to the actual context in which the learner finds himself. Two local (Denmark) cases illustrate various aspects of pervasive learning. One is the eBag, a pervasive digital portfolio used...

  5. Transnational Learning Processes

    DEFF Research Database (Denmark)

    Nedergaard, Peter

    This paper analyses and compares the transnational learning processes in the employment field in the European Union and among the Nordic countries. Based theoretically on a social constructivist model of learning and methodologically on a questionnaire distributed to the relevant participants......, a number of hypotheses concerning transnational learning processes are tested. The paper closes with a number of suggestions regarding an optimal institutional setting for facilitating transnational learning processes.Key words: Transnational learning, Open Method of Coordination, Learning, Employment......, European Employment Strategy, European Union, Nordic countries....

  6. Learning to Innovate

    DEFF Research Database (Denmark)

    Mei, Maggie

    the relationship between organizational learning and innovation creation in an organizational context. Taking a nuanced view of organizational learning, the dissertation investigates how three different organizational learning processes could affect innovation creation at the firm level and project level...... to the understanding of managing organizational learning for innovation creation at firms. The three studies in this dissertation show how three prominent organizational learning processes impact on firms’ innovation performance. Furthermore, the studies in this dissertation emphasize that there are limitation...... and boundary conditions for different organizational learning processes....

  7. e-Learning for Lifelong Learning in Denmark

    DEFF Research Database (Denmark)

    Buhl, Mie; Andreasen, Lars Birch

    2010-01-01

    The chapter on 'e-Learning for Lifelong Learning in Denmark' is part of an international White Paper, focusing on educational systems, describing status and characteristics and highlighting specific cases of e-learning and of lifelong learning....

  8. Homeostatic role of heterosynaptic plasticity: Models and experiments

    Directory of Open Access Journals (Sweden)

    Marina eChistiakova

    2015-07-01

    Full Text Available Homosynaptic Hebbian-type plasticity provides a cellular mechanism of learning and refinement of connectivity during development in a variety of biological systems. In this review we argue that a complimentary form of plasticity - heterosynaptic plasticity - represents a necessary cellular component for homeostatic regulation of synaptic weights and neuronal activity. The required properties of a homeostatic mechanism which acutely constrains the runaway dynamics imposed by Hebbian associative plasticity have been well-articulated by theoretical and modeling studies. Such mechanism(s should robustly support the stability of operation of neuronal networks and synaptic competition, include changes at non-active synapses, and operate on a similar time scale to Hebbian-type plasticity. The experimentally observed properties of heterosynaptic plasticity have introduced it as a strong candidate to fulfill this homeostatic role. Subsequent modeling studies which incorporate heterosynaptic plasticity into model neurons with Hebbian synapses (utilizing an STDP learning rule have confirmed its ability to robustly provide stability and competition. In contrast, properties of homeostatic synaptic scaling, which is triggered by extreme and long lasting (hours and days changes of neuronal activity, do not fit two crucial requirements for a hypothetical homeostatic mechanism needed to provide stability of operation in the face of on-going synaptic changes driven by Hebbian-type learning rules. Both the trigger and the time scale of homeostatic synaptic scaling are fundamentally different from those of the Hebbian-type plasticity. We conclude that heterosynaptic plasticity, which is triggered by the same episodes of strong postsynaptic activity and operates on the same time scale as Hebbian-type associative plasticity, is ideally suited to serve homeostatic role during on-going synaptic plasticity.

  9. Lessons Learned

    International Nuclear Information System (INIS)

    Dougan, A.D.; Blair, S.

    2006-01-01

    LLNL turned in 5 Declaration Line Items (DLI's) in 2006. Of these, one was declared completed. We made some changes to streamline our process from 2005, used less money, time and fewer team members. This report is a description of what changes we made in 2006 and what we learned. Many of our core review team had changed from last year, including our Laboratory Director, the Facility safety and security representatives, our Division Leader, and the OPSEC Committee Chair. We were able to hand out an AP Manual to some of them, and briefed all newcomers to the AP process. We first went to the OPSEC Committee and explained what the Additional Protocol process would be for 2006 and solicited their help in locating declarable projects. We utilized the 'three questions' from the AP meeting last year. LLNL has no single place to locate all projects at the laboratory. We talked to Resource Managers and key Managers in the Energy and Environment Directorate and in the Nonproliferation Homeland and International Security Directorate to find applicable projects. We also talked to the Principal Investigators who had projects last year. We reviewed a list of CRADA's and LDRD projects given to us by the Laboratory Site Office. Talking to the PI's proved difficult because of vacation or travel schedules. We were never able to locate one PI in town. Fortunately, collateral information allowed us to screen out his project. We had no problems in downloading new versions of the DWA and DDA. It was helpful for both Steve Blair and Arden Dougan to have write privileges. During the time we were working on the project, we had to tag-team the work to allow for travel and vacation schedules. We had some difficulty locating an 'activities block' in the software. This was mentioned as something we needed to fix from our 2005 declaration. Evidently the Activities Block has been removed from the current version of the software. We also had trouble finding the DLI Detail Report, which we included

  10. Stealth Learning: Unexpected Learning Opportunities through Games

    Science.gov (United States)

    Sharp, Laura A.

    2012-01-01

    Educators across the country struggle to create engaging, motivating learning environments for their Net Gen students. These learners expect instant gratification that traditional lectures do not provide. This leaves educators searching for innovative ways to engage students in order to encourage learning. One solution is for educators to use…

  11. From E-learning to Blended Learning

    DEFF Research Database (Denmark)

    Hansen, Line Skov; Hansen, Ole

    2013-01-01

    . The project uses a ?capacity building strategy where new practice and skills are built through pedagogical interventions mostly designed as courses based on blended learning with a dialogue oriented and practice related team-work as an important part. Through this work the team learns how to use a specific...

  12. Generative Learning: Adults Learning within Ambiguity

    Science.gov (United States)

    Nicolaides, Aliki

    2015-01-01

    This study explored the extent to which ambiguity can serve as a catalyst for adult learning. The purpose of this study is to understand learning that is generated when encountering ambiguity agitated by the complexity of liquid modernity. "Ambiguity," in this study, describes an encounter with an appearance of reality that is at first…

  13. LEARNING HOW TO LEARN A LANGUAGE

    CERN Multimedia

    Language Training; Tel. 73127; Andrée Fontbonne; Tel. 72844

    2001-01-01

    This bilingual seminar is for anyone who would like to develop learning strategies and skills for learning a foreign language. Languages: French and English. Length: 3 days, 7 hours per day. Dates: 4, 5, 6 March 2002. Price: 460 CHF per person (for a group of 8 people). If you are interested, please enrol through our Web pages: http://cern.ch/Training

  14. LEARNING HOW TO LEARN A LANGUAGE

    CERN Multimedia

    Language Training; Tel. 73127; Andrée Fontbonne; Tel. 72844

    2001-01-01

    This bilingual seminar is for anyone who would like to develop learning strategies and skills for learning a foreign language. Languages: French and English. Length: 3 days, 7 hours per day. Dates: 5, 6, 7 November 2001. Price: 460 CHF per person (for a group of 8 people). If you are interested, please enrol through our Web pages: http://cern.ch/Training

  15. Constructivist learning theories and complex learning environments

    NARCIS (Netherlands)

    R-J. Simons; Dr. S. Bolhuis

    2004-01-01

    Learning theories broadly characterised as constructivist, agree on the importance to learning of the environment, but differ on what exactly it is that constitutes this importance. Accordingly, they also differ on the educational consequences to be drawn from the theoretical perspective. Cognitive

  16. Transformative Learning: Personal Empowerment in Learning Mathematics

    Science.gov (United States)

    Hassi, Marja-Liisa; Laursen, Sandra L.

    2015-01-01

    This article introduces the concept of personal empowerment as a form of transformative learning. It focuses on commonly ignored but enhancing elements of mathematics learning and argues that crucial personal resources can be essentially promoted by high engagement in mathematical problem solving, inquiry, and collaboration. This personal…

  17. Facilitating "Organisational Learning" in a "Learning Institution"

    Science.gov (United States)

    Lawler, Alan; Sillitoe, James

    2013-01-01

    The term "organisational learning" was popularised by Peter Senge in "The Fifth Discipline", his seminal book from 1990. Since then, the term has become widely accepted among those interested in organisational learning and change management. However, partly due to the somewhat ambiguous situation which arises in a university…

  18. Cooperative Learning as a Democratic Learning Method

    Science.gov (United States)

    Erbil, Deniz Gökçe; Kocabas, Ayfer

    2018-01-01

    In this study, the effects of applying the cooperative learning method on the students' attitude toward democracy in an elementary 3rd-grade life studies course was examined. Over the course of 8 weeks, the cooperative learning method was applied with an experimental group, and traditional methods of teaching life studies in 2009, which was still…

  19. Learning about Allergies

    Science.gov (United States)

    ... Videos for Educators Search English Español Learning About Allergies KidsHealth / For Kids / Learning About Allergies What's in ... in the spring. Why Do Some Kids Get Allergies? People may be born with a genetic (say: ...

  20. Pervasive Learning Environments

    DEFF Research Database (Denmark)

    Hundebøl, Jesper; Helms, Niels Henrik

    in schools. The other is moreover related to work based learning in that it foresees a community of practitioners accessing, sharing and adding to knowledge and learning objects held within a pervasive business intelligence system. Limitations and needed developments of these and other systems are discussed......Abstract: The potentials of pervasive communication in learning within industry and education are right know being explored through different R&D projects. This paper outlines the background for and the possible learning potentials in what we describe as pervasive learning environments (PLE). PLE......'s differ from virtual learning environments (VLE) primarily because in PLE's the learning content is very much related to the actual context in which the learner finds himself. Two local (Denmark) cases illustrate various aspects of pervasive learning. One is the eBag, a pervasive digital portfolio used...

  1. Learning by Doing.

    Science.gov (United States)

    Schettler, Joel

    2002-01-01

    Suggests that, as people become the key differentiation of competitive advantage, companies are turning to experiential learning programs to foster work force collaboration and cooperation. Discusses the history of experiential learning and its application in the workplace. (JOW)

  2. Learning Networks Distributed Environment

    NARCIS (Netherlands)

    Martens, Harrie; Vogten, Hubert; Koper, Rob; Tattersall, Colin; Van Rosmalen, Peter; Sloep, Peter; Van Bruggen, Jan; Spoelstra, Howard

    2005-01-01

    Learning Networks Distributed Environment is a prototype of an architecture that allows the sharing and modification of learning materials through a number of transport protocols. The prototype implements a p2p protcol using JXTA.

  3. Learning about Proteins

    Science.gov (United States)

    ... Fitness Diseases & Conditions Infections Drugs & Alcohol School & Jobs Sports Expert Answers (Q&A) Staying Safe Videos for Educators Search English Español Learning About Proteins KidsHealth / For Kids / Learning About Proteins What's in ...

  4. Learning in Practice

    DEFF Research Database (Denmark)

    Helth, Poula

    on theories of aesthetic performance and transformative learning, and on empirical studies through interventive methods within action research and ethnography. Transformative learning in my study has been developed based on aesthetic performance addressing leaders’ learning in practice. This kind of learning......The thesis presents the essence of my study of how leaders transform their practice through aesthetic performance. The background of the study is leaders' need for learning in and through practice, as an alternative to learning in classrooms and to leadership education programs. The study is based...... happens when leaders become aware of the potential for transformation of their leadership practice when they experiment with aesthetic performance integrated in a learning process. The greatest impact in relation to organisational transformation is, when leaders base their learning on a collective...

  5. MOOC Blended learning ontwikkelen

    NARCIS (Netherlands)

    Verjans, Steven

    2015-01-01

    Presentatie over het ontwerpen van leeractiviteiten (learning design) tijdens de zesde live sessie van de MOOC Blended learning ontwikkelen. Met gebruikmaking van presentatiematerialen van Diana Laurillard, Grainne Conole, Helen Beetham, Jos Fransen, Pieter Swager, Helen Keegan, Corinne Weisgerber.

  6. Social Structures for Learning

    NARCIS (Netherlands)

    I.M. Bogenrieder (Irma); B. Nooteboom (Bart)

    2001-01-01

    textabstractThis article investigates what learning groups there are in organizations, other than the familiar 'communities of practice'. It first develops an interdisciplinary theoretical framework for identifying, categorizing and understanding learning groups. For this, it employs a

  7. Learning about Carbohydrates

    Science.gov (United States)

    ... Videos for Educators Search English Español Learning About Carbohydrates KidsHealth / For Kids / Learning About Carbohydrates Print en ... source of energy for the body. What Are Carbohydrates? There are two major types of carbohydrates (or ...

  8. Preventing Learned Helplessness.

    Science.gov (United States)

    Hoy, Cheri

    1986-01-01

    To prevent learned helplessness in learning disabled students, teachers can share responsibilities with the students, train students to reinforce themselves for effort and self control, and introduce opportunities for changing counterproductive attitudes. (CL)

  9. Teaming up for learning

    NARCIS (Netherlands)

    Fransen, Jos

    2012-01-01

    Fransen, J. (2012). Teaming up for learning: Team effectiveness in collaborative learning in higher education (Doctoral dissertation). November, 16, 2012, Open University in the Netherlands (CELSTEC), Heerlen, The Netherlands.

  10. Teachability in Computational Learning

    OpenAIRE

    Shinohara, Ayumi; Miyano, Satoru

    1990-01-01

    This paper considers computationai learning from the viewpoint of teaching. We introduce a notion of teachability with which we establish a relationship between the learnability and teachability. We also discuss the complexity issues of a teacher in relation to learning.

  11. The sign learning theory

    African Journals Online (AJOL)

    KING OF DAWN

    The sign learning theory also holds secrets that could be exploited in accomplishing motor tasks. ... Introduction ... In his classic work: Cognitive Map in Rats and Men (1948),Tolman talked about five groups of experiments viz: latent learning ...

  12. Efficient Learning Design

    DEFF Research Database (Denmark)

    Godsk, Mikkel

    This paper presents the current approach to implementing educational technology with learning design at the Faculty of Science and Technology, Aarhus University, by introducing the concept of ‘efficient learning design’. The underlying hypothesis is that implementing learning design is more than...... engaging educators in the design process and developing teaching and learning, it is a shift in educational practice that potentially requires a stakeholder analysis and ultimately a business model for the deployment. What is most important is to balance the institutional, educator, and student...... perspectives and to consider all these in conjunction in order to obtain a sustainable, efficient learning design. The approach to deploying learning design in terms of the concept of efficient learning design, the catalyst for educational development, i.e. the learning design model and how it is being used...

  13. Pervasive Learning Environments

    DEFF Research Database (Denmark)

    Helms, Niels Henrik; Hundebøl, Jesper

    2006-01-01

    The potentials of pervasive communication in learning within industry and education are right know being explored through different R&D projects. This paper outlines the background for and the possible learning potentials in what we describe as pervasive learning environments (PLE). PLE's differ...... from virtual learning environments (VLE) primarily because in PLE's the learning content is very much related to the actual context in which the learner finds himself. Two local (Denmark) cases illustrate various aspects of pervasive learning. One is the eBag, a pervasive digital portfolio used...... in schools. The other is moreover related to work based learning in that it foresees a community of practitioners accessing, sharing and adding to knowledge and learning objects held within a pervasive business intelligence system. Limitations and needed developments of these and other systems are discussed...

  14. Learning Design Tools

    NARCIS (Netherlands)

    Griffiths, David; Blat, Josep; Garcia, Rocío; Vogten, Hubert; Kwong, KL

    2005-01-01

    Griffiths, D., Blat, J., Garcia, R., Vogten, H. & Kwong, KL. (2005). Learning Design Tools. In: Koper, R. & Tattersall, C., Learning Design: A Handbook on Modelling and Delivering Networked Education and Training (pp. 109-136). Berlin-Heidelberg: Springer Verlag.

  15. Genetic Science Learning Center

    Science.gov (United States)

    Genetic Science Learning Center Making science and health easy for everyone to understand Home News Our Team What We Do ... Collaboration Conferences Current Projects Publications Contact The Genetic Science Learning Center at The University of Utah is a ...

  16. Mobile Informal Learning

    NARCIS (Netherlands)

    Glahn, Christian; Börner, Dirk

    2010-01-01

    Glahn, C., & Börner, D. (2009). Mobile Informal Learning. Presented at Mobile Learning in Context Symposium at the Open University of the Netherlands. September, 11, 2009, Heerlen, The Netherlands: Open University of the Netherlands.

  17. Making Learning Meaningful.

    Science.gov (United States)

    Odom, A. Louis; Kelly, Paul V.

    1998-01-01

    Discusses two theories of cognitive development, Ausubel's theory of verbal learning and Piaget's development theory. Illustrates that both concept mapping and the learning cycle are rooted in these two theories. (DDR)

  18. New learning : three ways to learn in a new balance

    NARCIS (Netherlands)

    Simons, P.R.J.

    2000-01-01

    Because people are learning all the time, we need criteria that can help us distinguish between better and worse kinds of learning. Organizations and societies as well as the psychology of learning ask for new learning outcomes, new learning processes and new forms of instruction. New learning

  19. Learning about Learning: A Conundrum and a Possible Resolution

    Science.gov (United States)

    Barnett, Ronald

    2011-01-01

    What is it to learn in the modern world? We can identify four "learning epochs" through which our understanding of learning has passed: a metaphysical view; an empirical view; an experiential view; and, currently, a "learning-amid-contestation" view. In this last and current view, learning has its place in a world in which, the more one learns,…

  20. Effects of Cooperative E-Learning on Learning Outcomes

    Science.gov (United States)

    Yeh, Shang-Pao; Fu, Hsin-Wei

    2014-01-01

    This study aims to discuss the effects of E-Learning and cooperative learning on learning outcomes. E-Learning covers the dimensions of Interpersonal communication, abundant resources, Dynamic instruction, and Learning community; and, cooperative learning contains three dimensions of Cooperative motive, Social interaction, and Cognition…

  1. Learning, Play, and Your Newborn

    Science.gov (United States)

    ... Staying Safe Videos for Educators Search English Español Learning, Play, and Your Newborn KidsHealth / For Parents / Learning, ... Some Other Ideas Print What Is My Newborn Learning? Play is the chief way that infants learn ...

  2. Supervised Learning for Dynamical System Learning.

    Science.gov (United States)

    Hefny, Ahmed; Downey, Carlton; Gordon, Geoffrey J

    2015-01-01

    Recently there has been substantial interest in spectral methods for learning dynamical systems. These methods are popular since they often offer a good tradeoff between computational and statistical efficiency. Unfortunately, they can be difficult to use and extend in practice: e.g., they can make it difficult to incorporate prior information such as sparsity or structure. To address this problem, we present a new view of dynamical system learning: we show how to learn dynamical systems by solving a sequence of ordinary supervised learning problems, thereby allowing users to incorporate prior knowledge via standard techniques such as L 1 regularization. Many existing spectral methods are special cases of this new framework, using linear regression as the supervised learner. We demonstrate the effectiveness of our framework by showing examples where nonlinear regression or lasso let us learn better state representations than plain linear regression does; the correctness of these instances follows directly from our general analysis.

  3. Immersive Learning Technologies

    Science.gov (United States)

    2009-08-20

    Immersive Learning Technologies Mr. Peter Smith Lead, ADL Immersive Learning Team 08/20/2009 Report Documentation Page Form ApprovedOMB No. 0704...to 00-00-2009 4. TITLE AND SUBTITLE Immersive Learning Technologies 5a. CONTRACT NUMBER 5b. GRANT NUMBER 5c. PROGRAM ELEMENT NUMBER 6. AUTHOR...unclassified c. THIS PAGE unclassified Standard Form 298 (Rev. 8-98) Prescribed by ANSI Std Z39-18 Why Immersive Learning Technologies

  4. Learning a Second Language

    OpenAIRE

    Murphy, Caroline; Hermann, Charlotte; Andersen, Signe Hvalsøe; Grigalauskyte, Simona; Tolsgaard, Mads; Holmegaard, Thorbjørn; Hajaya, Zaedo Musa

    2013-01-01

    This study examines the concept of second language learning in Denmark with focus on how second language learners negotiate their identities in relation to language learning and integration. By investigating three language learners’ acquisition of Danish through key theories on the field of second language learning, focus is centred on the subjects’ lived experiences of the learning process within their everyday lives and in the classroom. Through interviews and observations it can be conclud...

  5. Social Structures for Learning

    OpenAIRE

    Bogenrieder, I.M.; Nooteboom, B.

    2001-01-01

    textabstractThis article investigates what learning groups there are in organizations, other than the familiar 'communities of practice'. It first develops an interdisciplinary theoretical framework for identifying, categorizing and understanding learning groups. For this, it employs a constructivist, interactionist theory of knowledge and learning. It employs elements of transaction cost theory and of social theory of trust. Transaction cost economics neglects learning and trust, but element...

  6. Pervasive e-learning

    DEFF Research Database (Denmark)

    Hundebøl, Jesper; Helms, Niels Henrik

    2009-01-01

    This article falls within planning, production and delivery of innovative learning resources. The establishment of pervasive learning environments is based on the successful combination and re-configuration of inter-connected sets of learning objects, databases and data-streams. The text presents...... a definition of Pervasive Learning Environments and discusses the pedagogical potentials and challenges in developing such environments with emphasis on context, new didactics, content and affordances....

  7. Evolving to organizational learning.

    Science.gov (United States)

    Bechtold, B L

    2000-02-01

    To transform in stride with the business changes, organizations need to think of development as "organizational learning" rather than "training." Companies need to manage learning as a strategic competitive advantage for current and future business rather than as a perk for individuals. To position themselves for success in a dynamic business environment, companies need to reframe their concept of learning and development to a mindset of organizational learning.

  8. LEADING THE LEARNING ORGANIZATION

    OpenAIRE

    Sapna Rijal

    2009-01-01

    Researchers have identified leadership as being one of the most important factors that influence the development of learning organization. They suggest that creating a collective vision of the future, empowering and developing employees so that they are better able to handle environmental challenges, modeling learning behavior and creating a learning environment, are crucial skills for leaders of learning organization. These roles are suitable to a transformational leader. Despite the potenti...

  9. Social learning in fish

    OpenAIRE

    Atton, Nicola

    2010-01-01

    Social learning is known to be a common phenomenon in fish, which they utilise under many different contexts, including foraging, mate-choice and migration. Here I review the literature on social learning in fish and present two studies. The first examines the ability of threespined sticklebacks to use social learning in the enhancement of food preferences. The second study examines the ability of both threespined sticklebacks and ninespined sticklebacks to use social learning in the avoidanc...

  10. Learning from prescribing errors

    OpenAIRE

    Dean, B

    2002-01-01

    

 The importance of learning from medical error has recently received increasing emphasis. This paper focuses on prescribing errors and argues that, while learning from prescribing errors is a laudable goal, there are currently barriers that can prevent this occurring. Learning from errors can take place on an individual level, at a team level, and across an organisation. Barriers to learning from prescribing errors include the non-discovery of many prescribing errors, lack of feedback to th...

  11. Microsoft Azure machine learning

    CERN Document Server

    Mund, Sumit

    2015-01-01

    The book is intended for those who want to learn how to use Azure Machine Learning. Perhaps you already know a bit about Machine Learning, but have never used ML Studio in Azure; or perhaps you are an absolute newbie. In either case, this book will get you up-and-running quickly.

  12. My Teaching Learning Philosophy

    Science.gov (United States)

    Punjani, Neelam Saleem

    2014-01-01

    The heart of teaching learning philosophy is the concept of nurturing students and teaching them in a way that creates passion and enthusiasm in them for a lifelong learning. According to Duke (1990) education is a practice of artful action where teaching learning process is considered as design and knowledge is considered as colours. Teaching…

  13. Enhancing learning with technology

    NARCIS (Netherlands)

    Specht, Marcus; Klemke, Roland

    2013-01-01

    Specht, M., & Klemke, R. (2013, 26-27 September). Enhancing Learning with Technology. In D. Milosevic (Ed.), Proceedings of the fourth international conference on eLearning (eLearning 2013) (pp. 37-45). Belgrade Metropolitan University, Belgrade, Serbia. http://econference.metropolitan.ac.rs/

  14. Deep Learning Policy Quantization

    NARCIS (Netherlands)

    van de Wolfshaar, Jos; Wiering, Marco; Schomaker, Lambertus

    2018-01-01

    We introduce a novel type of actor-critic approach for deep reinforcement learning which is based on learning vector quantization. We replace the softmax operator of the policy with a more general and more flexible operator that is similar to the robust soft learning vector quantization algorithm.

  15. Games for Learning

    Science.gov (United States)

    Gee, James Paul

    2013-01-01

    Today there is a great deal of interest in and a lot of hype about using video games in schools. Video games are a new silver bullet. Games can create good learning because they teach in powerful ways. The theory behind game-based learning is not really new, but a traditional and well-tested approach to deep and effective learning, often…

  16. Learning Probabilistic Decision Graphs

    DEFF Research Database (Denmark)

    Jaeger, Manfred; Dalgaard, Jens; Silander, Tomi

    2004-01-01

    efficient representations than Bayesian networks. In this paper we present an algorithm for learning PDGs from data. First experiments show that the algorithm is capable of learning optimal PDG representations in some cases, and that the computational efficiency of PDG models learned from real-life data...

  17. A learning space Odyssey

    NARCIS (Netherlands)

    Beckers, Ronald

    2016-01-01

    This dissertation addresses the alignment of learning space with higher education learning and teaching. Significant changes in higher education the past decades, such as increased information and communication technology (ICT) and new learning theories have resulted in the dilemma whether higher

  18. Learning in Organization

    Science.gov (United States)

    Palos, Ramona; Veres Stancovici, Vesna

    2016-01-01

    Purpose: This study aims at identifying the presence of the dimensions of learning capabilities and the characteristics of a learning organization within two companies in the field of services, as well as identifying the relationships between their learning capability and the organizational culture. Design/methodology/approach: This has been a…

  19. Learning Outcomes Report

    NARCIS (Netherlands)

    Stoyanov, Slavi; Spoelstra, Howard; Burgoyne, Louise; O’Tuathaigh, Colm

    2018-01-01

    Aim of the study The learning outcomes study, conducted as part of WP3 of the BioApp project, has as objectives: (a) generating a comprehensive list of the learning outcomes; (b) reaching an agreement on the scope and priority of the learning outcomes, and (c) making suggestions for the further

  20. Action Learning in China

    Science.gov (United States)

    Marquardt, Michael J.

    2015-01-01

    Action learning was introduced into China less than 20 years ago, but has rapidly become a valuable tool for organizations seeking to solve problems, develop their leaders, and become learning organizations. This article provides an historical overview of action learning in China, its cultural underpinnings, and five case studies. It concludes…

  1. Invited Reaction: Influences of Formal Learning, Personal Learning Orientation, and Supportive Learning Environment on Informal Learning

    Science.gov (United States)

    Cseh, Maria; Manikoth, Nisha N.

    2011-01-01

    As the authors of the preceding article (Choi and Jacobs, 2011) have noted, the workplace learning literature shows evidence of the complementary and integrated nature of formal and informal learning in the development of employee competencies. The importance of supportive learning environments in the workplace and of employees' personal learning…

  2. KARATE WITH CONSTRUCTIVE LEARNING

    Directory of Open Access Journals (Sweden)

    Srikrishna Karanam

    2012-02-01

    Full Text Available Any conventional learning process involves the traditional hierarchy of garnering of information and then recall gathered information. Constructive learning is an important research area having wide impact on teaching methods in education, learning theories, and plays a major role in many education reform movements. It is observed that constructive learning advocates the interconnection between emotions and learning. Human teachers identify the emotions of students with varying degrees of accuracy and can improve the learning rate of the students by motivating them. In learning with computers, computers also should be given the capability to recognize emotions so as to optimize the learning process. Image Processing is a very popular tool used in the process of establishing the theory of Constructive Learning. In this paper we use the Optical Flow computation in image sequences to analyze the accuracy of the moves of a karate player. We have used the Lucas-Kanade method for computing the optical flow in image sequences. A database consisting of optical flow images by a group of persons learning karate is formed and the learning rates are analyzed in order to main constructive learning. The contours of flow images are compared with the standard images and the error graphs are plotted. Analysis of the emotion of the amateur karate player is made by observing the error plots.

  3. Repurposing learning object components

    NARCIS (Netherlands)

    Verbert, K.; Jovanovic, J.; Gasevic, D.; Duval, E.; Meersman, R.

    2005-01-01

    This paper presents an ontology-based framework for repurposing learning object components. Unlike the usual practice where learning object components are assembled manually, the proposed framework enables on-the-fly access and repurposing of learning object components. The framework supports two

  4. Canadian Chefs' Workplace Learning

    Science.gov (United States)

    Cormier-MacBurnie, Paulette; Doyle, Wendy; Mombourquette, Peter; Young, Jeffrey D.

    2015-01-01

    Purpose: This paper aims to examine the formal and informal workplace learning of professional chefs. In particular, it considers chefs' learning strategies and outcomes as well as the barriers to and facilitators of their workplace learning. Design/methodology/approach: The methodology is based on in-depth, face-to-face, semi-structured…

  5. Learning: An Evolutionary Analysis

    Science.gov (United States)

    Swann, Joanna

    2009-01-01

    This paper draws on the philosophy of Karl Popper to present a descriptive evolutionary epistemology that offers philosophical solutions to the following related problems: "What happens when learning takes place?" and "What happens in human learning?" It provides a detailed analysis of how learning takes place without any direct transfer of…

  6. Guided Learning at Work.

    Science.gov (United States)

    Billett, Stephen

    2000-01-01

    Guided learning (questioning, diagrams/analogies, modeling, coaching) was studied through critical incident interviews in five workplaces. Participation in everyday work activities was the most effective contributor to workplace learning. Organizational readiness and the efficacy of guided learning in resolving novel tasks were also important. (SK)

  7. Adult Learning Assumptions

    Science.gov (United States)

    Baskas, Richard S.

    2011-01-01

    The purpose of this study is to examine Knowles' theory of andragogy and his six assumptions of how adults learn while providing evidence to support two of his assumptions based on the theory of andragogy. As no single theory explains how adults learn, it can best be assumed that adults learn through the accumulation of formal and informal…

  8. Learning analytics dashboard applications

    NARCIS (Netherlands)

    Verbert, K.; Duval, E.; Klerkx, J.; Govaerts, S.; Santos, J.L.

    2013-01-01

    This article introduces learning analytics dashboards that visualize learning traces for learners and teachers. We present a conceptual framework that helps to analyze learning analytics applications for these kinds of users. We then present our own work in this area and compare with 15 related

  9. Innovazione nel mobile learning

    Directory of Open Access Journals (Sweden)

    Immaculada Arnedillo-Sànchez

    2008-01-01

    Full Text Available Descrizione, da una prospettiva europea, dell’innovazione nel settore del mobile learning e l’utilizzabilita’ del mobile learning in contesti educativi. Vengono illustrate i principali progetti europei di m-learning e si esamina le prospettive pedagogiche e teoriche relative al campo.

  10. Under Threes' Mathematical Learning

    Science.gov (United States)

    Franzén, Karin

    2015-01-01

    The article focuses on mathematics for toddlers in preschool, with the aim of challenging a strong learning discourse that mainly focuses on cognitive learning. By devoting more attention to other perspectives on learning, the hope is to better promote children's early mathematical development. Sweden is one of few countries to have a curriculum…

  11. Learning from Errors

    Science.gov (United States)

    Metcalfe, Janet

    2017-01-01

    Although error avoidance during learning appears to be the rule in American classrooms, laboratory studies suggest that it may be a counterproductive strategy, at least for neurologically typical students. Experimental investigations indicate that errorful learning followed by corrective feedback is beneficial to learning. Interestingly, the…

  12. Learning from Errors

    OpenAIRE

    Martínez-Legaz, Juan Enrique; Soubeyran, Antoine

    2003-01-01

    We present a model of learning in which agents learn from errors. If an action turns out to be an error, the agent rejects not only that action but also neighboring actions. We find that, keeping memory of his errors, under mild assumptions an acceptable solution is asymptotically reached. Moreover, one can take advantage of big errors for a faster learning.

  13. Learning from Failed Decisions

    Science.gov (United States)

    Nutt, Paul C.

    2010-01-01

    The consequences and dilemmas posed by learning issues for decision making are discussed. Learning requires both awareness of barriers and a coping strategy. The motives to hold back information essential for learning stem from perverse incentives, obscure outcomes, and the hindsight bias. There is little awareness of perverse incentives that…

  14. E-Learning Agents

    Science.gov (United States)

    Gregg, Dawn G.

    2007-01-01

    Purpose: The purpose of this paper is to illustrate the advantages of using intelligent agents to facilitate the location and customization of appropriate e-learning resources and to foster collaboration in e-learning environments. Design/methodology/approach: This paper proposes an e-learning environment that can be used to provide customized…

  15. A rank-based algorithm of differential expression analysis for small cell line data with statistical control.

    Science.gov (United States)

    Li, Xiangyu; Cai, Hao; Wang, Xianlong; Ao, Lu; Guo, You; He, Jun; Gu, Yunyan; Qi, Lishuang; Guan, Qingzhou; Lin, Xu; Guo, Zheng

    2017-10-13

    To detect differentially expressed genes (DEGs) in small-scale cell line experiments, usually with only two or three technical replicates for each state, the commonly used statistical methods such as significance analysis of microarrays (SAM), limma and RankProd (RP) lack statistical power, while the fold change method lacks any statistical control. In this study, we demonstrated that the within-sample relative expression orderings (REOs) of gene pairs were highly stable among technical replicates of a cell line but often widely disrupted after certain treatments such like gene knockdown, gene transfection and drug treatment. Based on this finding, we customized the RankComp algorithm, previously designed for individualized differential expression analysis through REO comparison, to identify DEGs with certain statistical control for small-scale cell line data. In both simulated and real data, the new algorithm, named CellComp, exhibited high precision with much higher sensitivity than the original RankComp, SAM, limma and RP methods. Therefore, CellComp provides an efficient tool for analyzing small-scale cell line data. © The Author 2017. Published by Oxford University Press.

  16. How People Learn in an Asynchronous Online Learning Environment: The Relationships between Graduate Students' Learning Strategies and Learning Satisfaction

    Science.gov (United States)

    Choi, Beomkyu

    2016-01-01

    The purpose of this study was to examine the relationships between learners' learning strategies and learning satisfaction in an asynchronous online learning environment. In an attempt to shed some light on how people learn in an online learning environment, one hundred and sixteen graduate students who were taking online learning courses…

  17. A Flow of Entrepreneurial Learning Elements in Experiential Learning Settings

    DEFF Research Database (Denmark)

    Ramsgaard, Michael Breum; Christensen, Marie Ernst

    This paper explored the concept of learning in an experiential learning setting and whether the learning process can be understood as a flow of learning factors influencing the outcome. If many constituting factors lead to the development of learning outcomes, there might need to be developed...... that are a part of experiential learning settings and curriculum development....... a differentiated approach to facilitate experiential learning. Subsequently the paper investigated how facilitators of learning processes can design a learning space where the boundary of what is expected from the learner is challenged. In other words the aim was to explore the transformative learning processes...

  18. Critical dynamics in associative memory networks

    Directory of Open Access Journals (Sweden)

    Maximilian eUhlig

    2013-07-01

    Full Text Available Critical behavior in neural networks is characterized by scale-free avalanche size distributions and can be explained by self-regulatory mechanisms. Theoretical and experimental evidence indicates that information storage capacity reaches its maximum in the critical regime. We study the effect of structural connectivity formed by Hebbian learning on the criticality of network dynamics. The network endowed with Hebbian learning only does not allow for simultaneous information storage and criticality. However, the critical regime is can be stabilized by short-term synaptic dynamics in the form of synaptic depression and facilitation or, alternatively, by homeostatic adaptation of the synaptic weights. We show that a heterogeneous distribution of maximal synaptic strengths does not preclude criticality if the Hebbian learning is alternated with periods of critical dynamics recovery. We discuss the relevance of these findings for the flexibility of memory in aging and with respect to the recent theory of synaptic plasticity.

  19. Learning in context

    DEFF Research Database (Denmark)

    Keiding, Tina Bering

    2007-01-01

    This article offers a re-description of the concept of learning context. Drawing on Niklas Luhmann and Gregory Bateson it suggests an alternative to situated, social learning and activity theory. The conclusion is that learning context designates an individual's reconstruction of the environment...... through contingent handling of differences and that the individual emerge as learning through the actual construction. Selection of differences is influenced by the learner's actual knowledge, the nature of the environment and the current horizon of meaning in which the current adaptive perspective...... becomes a significant factor. The re-description contributes to didaktik  through renewed understandings of participants' background in teaching and learning....

  20. Political learning among youth

    DEFF Research Database (Denmark)

    Solhaug, Trond; Kristensen, Niels Nørgaard

    2014-01-01

    This article focuses on students’ first political learning and explores the research question, what dynamic patterns of political learning can be explored among a selection of young, diverse Danish students’ first political interests? The authors use theories of learning in their analytical......, but are active constructors of their political life. Their emotions and social environment are highly important for their political orientation. It is recommended that further research focus on dynamic learning and on arenas for political learning rather than on “single agent studies.” Recommendations...

  1. Quantum machine learning.

    Science.gov (United States)

    Biamonte, Jacob; Wittek, Peter; Pancotti, Nicola; Rebentrost, Patrick; Wiebe, Nathan; Lloyd, Seth

    2017-09-13

    Fuelled by increasing computer power and algorithmic advances, machine learning techniques have become powerful tools for finding patterns in data. Quantum systems produce atypical patterns that classical systems are thought not to produce efficiently, so it is reasonable to postulate that quantum computers may outperform classical computers on machine learning tasks. The field of quantum machine learning explores how to devise and implement quantum software that could enable machine learning that is faster than that of classical computers. Recent work has produced quantum algorithms that could act as the building blocks of machine learning programs, but the hardware and software challenges are still considerable.

  2. Rethinking expansive learning

    DEFF Research Database (Denmark)

    Kolbæk, Ditte; Lundh Snis, Ulrika

    Abstract: This paper analyses an online community of master’s students taking a course in ICT and organisational learning. The students initiated and facilitated an educational design for organisational learning called Proactive Review in the organisation where they are employed. By using an online...... discussion forum on Google groups, they created new ways of reflecting and learning. We used netnography to select qualitative postings from the online community and expansive learning concepts for data analysis. The findings show how students changed practices of organisational learning...

  3. Machine learning with R

    CERN Document Server

    Lantz, Brett

    2013-01-01

    Written as a tutorial to explore and understand the power of R for machine learning. This practical guide that covers all of the need to know topics in a very systematic way. For each machine learning approach, each step in the process is detailed, from preparing the data for analysis to evaluating the results. These steps will build the knowledge you need to apply them to your own data science tasks.Intended for those who want to learn how to use R's machine learning capabilities and gain insight from your data. Perhaps you already know a bit about machine learning, but have never used R; or

  4. Learning Analytics for Supporting Seamless Language Learning Using E-Book with Ubiquitous Learning System

    Science.gov (United States)

    Mouri, Kousuke; Uosaki, Noriko; Ogata, Hiroaki

    2018-01-01

    Seamless learning has been recognized as an effective learning approach across various dimensions including formal and informal learning contexts, individual and social learning, and physical world and cyberspace. With the emergence of seamless learning, the majority of the current research focuses on realizing a seamless learning environment at…

  5. FLIPPED LEARNING: PRACTICAL ASPECTS

    Directory of Open Access Journals (Sweden)

    Olena Kuzminska

    2016-03-01

    Full Text Available The article is devoted to issues of implementation of the flipped learning technology in the practice of higher education institutions. The article defines the principles of technology and a model of the educational process, it notes the need to establish an information support system. The article defines online platforms and resources; it describes recommendations for the design of electronic training courses and organization of the students in the process of implementing the proposed model, as well as tools for assessing its effectiveness. The article provides a description of flipped learning implementation scenario and formulates suggestions regarding the use of this model as a mechanism to improve the efficiency of the learning process in the ICT-rich environment of high school: use of learning management systems (LMS and personal learning environments (PLE of participants in a learning process. The article provides an example of implementation of the flipped learning model as a part of the Information Technologies course in the National University of Life and Environmental Sciences of Ukraine (NULES. The article gives examples of tasks, resources and services, results of students’ research activity, as well as an example of the personal learning network, established in the course of implementation of the flipped learning model and elements of digital student portfolios. It presents the results of the monitoring of learning activities and students’ feedback. The author describes cautions against the mass introduction of the flipped learning model without monitoring of readiness of the participants of the educational process for its implementation

  6. Learning and memory

    Directory of Open Access Journals (Sweden)

    P. A. J. Ryke

    1989-03-01

    Full Text Available Under various circumstances and in different species the outward expression of learning varies considerably, and this has led to the classification of different categories of learning. Just as there is no generally agreed on definition of learning, there is no one system of classification. Types of learning commonly recognized are: Habituation, sensitization, classical conditioning, operant conditioning, trial and error, taste aversion, latent learning, cultural learning, imprinting, insight learning, learning-set learning and instinct. The term memory must include at least two separate processes. It must involve, on the one hand, that of learning something and on the other, at some later date, recalling that thing. What lies between the learning and (he remembering must be some permanent record — a memory trace — within the brain. Memory exists in at least two forms: memory for very recent events (short-term which is relatively labile and easily disruptable; and long-term memory, which is much more stable. Not everything that gets into short-term memory becomes fixed in the long-term store; a filtering mechanism selects things that might be important and discards the rest.

  7. Approximate kernel competitive learning.

    Science.gov (United States)

    Wu, Jian-Sheng; Zheng, Wei-Shi; Lai, Jian-Huang

    2015-03-01

    Kernel competitive learning has been successfully used to achieve robust clustering. However, kernel competitive learning (KCL) is not scalable for large scale data processing, because (1) it has to calculate and store the full kernel matrix that is too large to be calculated and kept in the memory and (2) it cannot be computed in parallel. In this paper we develop a framework of approximate kernel competitive learning for processing large scale dataset. The proposed framework consists of two parts. First, it derives an approximate kernel competitive learning (AKCL), which learns kernel competitive learning in a subspace via sampling. We provide solid theoretical analysis on why the proposed approximation modelling would work for kernel competitive learning, and furthermore, we show that the computational complexity of AKCL is largely reduced. Second, we propose a pseudo-parallelled approximate kernel competitive learning (PAKCL) based on a set-based kernel competitive learning strategy, which overcomes the obstacle of using parallel programming in kernel competitive learning and significantly accelerates the approximate kernel competitive learning for large scale clustering. The empirical evaluation on publicly available datasets shows that the proposed AKCL and PAKCL can perform comparably as KCL, with a large reduction on computational cost. Also, the proposed methods achieve more effective clustering performance in terms of clustering precision against related approximate clustering approaches. Copyright © 2014 Elsevier Ltd. All rights reserved.

  8. Motion Learning Based on Bayesian Program Learning

    Directory of Open Access Journals (Sweden)

    Cheng Meng-Zhen

    2017-01-01

    Full Text Available The concept of virtual human has been highly anticipated since the 1980s. By using computer technology, Human motion simulation could generate authentic visual effect, which could cheat human eyes visually. Bayesian Program Learning train one or few motion data, generate new motion data by decomposing and combining. And the generated motion will be more realistic and natural than the traditional one.In this paper, Motion learning based on Bayesian program learning allows us to quickly generate new motion data, reduce workload, improve work efficiency, reduce the cost of motion capture, and improve the reusability of data.

  9. Blended Learning or E-learning?

    OpenAIRE

    Tayebinik, Maryam; Puteh, Marlia

    2013-01-01

    ICT or Information and Communication Technology has pervaded the fields of education.In recent years the term e-learning has emerged as a result of the integration of ICT in the education fields. Following the application this technology into teaching, some pitfalls have been identified and this have led to the Blended learning phenomenon.However the preference on this new method has been debated quite extensively.The aim of this paper is to investigate the advantages of blended learning over...

  10. Evaluation and Policy Learning

    DEFF Research Database (Denmark)

    Borrás, Susana; Højlund, Steven

    2015-01-01

    This article examines how evaluation induces policy learning – a question largely neglected by the scholarly literature on evaluation and policy learning. Following a learner's perspective, the article attempts to ascertain who the learners are, and what, and how, learners actually learn from...... evaluations. In so doing, it focuses on what different types of learners actually learn within the context of the evaluation framework (the set of administrative structures defining the evaluation goals and process). Taking the empirical case of three EU programme evaluations, the patterns of policy learning...... emanating from them are examined. The findings are that only two types of actors involved in the evaluation are actually learning (programme units and external evaluators), that learners learn different things (programme overview, small-scale programme adjustments, policy change and evaluation methods...

  11. Introduction to machine learning.

    Science.gov (United States)

    Baştanlar, Yalin; Ozuysal, Mustafa

    2014-01-01

    The machine learning field, which can be briefly defined as enabling computers make successful predictions using past experiences, has exhibited an impressive development recently with the help of the rapid increase in the storage capacity and processing power of computers. Together with many other disciplines, machine learning methods have been widely employed in bioinformatics. The difficulties and cost of biological analyses have led to the development of sophisticated machine learning approaches for this application area. In this chapter, we first review the fundamental concepts of machine learning such as feature assessment, unsupervised versus supervised learning and types of classification. Then, we point out the main issues of designing machine learning experiments and their performance evaluation. Finally, we introduce some supervised learning methods.

  12. Semantic Learning Service Personalized

    Directory of Open Access Journals (Sweden)

    Yibo Chen

    2012-02-01

    Full Text Available To provide users with more suitable and personalized service, personalization is widely used in various fields. Current e-Learning systems search for learning resources using information search technology, based on the keywords that selected or inputted by the user. Due to lack of semantic analysis for keywords and exploring the user contexts, the system cannot provide a good learning experiment. In this paper, we defined the concept and characteristic of the personalized learning service, and proposed a semantic learning service personalized framework. Moreover, we made full use of semantic technology, using ontologies to represent the learning contents and user profile, mining and utilizing the friendship and membership of the social relationship to construct the user social relationship profile, and improved the collaboration filtering algorithm to recommend personalized learning resources for users. The results of the empirical evaluation show that the approach is effectiveness in augmenting recommendation.

  13. Infant Statistical Learning

    Science.gov (United States)

    Saffran, Jenny R.; Kirkham, Natasha Z.

    2017-01-01

    Perception involves making sense of a dynamic, multimodal environment. In the absence of mechanisms capable of exploiting the statistical patterns in the natural world, infants would face an insurmountable computational problem. Infant statistical learning mechanisms facilitate the detection of structure. These abilities allow the infant to compute across elements in their environmental input, extracting patterns for further processing and subsequent learning. In this selective review, we summarize findings that show that statistical learning is both a broad and flexible mechanism (supporting learning from different modalities across many different content areas) and input specific (shifting computations depending on the type of input and goal of learning). We suggest that statistical learning not only provides a framework for studying language development and object knowledge in constrained laboratory settings, but also allows researchers to tackle real-world problems, such as multilingualism, the role of ever-changing learning environments, and differential developmental trajectories. PMID:28793812

  14. Exploitative Learning by Exporting

    DEFF Research Database (Denmark)

    Golovko, Elena; Lopes Bento, Cindy; Sofka, Wolfgang

    Decisions on entering foreign markets are among the most challenging but also potentially rewarding strategy choices managers can make. In this study, we examine the effect of export entry on the firm investment decisions in two activities associated with learning about new technologies...... and learning about new markets ? R&D investments and marketing investments, in search of novel insights into the content and process underlying learning by exporting. We draw from organizational learning theory for predicting changes in both R&D and marketing investment patterns that accompany firm entry......, it is predominantly the marketing-related investment decisions associated with starting to export that lead to increases in firm productivity. We conclude that learning-by-exporting might be more properly characterized as ?learning about and exploiting new markets? rather than ?learning about new technologies...

  15. Learning as Negotiating Identities

    DEFF Research Database (Denmark)

    Jørgensen, Kenneth Mølbjerg; Keller, Hanne Dauer

    The paper explores the contribution of Communities of Practice (COP) to Human Resource Development (HRD). Learning as negotiating identities captures the contribution of COP to HRD. In COP the development of practice happens through negotiation of meaning. The learning process also involves modes...... of belonging constitutive of our identities. We suggest that COP makes a significant contribution by linking learning and identification. This means that learning becomes much less instrumental and much more linked to fundamental questions of being. We argue that the COP-framework links learning with the issue...... of time - caught in the notion of trajectories of learning - that integrate past, present and future. Working with the learners' notion of time is significant because it is here that new learning possibilities become visible and meaningful for individuals. Further, we argue that the concept of identity...

  16. Lessons learned bulletin

    International Nuclear Information System (INIS)

    1994-05-01

    During the past four years, the Department of Energy -- Savannah River Operations Office and the Westinghouse Savannah River Company (WSRC) Environmental Restoration (ER) Program completed various activities ranging from waste site investigations to closure and post closure projects. Critiques for lessons learned regarding project activities are performed at the completion of each project milestone, and this critique interval allows for frequent recognition of lessons learned. In addition to project related lessons learned, ER also performs lessons learned critiques. T'he Savannah River Site (SRS) also obtains lessons learned information from general industry, commercial nuclear industry, naval nuclear programs, and other DOE sites within the complex. Procedures are approved to administer the lessons learned program, and a database is available to catalog applicable lessons learned regarding environmental remediation, restoration, and administrative activities. ER will continue to use this database as a source of information available to SRS personnel

  17. Is mobile learning a substitute for electronic learning?

    OpenAIRE

    Sitthiworachart, Jirarat; Joy, Mike

    2008-01-01

    Mobile learning is widely regarded as the next generation of learning technologies, and refers to the use of mobile devices in education to enhance learning activities. The increasing use of mobile devices has encouraged research into the capabilities of mobile learning systems. Many questions arise about mobile learning, such as whether mobile learning can be a substitute for electronic learning, what the potential benefits and problems of utilizing mobile devices in education are, and what ...

  18. Emergent learning and learning ecologies in Web 2.0

    OpenAIRE

    Williams, Roy; Karousou, Regina; Mackness, J.

    2011-01-01

    This paper describes emergent learning and situates it within learning networks and systems and the broader learning ecology of Web 2.0. It describes the nature of emergence and emergent learning and the conditions that enable emergent, self-organised learning to occur and to flourish. Specifically, it explores whether emergent learning can be validated and self-correcting and whether it is possible to link or integrate emergent and prescribed learning. It draws on complexity theory, commu...

  19. Workplaces as Transformative Learning Spaces

    DEFF Research Database (Denmark)

    Maslo, Elina

    2010-01-01

    some other examples on “successful learning” from the formal, informal and non-formal learning environments, trying to prove those criteria. This presentation provides a view on to new examples on transformative learning spaces we discovered doing research on Workplace Learning in Latvia as a part......Abstract to the Vietnam Forum on Lifelong Learning: Building a Learning Society Hanoi, 7-8 December 2010 Network 2: Competence development as Workplace Learning Title of proposal: Workplaces as Transformative Learning Spaces Author: Elina Maslo, dr. paed., University of Latvia, elina@latnet.lv Key...... words: learning, lifelong learning, adult learning, workplace learning, transformative learning spaces During many years of research on lifelong foreign language learning with very different groups of learners, we found some criteria, which make learning process successful. Since then we tried to find...

  20. Flexible learning intinerary vs. linear learning itinerary

    OpenAIRE

    Martín San José, Juan Fernando; Juan Lizandra, María Carmen; Gil Gómez, Jose Antonio; Rando, Noemí

    2014-01-01

    The latest video game and entertainment technology and other technologies are facilitating the development of new and powerful e-Learning systems. In this paper, we present a computer-based game for learning about five historical ages. The objective of the game is to reinforce the events that mark the transition from one historical age to another and the order of the historical ages. Our game incorporates natural human-computer interaction based on video game technology, Frontal Projection, a...

  1. LEARNING HOW TO LEARN A LANGUAGE

    CERN Multimedia

    Language Training; Tel. 73127; Andrée Fontbonne; Tel. 72844

    2001-01-01

    This bilingual seminar is for anyone who would like to develop learning strategies and skills for learning a foreign language. Languages: French and English. Length: 3 days, 7 hours per day. Dates: 7, 8, 9 March 2001. Price: 462 CHF per person (for a group of 8 people). If you are interested, please enrol through our Web pages: http://training.web.cern.ch/Training/LANG/lang0_F.html

  2. LEARNING HOW TO LEARN A LANGUAGE

    CERN Multimedia

    Moniek Laurent

    2002-01-01

    This bilingual seminar is for anyone who would like to develop learning strategies and skills for learning a foreign language. Languages: French and English. Length: 3 days, 7 hours per day. Dates: 4, 5, 6 March 2002. Price: 460 CHF per person (for a group of 8 people). If you are interested, please enrol through our Web pages: http://cern.ch/Training   Language Training Moniek Laurent Tel. 78582 moniek.laurent@cern.ch

  3. LEARNING HOW TO LEARN A LANGUAGE

    CERN Multimedia

    Formation en Langues; Andrée Fontbonne - Tél. 72844; Language Training; Françoise Benz - Tel. 73127; Andrée Fontbonne - Tel. 72844

    2000-01-01

    This bilingual seminar is for anyone who would like to develop learning strategies and skills for learning a foreign language. It is particularly recommended for those wishing to sign up for a 3-month self-study session in the Resource Centre. Languages: French and English. Length: 5 hours a day for one week. Dates: 27 November to December 2000. Price: 490 CHF per person (for a group of 8 people). If you are interested, please enrol through our Web pages.

  4. Learning to practice: Practicing to learn

    OpenAIRE

    McBride, F.

    2005-01-01

    There is clearly a lack of consensus regarding the terminology used to describe the APStraciJ eXp 0jtatjon of knowledge in an organisational context. The theory of knowledge exploitation is bound up in various concepts, the most familiar being Organisational Learning, Knowledge Management and the Learning Organisation. This report is an enquiry into the applicability of these concepts to the design led architectural practice. Implicit within this study is a suggestion that the firm can be suc...

  5. CULTURAL VARIATIONS IN LEARNING AND LEARNING STYLES

    Directory of Open Access Journals (Sweden)

    Pegah OMIDVAR,, Putra University, MALAYSIA

    2012-08-01

    Full Text Available The need for cross-cultural understanding of the relationship between culture and learning style is becoming increasingly important because of the changing cultural mix of classrooms and society at large. The research done regarding the two variables is mostly quantitative. This review summarizes results of the existing research on cultural variations in learning styles. Limitations of the existing studies are discussed and some suggestion for future research is proposed.

  6. Learning from neural control.

    Science.gov (United States)

    Wang, Cong; Hill, David J

    2006-01-01

    One of the amazing successes of biological systems is their ability to "learn by doing" and so adapt to their environment. In this paper, first, a deterministic learning mechanism is presented, by which an appropriately designed adaptive neural controller is capable of learning closed-loop system dynamics during tracking control to a periodic reference orbit. Among various neural network (NN) architectures, the localized radial basis function (RBF) network is employed. A property of persistence of excitation (PE) for RBF networks is established, and a partial PE condition of closed-loop signals, i.e., the PE condition of a regression subvector constructed out of the RBFs along a periodic state trajectory, is proven to be satisfied. Accurate NN approximation for closed-loop system dynamics is achieved in a local region along the periodic state trajectory, and a learning ability is implemented during a closed-loop feedback control process. Second, based on the deterministic learning mechanism, a neural learning control scheme is proposed which can effectively recall and reuse the learned knowledge to achieve closed-loop stability and improved control performance. The significance of this paper is that the presented deterministic learning mechanism and the neural learning control scheme provide elementary components toward the development of a biologically-plausible learning and control methodology. Simulation studies are included to demonstrate the effectiveness of the approach.

  7. Theoretical Foundations of Active Learning

    Science.gov (United States)

    2009-05-01

    I study the informational complexity of active learning in a statistical learning theory framework. Specifically, I derive bounds on the rates of...convergence achievable by active learning , under various noise models and under general conditions on the hypothesis class. I also study the theoretical...advantages of active learning over passive learning, and develop procedures for transforming passive learning algorithms into active learning algorithms

  8. The Army Learning Organisation Workshop

    Science.gov (United States)

    2013-06-01

    learning • Sharing information • Learning resulting in purposeful action • Creating environments that promote learning • Technology and resources...individual and collective learning • Exploiting and investing in technology to facilitate learning (i.e. blended and E- learning ) • Lifelong or...opportunities provided by training and education programs. More significantly, participants noted the multi-layered nature of informal and formal learning

  9. Active Learning Through Discussion in E-Learning

    OpenAIRE

    Daru Wahyuningsih

    2016-01-01

    Active learning is generally made by a lecturer in learning face to face. In the face to face learning, lecturer can implement a variety of teaching methods to make students actively involved in learning. This is different from learning that is actuating in e-learning. The main characteristic of e-learning is learning that can take place anytime and anywhere. Special strategies are needed so that lecturer can make students play an active role in the course of e-learning. Research in order to ...

  10. Zero Learning: Case explorations of barriers to organizational learning

    DEFF Research Database (Denmark)

    Jørgensen, Frances; S., Jacob

    2003-01-01

    that the existence of learning barriers may not only inhibit on-going learning process, but also lead to a negative cycle of non-learning in the organization. The implications of a "zero learning" cycle caused by learning barriers are discussed and insights are provided as to how barriers may be resolved so...

  11. Seamless Language Learning: Second Language Learning with Social Media

    Science.gov (United States)

    Wong, Lung-Hsiang; Chai, Ching Sing; Aw, Guat Poh

    2017-01-01

    This conceptual paper describes a language learning model that applies social media to foster contextualized and connected language learning in communities. The model emphasizes weaving together different forms of language learning activities that take place in different learning contexts to achieve seamless language learning. it promotes social…

  12. Learning "While" Working: Success Stories on Workplace Learning in Europe

    Science.gov (United States)

    Lardinois, Rocio

    2011-01-01

    Cedefop's report "Learning while working: success stories on workplace learning in Europe" presents an overview of key trends in adult learning in the workplace. It takes stock of previous research carried out by Cedefop between 2003 and 2010 on key topics for adult learning: governance and the learning regions; social partner roles in…

  13. Can Social Learning Increase Learning Speed, Performance or Both?

    NARCIS (Netherlands)

    Heinerman, J.V.; Stork, J.; Rebolledo Coy, M.A.; Hubert, J.G.; Eiben, A.E.; Bartz-Beielstein, Thomas; Haasdijk, Evert

    2017-01-01

    Social learning enables multiple robots to share learned experiences while completing a task. The literature offers contradicting examples of its benefits; robots trained with social learning reach a higher performance, an increased learning speed, or both, compared to their individual learning

  14. Toward a Social Approach to Learning in Community Service Learning

    Science.gov (United States)

    Cooks, Leda; Scharrer, Erica; Paredes, Mari Castaneda

    2004-01-01

    The authors describe a social approach to learning in community service learning that extends the contributions of three theoretical bodies of scholarship on learning: social constructionism, critical pedagogy, and community service learning. Building on the assumptions about learning described in each of these areas, engagement, identity, and…

  15. Deep learning: Using machine learning to study biological vision

    OpenAIRE

    Majaj, Najib; Pelli, Denis

    2017-01-01

    Today most vision-science presentations mention machine learning. Many neuroscientists use machine learning to decode neural responses. Many perception scientists try to understand recognition by living organisms. To them, machine learning offers a reference of attainable performance based on learned stimuli. This brief overview of the use of machine learning in biological vision touches on its strengths, weaknesses, milestones, controversies, and current directions.

  16. Web-based newborn screening system for metabolic diseases: machine learning versus clinicians.

    Science.gov (United States)

    Chen, Wei-Hsin; Hsieh, Sheau-Ling; Hsu, Kai-Ping; Chen, Han-Ping; Su, Xing-Yu; Tseng, Yi-Ju; Chien, Yin-Hsiu; Hwu, Wuh-Liang; Lai, Feipei

    2013-05-23

    A hospital information system (HIS) that integrates screening data and interpretation of the data is routinely requested by hospitals and parents. However, the accuracy of disease classification may be low because of the disease characteristics and the analytes used for classification. The objective of this study is to describe a system that enhanced the neonatal screening system of the Newborn Screening Center at the National Taiwan University Hospital. The system was designed and deployed according to a service-oriented architecture (SOA) framework under the Web services .NET environment. The system consists of sample collection, testing, diagnosis, evaluation, treatment, and follow-up services among collaborating hospitals. To improve the accuracy of newborn screening, machine learning and optimal feature selection mechanisms were investigated for screening newborns for inborn errors of metabolism. The framework of the Newborn Screening Hospital Information System (NSHIS) used the embedded Health Level Seven (HL7) standards for data exchanges among heterogeneous platforms integrated by Web services in the C# language. In this study, machine learning classification was used to predict phenylketonuria (PKU), hypermethioninemia, and 3-methylcrotonyl-CoA-carboxylase (3-MCC) deficiency. The classification methods used 347,312 newborn dried blood samples collected at the Center between 2006 and 2011. Of these, 220 newborns had values over the diagnostic cutoffs (positive cases) and 1557 had values that were over the screening cutoffs but did not meet the diagnostic cutoffs (suspected cases). The original 35 analytes and the manifested features were ranked based on F score, then combinations of the top 20 ranked features were selected as input features to support vector machine (SVM) classifiers to obtain optimal feature sets. These feature sets were tested using 5-fold cross-validation and optimal models were generated. The datasets collected in year 2011 were used as

  17. Blended learning in anatomy

    DEFF Research Database (Denmark)

    Østergaard, Gert Værge; Brogner, Heidi Marie

    behind DBR is that new knowledge is generated through processes that simultaneously develop, test and improve a design, in this case, an educational design (1) The main principles used in the project is blended learning and flipped learning (2). …"I definitely learn best in practice, but the theory...... in working with the assignments in the classroom."... External assesor, observer and interviewer Based on the different evaluations, the conclusion are that the blended learning approach combined with the ‘flipped classroom’ is a very good way to learn and apply the anatomy, both for the students......The aim of the project was to bridge the gap between theory and practice by working more collaboratively, both peer-to-peer and between student and lecturer. Furthermore the aim was to create active learning environments. The methodology of the project is Design-Based Research (DBR). The idea...

  18. Cultural dimensions of learning

    Science.gov (United States)

    Eyford, Glen A.

    1990-06-01

    How, what, when and where we learn is frequently discussed, as are content versus process, or right brain versus left brain learning. What is usually missing is the cultural dimension. This is not an easy concept to define, but various aspects can be identified. The World Decade for Cultural Development emphasizes the need for a counterbalance to a quantitative, economic approach. In the last century poets also warned against brutalizing materialism, and Sorokin and others have described culture more recently in terms of cohesive basic values expressed through aesthetics and institutions. Bloom's taxonomy incorporates the category of affective learning, which internalizes values. If cultural learning goes beyond knowledge acquisition, perhaps the surest way of understanding the cultural dimension of learning is to examine the aesthetic experience. This can use myths, metaphors and symbols, and to teach and learn by using these can help to unlock the human potential for vision and creativity.

  19. Learning through reactions

    DEFF Research Database (Denmark)

    Hasse, Cathrine

    2007-01-01

    Universities can from the student?s point of view be seen as places of learning an explicit curriculum of a particular discipline. From a fieldwork among physicist students at the Niels Bohr Institute in Denmark, I argue that the learning of cultural code-curricula in higher educational...... institutions designate in ambiguous ways. I argue claim that students also have to learn institutional cultural codes, which are not the explicit curricula presented in textbooks, but a socially designated cultural code-curricula learned through everyday interactions at the university institutes. I further...... argue that this code-curriculum is learned through what I shall term indefinite learning processes, which are mainly pre-discursive to the newcomer...

  20. Cultural Learning Redux.

    Science.gov (United States)

    Tomasello, Michael

    2016-05-01

    M. Tomasello, A. Kruger, and H. Ratner (1993) proposed a theory of cultural learning comprising imitative learning, instructed learning, and collaborative learning. Empirical and theoretical advances in the past 20 years suggest modifications to the theory; for example, children do not just imitate but overimitate in order to identify and affiliate with others in their cultural group, children learn from pedagogy not just episodic facts but the generic structure of their cultural worlds, and children collaboratively co-construct with those in their culture normative rules for doing things. In all, human children do not just culturally learn useful instrumental activities and information, they conform to the normative expectations of the cultural group and even contribute themselves to the creation of such normative expectations. © 2016 The Author. Child Development © 2016 Society for Research in Child Development, Inc.

  1. Problem Based Learning

    DEFF Research Database (Denmark)

    de Graaff, Erik; Guerra, Aida

    , the key principles remain the same everywhere. Graaff & Kolmos (2003) identify the main PBL principles as follows: 1. Problem orientation 2. Project organization through teams or group work 3. Participant-directed 4. Experiental learning 5. Activity-based learning 6. Interdisciplinary learning and 7...... model and in general problem based and project based learning. We apply the principle of teach as you preach. The poster aims to outline the visitors’ workshop programme showing the results of some recent evaluations.......Problem-Based Learning (PBL) is an innovative method to organize the learning process in such a way that the students actively engage in finding answers by themselves. During the past 40 years PBL has evolved and diversified resulting in a multitude in variations in models and practices. However...

  2. Deep learning with Python

    CERN Document Server

    Chollet, Francois

    2018-01-01

    DESCRIPTION Deep learning is applicable to a widening range of artificial intelligence problems, such as image classification, speech recognition, text classification, question answering, text-to-speech, and optical character recognition. Deep Learning with Python is structured around a series of practical code examples that illustrate each new concept introduced and demonstrate best practices. By the time you reach the end of this book, you will have become a Keras expert and will be able to apply deep learning in your own projects. KEY FEATURES • Practical code examples • In-depth introduction to Keras • Teaches the difference between Deep Learning and AI ABOUT THE TECHNOLOGY Deep learning is the technology behind photo tagging systems at Facebook and Google, self-driving cars, speech recognition systems on your smartphone, and much more. AUTHOR BIO Francois Chollet is the author of Keras, one of the most widely used libraries for deep learning in Python. He has been working with deep neural ...

  3. Unsupervised clustering with spiking neurons by sparse temporal coding and multi-layer RBF networks

    NARCIS (Netherlands)

    S.M. Bohte (Sander); J.A. La Poutré (Han); J.N. Kok (Joost)

    2000-01-01

    textabstractWe demonstrate that spiking neural networks encoding information in spike times are capable of computing and learning clusters from realistic data. We show how a spiking neural network based on spike-time coding and Hebbian learning can successfully perform unsupervised clustering on

  4. Pattern recognition & machine learning

    CERN Document Server

    Anzai, Y

    1992-01-01

    This is the first text to provide a unified and self-contained introduction to visual pattern recognition and machine learning. It is useful as a general introduction to artifical intelligence and knowledge engineering, and no previous knowledge of pattern recognition or machine learning is necessary. Basic for various pattern recognition and machine learning methods. Translated from Japanese, the book also features chapter exercises, keywords, and summaries.

  5. Formalized Informal Learning

    DEFF Research Database (Denmark)

    Levinsen, Karin Tweddell; Sørensen, Birgitte Holm

    2013-01-01

    are examined and the relation between network society competences, learners’ informal learning strategies and ICT in formalized school settings over time is studied. The authors find that aspects of ICT like multimodality, intuitive interaction design and instant feedback invites an informal bricoleur approach....... When integrated into certain designs for teaching and learning, this allows for Formalized Informal Learning and support is found for network society competences building....

  6. Deep learning relevance

    DEFF Research Database (Denmark)

    Lioma, Christina; Larsen, Birger; Petersen, Casper

    2016-01-01

    train a Recurrent Neural Network (RNN) on existing relevant information to that query. We then use the RNN to "deep learn" a single, synthetic, and we assume, relevant document for that query. We design a crowdsourcing experiment to assess how relevant the "deep learned" document is, compared...... to existing relevant documents. Users are shown a query and four wordclouds (of three existing relevant documents and our deep learned synthetic document). The synthetic document is ranked on average most relevant of all....

  7. Professional Learning and Collaboration

    OpenAIRE

    Greer, Janet Agnes

    2012-01-01

    The American education system must utilize collaboration to meet the challenges and demands our culture poses for schools. Deeply rooted processes and structures favor teaching and learning in isolation and hinder the shift to a more collaborative paradigm. Professional learning communities (PLCs) support continuous teacher learning, improved efficacy, and program implementation. The PLC provides the framework for the development and enhancement of teacher collaboration and teacher collaborat...

  8. Introduction to machine learning

    OpenAIRE

    Baştanlar, Yalın; Özuysal, Mustafa

    2014-01-01

    The machine learning field, which can be briefly defined as enabling computers make successful predictions using past experiences, has exhibited an impressive development recently with the help of the rapid increase in the storage capacity and processing power of computers. Together with many other disciplines, machine learning methods have been widely employed in bioinformatics. The difficulties and cost of biological analyses have led to the development of sophisticated machine learning app...

  9. Learning Mathematics through Programming

    DEFF Research Database (Denmark)

    Misfeldt, Morten; Ejsing-Duun, Stine

    2015-01-01

    In this paper we explore the potentials for learning mathematics through programming by a combination of theoretically derived potentials and cases of practical pedagogical work. We propose a model with three interdependent learning potentials as programming which can: (1) help reframe the students...... to mathematics is paramount. Analyzing two cases, we suggest a number of ways in which didactical attention to epistemic mediation can support learning mathematics....

  10. Learning Motivation and Achievements

    Institute of Scientific and Technical Information of China (English)

    冯泽野

    2016-01-01

    It is known to all that motivation is one of the most important elements in EFL learning.This study analyzes the type of English learning motivations and learning achievements within non-English majors’ students (Bilingual program in Highway School and Architecture) in Chang’an University, who has been considered English as the foreign language. This thesis intends to put forward certain strategies in promoting foreign language teaching.

  11. Learning Perforce SCM

    CERN Document Server

    Cowham, Robert

    2013-01-01

    Learning Perforce SCM is written in a friendly and practical style with a focus on getting you started with Perforce efficiently and effectively. The book provides plenty of examples and screenshots to guide you through the process of learning.""Learning Perforce SCM"" is for anyone who wants to know how to adeptly manage software development activities using Perforce. Experience with other version control tools is a plus but is not required.

  12. Budgeted Interactive Learning

    Science.gov (United States)

    2017-06-15

    2, and 3). The selection scheme is implemented and released as an open-source active learning package. They have studied theories for designing...We have studied theories for designing algorithms for interactive learning with batch-like feedback (for 1) and algorithms for online digestion of... necessity on pre-training. The new idea provides layer-wise cost estimation with auxiliary nodes, and is applicable to a wider range of deep learning

  13. Neuromorphic Deep Learning Machines

    OpenAIRE

    Neftci, E; Augustine, C; Paul, S; Detorakis, G

    2017-01-01

    An ongoing challenge in neuromorphic computing is to devise general and computationally efficient models of inference and learning which are compatible with the spatial and temporal constraints of the brain. One increasingly popular and successful approach is to take inspiration from inference and learning algorithms used in deep neural networks. However, the workhorse of deep learning, the gradient descent Back Propagation (BP) rule, often relies on the immediate availability of network-wide...

  14. Learning dialog act processing

    OpenAIRE

    Wermter, Stefan; Löchel, Matthias

    1996-01-01

    In this paper we describe a new approach for learning dialog act processing. In this approach we integrate a symbolic semantic segmentation parser with a learning dialog act network. In order to support the unforeseeable errors and variations of spoken language we have concentrated on robust data-driven learning. This approach already compares favorably with the statistical average plausibility method, produces a segmentation and dialog act assignment for all utterances in a robust manner,...

  15. Lifelong Open and Flexible Learning

    DEFF Research Database (Denmark)

    Bang, Jørgen

    2006-01-01

    and Flexible (LOF) learning embracing characteristics as: open learning, distance learning, e-learning, online learning, open accessibility, multimedia support, virtual mobility, learning communities, dual mode (earn & learn) approaches, and the like.In my presentation I will focus on the EADTU strategies...... for creating a synergy network in e-learning – eventually leading to a European Learning Space that supports virtual mobility of students, staff and courses, adds an e-dimension to the Bologa process and facilitates collaboration between universities and the corporate sector....

  16. Feature Inference Learning and Eyetracking

    Science.gov (United States)

    Rehder, Bob; Colner, Robert M.; Hoffman, Aaron B.

    2009-01-01

    Besides traditional supervised classification learning, people can learn categories by inferring the missing features of category members. It has been proposed that feature inference learning promotes learning a category's internal structure (e.g., its typical features and interfeature correlations) whereas classification promotes the learning of…

  17. Develop a Professional Learning Plan

    Science.gov (United States)

    Journal of Staff Development, 2013

    2013-01-01

    A professional learning plan establishes short-and long-term plans for professional learning and implementation of the learning. Such plans guide individuals, schools, districts, and states in coordinating learning experiences designed to achieve outcomes for educators and students. Professional learning plans focus on the program of educator…

  18. The Organization of Informal Learning

    Science.gov (United States)

    Rogoff, Barbara; Callanan, Maureen; Gutiérrez, Kris D.; Erickson, Frederick

    2016-01-01

    Informal learning is often treated as simply an alternative to formal, didactic instruction. This chapter discusses how the organization of informal learning differs across distinct settings but with important commonalities distinguishing informal learning from formal learning: Informal learning is nondidactic, is embedded in meaningful activity,…

  19. Readiness of Adults to Learn Using E-Learning, M-Learning and T-Learning Technologies

    Science.gov (United States)

    Vilkonis, Rytis; Bakanoviene, Tatjana; Turskiene, Sigita

    2013-01-01

    The article presents results of the empirical research revealing readiness of adults to participate in the lifelong learning process using e-learning, m-learning and t-learning technologies. The research has been carried out in the framework of the international project eBig3 aiming at development a new distance learning platform blending virtual…

  20. Holistic evaluations of learning materials

    DEFF Research Database (Denmark)

    Bundsgaard, Jeppe; Hansen, Thomas Illum

    2011-01-01

    The aim of this paper is to present a holistic framework for evaluating learning materials and designs for learning. A holistic evaluation of learning material comprises investigations of - the potential learning potential, i.e. the affordances and challenges of the learning material...

  1. Formative assessment and learning analytics

    NARCIS (Netherlands)

    Tempelaar, D.T.; Heck, A.; Cuypers, H.; van der Kooij, H.; van de Vrie, E.; Suthers, D.; Verbert, K.; Duval, E.; Ochoa, X.

    2013-01-01

    Learning analytics seeks to enhance the learning process through systematic measurements of learning related data, and informing learners and teachers of the results of these measurements, so as to support the control of the learning process. Learning analytics has various sources of information,

  2. Researching workplace learning

    DEFF Research Database (Denmark)

    Jørgensen, Christian Helms; Warring, Niels

    2007-01-01

    This article presents a theoretical and methodological framework for understanding and researching learning in the workplace. The workplace is viewed in a societal context and the learner is viewed as more than an employee in order to understand the learning process in relation to the learner......'s life history.Moreover we will explain the need to establish a 'double view' by examining learning in the workplace both as an objective and as a subjective reality. The article is mainly theoretical, but can also be of interest to practitioners who wish to understand learning in the workplace both...

  3. Recommender Systems for Learning

    CERN Document Server

    Manouselis, Nikos; Verbert, Katrien; Duval, Erik

    2013-01-01

    Technology enhanced learning (TEL) aims to design, develop and test sociotechnical innovations that will support and enhance learning practices of both individuals and organisations. It is therefore an application domain that generally covers technologies that support all forms of teaching and learning activities. Since information retrieval (in terms of searching for relevant learning resources to support teachers or learners) is a pivotal activity in TEL, the deployment of recommender systems has attracted increased interest. This brief attempts to provide an introduction to recommender systems for TEL settings, as well as to highlight their particularities compared to recommender systems for other application domains.

  4. Learning efficient correlated equilibria

    KAUST Repository

    Borowski, Holly P.; Marden, Jason R.; Shamma, Jeff S.

    2014-01-01

    The majority of distributed learning literature focuses on convergence to Nash equilibria. Correlated equilibria, on the other hand, can often characterize more efficient collective behavior than even the best Nash equilibrium. However, there are no existing distributed learning algorithms that converge to specific correlated equilibria. In this paper, we provide one such algorithm which guarantees that the agents' collective joint strategy will constitute an efficient correlated equilibrium with high probability. The key to attaining efficient correlated behavior through distributed learning involves incorporating a common random signal into the learning environment.

  5. Learning and Communicative Rationality

    DEFF Research Database (Denmark)

    Rasmussen, Palle

    The paper is an attempt to outline Habermas' contributions to a theory of learning. Such contributions are found in his work on individual learning and socialization, the constitution and reproduction of lifeworlds, the character of social evolution, the processes of public delibearation...... and democracy and the idea and role of universities. A "theory of learning" is not taken in a very formalised sense, rather the idea is to identify themes where Habermas' theoretical framework provide the opportunity for locating important aspects of learning in modern society....

  6. Strengths-based Learning

    DEFF Research Database (Denmark)

    Ledertoug, Mette Marie

    -being. The Ph.D.-project in Strength-based learning took place in a Danish school with 750 pupils age 6-16 and a similar school was functioning as a control group. The presentation will focus on both the aware-explore-apply processes and the practical implications for the schools involved, and on measurable......Strength-based learning - Children͛s Character Strengths as Means to their Learning Potential͛ is a Ph.D.-project aiming to create a strength-based mindset in school settings and at the same time introducing strength-based interventions as specific tools to improve both learning and well...

  7. The interprofessional learning experience

    DEFF Research Database (Denmark)

    Jakobsen, Flemming; Morcke, Anne Mette; Hansen, Torben Baek

    2017-01-01

    in a safe and challenging learning environment. The shift to the outpatient setting was strongly and practically supported by the management. This study indicates that student learning can be shifted to the outpatient clinic setting if there is supportive management and dedicated supervisors who establish...... a challenging yet safe interprofessional learning environment....... who worked in an interprofessional outpatient orthopaedic clinic from March 2015 to January 2016. The interviews were transcribed and analysed using systematic text condensation. The students’ self-reported learning experience in this outpatient clinic was characterised by direct patient contact...

  8. Perspectives on ontology learning

    CERN Document Server

    Lehmann, J

    2014-01-01

    Perspectives on Ontology Learning brings together researchers and practitioners from different communities − natural language processing, machine learning, and the semantic web − in order to give an interdisciplinary overview of recent advances in ontology learning.Starting with a comprehensive introduction to the theoretical foundations of ontology learning methods, the edited volume presents the state-of-the-start in automated knowledge acquisition and maintenance. It outlines future challenges in this area with a special focus on technologies suitable for pushing the boundaries beyond the c

  9. Learning efficient correlated equilibria

    KAUST Repository

    Borowski, Holly P.

    2014-12-15

    The majority of distributed learning literature focuses on convergence to Nash equilibria. Correlated equilibria, on the other hand, can often characterize more efficient collective behavior than even the best Nash equilibrium. However, there are no existing distributed learning algorithms that converge to specific correlated equilibria. In this paper, we provide one such algorithm which guarantees that the agents\\' collective joint strategy will constitute an efficient correlated equilibrium with high probability. The key to attaining efficient correlated behavior through distributed learning involves incorporating a common random signal into the learning environment.

  10. Editorial: Advanced learning technologies

    Directory of Open Access Journals (Sweden)

    Yu-Ju Lan

    2012-03-01

    Full Text Available Recent rapid development of advanced information technology brings high expectations of its potential to improvement and innovations in learning. This special issue is devoted to using some of the emerging technologies issues related to the topic of education and knowledge sharing, involving several cutting edge research outcomes from recent advancement of learning technologies. Advanced learning technologies are the composition of various related technologies and concepts such as mobile technologies and social media towards learner centered learning. This editorial note provides an overview of relevant issues discussed in this special issue.

  11. Machine Learning for Hackers

    CERN Document Server

    Conway, Drew

    2012-01-01

    If you're an experienced programmer interested in crunching data, this book will get you started with machine learning-a toolkit of algorithms that enables computers to train themselves to automate useful tasks. Authors Drew Conway and John Myles White help you understand machine learning and statistics tools through a series of hands-on case studies, instead of a traditional math-heavy presentation. Each chapter focuses on a specific problem in machine learning, such as classification, prediction, optimization, and recommendation. Using the R programming language, you'll learn how to analyz

  12. Learning in Ressource Mangement

    DEFF Research Database (Denmark)

    Hoffmann, Birgitte; Agger, Annika

    2005-01-01

    This paper explores the roles of NGOs as intermediaries in societal learning processes. The key question is how NGOs facilitate learning in the water sector, and the paper explores the notion of critical friends to grasp their dual position as both partner in the development and critical...... strategies and finally in relation to changes of the water management discourse. Developing the notion ‘critical friend’ the paper discusses how the NGO facilitates learning processes. Traditional approaches to learning are often based on the ‘petrol station pedagogy’ in which knowledge figuratively ‘are...

  13. Georgia - Improved Learning Environment

    Data.gov (United States)

    Millennium Challenge Corporation — The school rehabilitation activity seeks to decrease student and teacher absenteeism, increase students’ time on task, and, ultimately, improve learning and labor...

  14. What is Social Learning?

    Directory of Open Access Journals (Sweden)

    Mark S. Reed

    2010-12-01

    between individual and wider social learning. Many unsubstantiated claims for social learning exist, and there is frequently confusion between the concept itself and its potential outcomes. This lack of conceptual clarity has limited our capacity to assess whether social learning has occurred, and if so, what kind of learning has taken place, to what extent, between whom, when, and how. This response attempts to provide greater clarity on the conceptual basis for social learning. We argue that to be considered social learning, a process must: (1 demonstrate that a change in understanding has taken place in the individuals involved; (2 demonstrate that this change goes beyond the individual and becomes situated within wider social units or communities of practice; and (3 occur through social interactions and processes between actors within a social network. A clearer picture of what we mean by social learning could enhance our ability to critically evaluate outcomes and better understand the processes through which social learning occurs. In this way, it may be possible to better facilitate the desired outcomes of social learning processes.

  15. Manifold Regularized Reinforcement Learning.

    Science.gov (United States)

    Li, Hongliang; Liu, Derong; Wang, Ding

    2018-04-01

    This paper introduces a novel manifold regularized reinforcement learning scheme for continuous Markov decision processes. Smooth feature representations for value function approximation can be automatically learned using the unsupervised manifold regularization method. The learned features are data-driven, and can be adapted to the geometry of the state space. Furthermore, the scheme provides a direct basis representation extension for novel samples during policy learning and control. The performance of the proposed scheme is evaluated on two benchmark control tasks, i.e., the inverted pendulum and the energy storage problem. Simulation results illustrate the concepts of the proposed scheme and show that it can obtain excellent performance.

  16. Learner Managed Learning: Managing To Learn or Learning To Manage?

    Science.gov (United States)

    Harrison, Roger

    2000-01-01

    In the discourse of learner self-management, learners must take responsibility for learning and are offered the possibility of individual autonomy and control. A critical perspective reveals that environmental constraints inhibit the success of technical-rational self-management techniques. An alternative view is the entrepreneurial self, a…

  17. Learning about Learning: Action Learning in Times of Organisational Change

    Science.gov (United States)

    Hill, Robyn

    2009-01-01

    This paper explores the conduct and outcomes of an action learning activity during a period of intense organisational change in a medium-sized vocational education and training organisation in Victoria, Australia. This organisation was the subject of significant change due to government-driven and statewide amalgamation, downsizing and sector…

  18. Organizational learning viewed from a social learning perspective

    DEFF Research Database (Denmark)

    Elkjær, Bente; Brandi, Ulrik

    2011-01-01

    This chapter reviews the literature on organizational learning through the lens of a social learning perspective. We start with an individual learning perspective, before moving on to a social learning perspective with a particular focus upon pragmatism. The literature review covers the following...... four issues: the content of learning, the process of learning, the relation between individual and organization, and the concept of organization. An important separator between individual and social learning perspectives is the different emphasis on learning as acquisition of skills and knowledge......, versus learning as encompassing development of identities and socialization to organizational work and life. A pragmatist social learning perspective emphasizes both learning as acquisition through experience and inquiry, and learning as development of identities and socialization through individuals...

  19. Learned-Helplessness Theory: Implications for Research in Learning Disabilities.

    Science.gov (United States)

    Canino, Frank J.

    1981-01-01

    The application of learned helplessness theory to achievement is discussed within the context of implications for research in learning disabilities. Finally, the similarities between helpless children and learning disabled students in terms of problems solving and attention are discussed. (Author)

  20. Challenges of Implementing Mobile Learning in Distance Learning ...

    African Journals Online (AJOL)

    Challenges of Implementing Mobile Learning in Distance Learning in Tanzania. ... A sample of 450 students were interviewed by using both questionnaire and ... the biggest advantage of M-learning technology- when used, is that it can be ...

  1. Involvement in Learning Revisited: Lessons We Have Learned.

    Science.gov (United States)

    Astin, Alexander W.

    1996-01-01

    Discusses interconnections between the following two national reports: (1) Involvement in Learning; and (2) The Student Learning Imperative. Reviews recent research on student development in order to demonstrate how student affairs professionals can use this information to enhance learning. (SNR)

  2. E-LEARNING-IMPLICATIONS FOR ADULT LEARNING

    Directory of Open Access Journals (Sweden)

    Roxana CRIU

    2013-04-01

    Full Text Available If a few decades ago, the education received in school could be in most of the cases enough to go with for the rest of one’s entire life, today the situation has changed dramatically. The individual has to be prepared for a new type of life and training, namely lifelong learning. The individual’s survival in society could depend on his capacity to learn, to re-qualify, to forget what he once learned and to train for the future in an entirely different manner. Within this context, e-learning and distance education can be viable alternatives for the necessary and imperative adaptation process. Modern man’s education has to go beyond the stage of level oriented education (limited in terms of trainee number and training duration and advance towards continuous education, which is able to train the individual irrespective of his location and with no limitations in terms of time. The passage towards the information society involves mutations in the object of the activities, mainly in terms of selecting, storing, preserving, managing and protecting information. Against this extremely fluctuant background, a relevant question rises: is the adult capable of coping, both individually and socially, with the challenge of e-learning?

  3. Learning environment, learning styles and conceptual understanding

    Science.gov (United States)

    Ferrer, Lourdes M.

    1990-01-01

    In recent years there have been many studies on learners developing conceptions of natural phenomena. However, so far there have been few attempts to investigate how the characteristics of the learners and their environment influence such conceptions. This study began with an attempt to use an instrument developed by McCarthy (1981) to describe learners in Malaysian primary schools. This proved inappropriate as Asian primary classrooms do not provide the same kind of environment as US classrooms. It was decided to develop a learning style checklist to suit the local context and which could be used to describe differences between learners which teachers could appreciate and use. The checklist included four dimensions — perceptual, process, self-confidence and motivation. The validated instrument was used to determine the learning style preferences of primary four pupils in Penang, Malaysia. Later, an analysis was made regarding the influence of learning environment and learning styles on conceptual understanding in the topics of food, respiration and excretion. This study was replicated in the Philippines with the purpose of investigating the relationship between learning styles and achievement in science, where the topics of food, respiration and excretion have been taken up. A number of significant relationships were observed in these two studies.

  4. Mobile learning for teacher professional learning: benefits, obstacles and issues

    OpenAIRE

    Aubusson, Peter; Schuck, Sandy; Burden, Kevin

    2009-01-01

    This paper reflects on the role of mobile learning in teachers’ professional learning. It argues that effective professional learning requires reflection and collaboration and that mobile learning is ideally suited to allow reflection-inaction and to capture the spontaneity of learning moments. The paper also argues for the value of collaborations between teachers and students in professional learning. It suggests that authentic artefacts and anecdotes, captured through mobile technologies, c...

  5. Effective Learning Environments in Relation to Different Learning Theories

    OpenAIRE

    Guney, Ali; Al, Selda

    2012-01-01

    There are diverse learning theories which explain learning processes which are discussed within this paper, through cognitive structure of learning process. Learning environments are usually described in terms of pedagogical philosophy, curriculum design and social climate. There have been only just a few studies about how physical environment is related to learning process. Many researchers generally consider teaching and learning issues as if independent from physical environment, whereas p...

  6. Does peer learning or higher levels of e-learning improve learning abilities?

    DEFF Research Database (Denmark)

    Worm, Bjarne Skjødt; Jensen, Kenneth

    2013-01-01

    The fast development of e-learning and social forums demands us to update our understanding of e-learning and peer learning. We aimed to investigate if higher, pre-defined levels of e-learning or social interaction in web forums improved students' learning ability....

  7. From Learning Object to Learning Cell: A Resource Organization Model for Ubiquitous Learning

    Science.gov (United States)

    Yu, Shengquan; Yang, Xianmin; Cheng, Gang; Wang, Minjuan

    2015-01-01

    This paper presents a new model for organizing learning resources: Learning Cell. This model is open, evolving, cohesive, social, and context-aware. By introducing a time dimension into the organization of learning resources, Learning Cell supports the dynamic evolution of learning resources while they are being used. In addition, by introducing a…

  8. Learning Science, Learning about Science, Doing Science: Different Goals Demand Different Learning Methods

    Science.gov (United States)

    Hodson, Derek

    2014-01-01

    This opinion piece paper urges teachers and teacher educators to draw careful distinctions among four basic learning goals: learning science, learning about science, doing science and learning to address socio-scientific issues. In elaboration, the author urges that careful attention is paid to the selection of teaching/learning methods that…

  9. Learning to learn in the European Reference Framework for lifelong learning

    NARCIS (Netherlands)

    Pirrie, Anne; Thoutenhoofd, Ernst D.

    2013-01-01

    This article explores the construction of learning to learn that is implicit in the document Key Competences for Lifelong LearningEuropean Reference Framework and related education policy from the European Commission. The authors argue that the hallmark of learning to learn is the development of a

  10. The Future of Learning: From eLearning to mLearning.

    Science.gov (United States)

    Keegan, Desmond

    The future of electronic learning was explored in an analysis that viewed the provision of learning at a distance as a continuum and traced the evolution from distance learning to electronic learning to mobile learning in Europe and elsewhere. Special attention was paid to the following topics: (1) the impact of the industrial revolution, the…

  11. The Relationship among Self-Regulated Learning, Procrastination, and Learning Behaviors in Blended Learning Environment

    Science.gov (United States)

    Yamada, Masanori; Goda, Yoshiko; Matsuda, Takeshi; Kato, Hiroshi; Miyagawa, Hiroyuki

    2015-01-01

    This research aims to investigate the relationship among the awareness of self-regulated learning (SRL), procrastination, and learning behaviors in blended learning environment. One hundred seventy nine freshmen participated in this research, conducted in the blended learning style class using learning management system. Data collection was…

  12. From Self-Regulation to Learning to Learn: Observations on the Construction of Self and Learning

    Science.gov (United States)

    Thoutenhoofd, Ernst D.; Pirrie, Anne

    2015-01-01

    The purpose of this article is to clarify the epistemological basis of self-regulated learning. The authors note that learning to learn, a term that has pervaded education policy at EU and national levels in recent years is often conflated with self-regulated learning. As a result, there has been insufficient attention paid to learning as social…

  13. Teaching Problem Based Learning as Blended Learning

    DEFF Research Database (Denmark)

    Kolbæk, Ditte; Nortvig, Anne-Mette

    2018-01-01

    Problem-based and project organized learning (PBL) was originally developed for collaboration between physically present students, but political decisions at many universities require that collaboration, dialogues, and other PBL activities take place online as well. With a theoretical point...... of departure in Dewey and a methodological point of departure in netnography, this study focuses on an online module at Aalborg University where teaching is based on PBL. With the research question ‘How can teachers design for PBL online,’ this study explores the teacher’s role in a six weeks’ blended learning...... program, and we present suggestions for designs for blended learning PBL based on case studies from two PBL courses...

  14. Web-Based Instruction, Learning Effectiveness and Learning Behavior: The Impact of Relatedness

    Science.gov (United States)

    Shieh, Chich-Jen; Liao, Ying; Hu, Ridong

    2013-01-01

    This study aims to discuss the effects of Web-based Instruction and Learning Behavior on Learning Effectiveness. Web-based Instruction contains the dimensions of Active Learning, Simulation-based Learning, Interactive Learning, and Accumulative Learning; and, Learning Behavior covers Learning Approach, Learning Habit, and Learning Attitude. The…

  15. The learning environment and learning styles: a guide for mentors.

    Science.gov (United States)

    Vinales, James Jude

    The learning environment provides crucial exposure for the pre-registration nursing student. It is during this time that the student nurse develops his or her repertoire of skills, knowledge, attitudes and behaviour in order to meet competencies and gain registration with the Nursing and Midwifery Council. The role of the mentor is vital within the learning environment for aspiring nurses. The learning environment is a fundamental platform for student learning, with mentors key to identifying what is conducive to learning. This article will consider the learning environment and learning styles, and how these two essential elements guide the mentor in making sure they are conducive to learning.

  16. E-Learning 2.0: Learning Redefined

    OpenAIRE

    Kumar, Rupesh

    2009-01-01

    The conventional e-learning approach emphasizes a learning system more than a learning environment. While traditional e-learning systems continue to be significant, there is a new set of services emerging, embracing the philosophy of Web 2.0. Known as e-learning 2.0, it aims to create a personalized learning environment. E-learning 2.0 combines the use of discrete but complementary tools and web services to support the creation of ad-hoc learning communities. This paper discusses the influenc...

  17. Evaluating the Stage Learning Hypothesis.

    Science.gov (United States)

    Thomas, Hoben

    1980-01-01

    A procedure for evaluating the Genevan stage learning hypothesis is illustrated by analyzing Inhelder, Sinclair, and Bovet's guided learning experiments (in "Learning and the Development of Cognition." Cambridge: Harvard University Press, 1974). (Author/MP)

  18. Collaborations in Open Learning Environments

    NARCIS (Netherlands)

    Spoelstra, Howard

    2015-01-01

    This thesis researches automated services for professionals aiming at starting collaborative learning projects in open learning environments, such as MOOCs. It investigates the theoretical backgrounds of team formation for collaborative learning. Based on the outcomes, a model is developed

  19. Minimax bounds for active learning

    NARCIS (Netherlands)

    Castro, R.M.; Nowak, R.

    2008-01-01

    This paper analyzes the potential advantages and theoretical challenges of "active learning" algorithms. Active learning involves sequential sampling procedures that use information gleaned from previous samples in order to focus the sampling and accelerate the learning process relative to "passive

  20. The VREST learning environment.

    Science.gov (United States)

    Kunst, E E; Geelkerken, R H; Sanders, A J B

    2005-01-01

    The VREST learning environment is an integrated architecture to improve the education of health care professionals. It is a combination of a learning, content and assessment management system based on virtual reality. The generic architecture is now being build and tested around the Lichtenstein protocol for hernia inguinalis repair.