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

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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

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

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

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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

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

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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.

Towards autonomous neuroprosthetic control using Hebbian reinforcement learning.

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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.

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

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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 (�

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

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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

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

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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

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

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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.

Anti-Hebbian Spike Timing Dependent Plasticity and Adaptive Sensory Processing

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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.

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

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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

Demystifying social cognition : a Hebbian perspective

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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

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

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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

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

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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.

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

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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

Sleep: The hebbian reinforcement of the local inhibitory synapses.

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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.

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

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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.

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

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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

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

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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.

Optical implementations of associative networks with versatile adaptive learning capabilities.

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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.

Associative Learning in Invertebrates

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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

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

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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

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

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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.

Hebbian Plasticity Guides Maturation of Glutamate Receptor Fields In Vivo

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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.

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

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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.

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

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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

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

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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.

Logic Learning in Hopfield Networks

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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.

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

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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.

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

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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.

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

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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.

The neuroscience of learning: beyond the Hebbian synapse.

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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.

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

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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.

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

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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.

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...

Spike-Based Bayesian-Hebbian Learning of Temporal Sequences.

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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.

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

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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.

Global adaptation in networks of selfish components: emergent associative memory at the system scale.

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Watson, Richard A; Mills, Rob; Buckley, C L

2011-01-01

In some circumstances complex adaptive systems composed of numerous self-interested agents can self-organize into structures that enhance global adaptation, efficiency, or function. However, the general conditions for such an outcome are poorly understood and present a fundamental open question for domains as varied as ecology, sociology, economics, organismic biology, and technological infrastructure design. In contrast, sufficient conditions for artificial neural networks to form structures that perform collective computational processes such as associative memory/recall, classification, generalization, and optimization are well understood. Such global functions within a single agent or organism are not wholly surprising, since the mechanisms (e.g., Hebbian learning) that create these neural organizations may be selected for this purpose; but agents in a multi-agent system have no obvious reason to adhere to such a structuring protocol or produce such global behaviors when acting from individual self-interest. However, Hebbian learning is actually a very simple and fully distributed habituation or positive feedback principle. Here we show that when self-interested agents can modify how they are affected by other agents (e.g., when they can influence which other agents they interact with), then, in adapting these inter-agent relationships to maximize their own utility, they will necessarily alter them in a manner homologous with Hebbian learning. Multi-agent systems with adaptable relationships will thereby exhibit the same system-level behaviors as neural networks under Hebbian learning. For example, improved global efficiency in multi-agent systems can be explained by the inherent ability of associative memory to generalize by idealizing stored patterns and/or creating new combinations of subpatterns. Thus distributed multi-agent systems can spontaneously exhibit adaptive global behaviors in the same sense, and by the same mechanism, as with the organizational

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

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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.

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

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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.

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

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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

Syntactic sequencing in Hebbian cell assemblies.

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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.

Critical dynamics in associative memory networks

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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.

Circuit mechanisms of sensorimotor learning

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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

Learning with incomplete information in the committee machine.

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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.

ERP evidence for conflict in contingency learning.

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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.

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

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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.

Theta coordinated error-driven learning in the hippocampus.

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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.

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.

Learning in AN Oscillatory Cortical Model

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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.

A Reinforcement Learning Framework for Spiking Networks with Dynamic Synapses

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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.

Homeostatic role of heterosynaptic plasticity: Models and experiments

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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.

A Learning Model for L/M Specificity in Ganglion Cells

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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.

Autonomous dynamics in neural networks: the dHAN concept and associative thought processes

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Gros, Claudius

2007-02-01

The neural activity of the human brain is dominated by self-sustained activities. External sensory stimuli influence this autonomous activity but they do not drive the brain directly. Most standard artificial neural network models are however input driven and do not show spontaneous activities. It constitutes a challenge to develop organizational principles for controlled, self-sustained activity in artificial neural networks. Here we propose and examine the dHAN concept for autonomous associative thought processes in dense and homogeneous associative networks. An associative thought-process is characterized, within this approach, by a time-series of transient attractors. Each transient state corresponds to a stored information, a memory. The subsequent transient states are characterized by large associative overlaps, which are identical to acquired patterns. Memory states, the acquired patterns, have such a dual functionality. In this approach the self-sustained neural activity has a central functional role. The network acquires a discrimination capability, as external stimuli need to compete with the autonomous activity. Noise in the input is readily filtered-out. Hebbian learning of external patterns occurs coinstantaneous with the ongoing associative thought process. The autonomous dynamics needs a long-term working-point optimization which acquires within the dHAN concept a dual functionality: It stabilizes the time development of the associative thought process and limits runaway synaptic growth, which generically occurs otherwise in neural networks with self-induced activities and Hebbian-type learning rules.

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.

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

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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.

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.

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...

MOLECULAR MECHANISMS OF FEAR LEARNING AND MEMORY

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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

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.

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

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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.

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

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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.

Anticipation by multi-modal association through an artificial mental imagery process

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Gaona, Wilmer; Escobar, Esaú; Hermosillo, Jorge; Lara, Bruno

2015-01-01

Mental imagery has become a central issue in research laboratories seeking to emulate basic cognitive abilities in artificial agents. In this work, we propose a computational model to produce an anticipatory behaviour by means of a multi-modal off-line hebbian association. Unlike the current state of the art, we propose to apply hebbian learning during an internal sensorimotor simulation, emulating a process of mental imagery. We associate visual and tactile stimuli re-enacted by a long-term predictive simulation chain motivated by covert actions. As a result, we obtain a neural network which provides a robot with a mechanism to produce a visually conditioned obstacle avoidance behaviour. We developed our system in a physical Pioneer 3-DX robot and realised two experiments. In the first experiment we test our model on one individual navigating in two different mazes. In the second experiment we assess the robustness of the model by testing in a single environment five individuals trained under different conditions. We believe that our work offers an underpinning mechanism in cognitive robotics for the study of motor control strategies based on internal simulations. These strategies can be seen analogous to the mental imagery process known in humans, opening thus interesting pathways to the construction of upper-level grounded cognitive abilities.

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

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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

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

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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

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.

Hebb, pandemonium and catastrophic hypermnesia: the hippocampus as a suppressor of inappropriate associations.

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McNaughton, Neil; Wickens, Jeff

2003-01-01

The hippocampus has been proposed as a key component of a "behavioural inhibition system". We explore the implications of this idea for the nature of associative memory--i.e. learning that is distinct from the moulding of response sequences by error correction and reinforcement. It leads to the view that all associative memory depends on purely Hebbian mechanisms. Memories involve acquisition of new goals not the strengthening of new stimulus-response links. Critically, memories will consist of affectively positive and affectively negative associations as well "purely cognitive" information. The hippocampus is seen as a supervisor that is normally "just checking" information about current available goals. When one available goal is pre-eminent there is no hippocampal output and the goal controls the response system. When two or more goals are similarly and highly primed there is conflict. This is detected by the hippocampus which sends output that increases the valence of affectively negative perceptions and so resolves the conflict by suppressing more aversive goals. Such conflict resolution occurs with innate as well as acquired goals and is fundamentally non-memorial. But, in memory paradigms, it can often act to suppress interference on the current trial and, through Hebbian association of the increase in negative affect, decrease the probability of interference on later trials and during consolidation. Both memory-driven and innate behaviour is made hippocampal-dependent by innate and acquired conflicting tendencies and not the class of stimulus presented.

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.

Competitive STDP Learning of Overlapping Spatial Patterns.

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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.

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.

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.

Thermodynamic efficiency of learning a rule in neural networks

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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.

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.

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.

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

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.

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

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.

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.

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.

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.

Learning: from association to cognition.

Science.gov (United States)

Shanks, David R

2010-01-01

Since the very earliest experimental investigations of learning, tension has existed between association-based and cognitive theories. Associationism accounts for the phenomena of both conditioning and "higher" forms of learning via concepts such as excitation, inhibition, and reinforcement, whereas cognitive theories assume that learning depends on hypothesis testing, cognitive models, and propositional reasoning. Cognitive theories have received considerable impetus in regard to both human and animal learning from recent research suggesting that the key illustration of cue selection in learning, blocking, often arises from inferential reasoning. At the same time, a dichotomous view that separates noncognitive, unconscious (implicit) learning from cognitive, conscious (explicit) learning has gained favor. This review selectively describes key findings from this research, evaluates evidence for and against associative and cognitive explanatory constructs, and critically examines both the dichotomous view of learning as well as the claim that learning can occur unconsciously.

An instance theory of associative learning.

Science.gov (United States)

Jamieson, Randall K; Crump, Matthew J C; Hannah, Samuel D

2012-03-01

We present and test an instance model of associative learning. The model, Minerva-AL, treats associative learning as cued recall. Memory preserves the events of individual trials in separate traces. A probe presented to memory contacts all traces in parallel and retrieves a weighted sum of the traces, a structure called the echo. Learning of a cue-outcome relationship is measured by the cue's ability to retrieve a target outcome. The theory predicts a number of associative learning phenomena, including acquisition, extinction, reacquisition, conditioned inhibition, external inhibition, latent inhibition, discrimination, generalization, blocking, overshadowing, overexpectation, superconditioning, recovery from blocking, recovery from overshadowing, recovery from overexpectation, backward blocking, backward conditioned inhibition, and second-order retrospective revaluation. We argue that associative learning is consistent with an instance-based approach to learning and memory.

Associative learning and animal cognition.

Science.gov (United States)

Dickinson, Anthony

2012-10-05

Associative learning plays a variety of roles in the study of animal cognition from a core theoretical component to a null hypothesis against which the contribution of cognitive processes is assessed. Two developments in contemporary associative learning have enhanced its relevance to animal cognition. The first concerns the role of associatively activated representations, whereas the second is the development of hybrid theories in which learning is determined by prediction errors, both directly and indirectly through associability processes. However, it remains unclear whether these developments allow associative theory to capture the psychological rationality of cognition. I argue that embodying associative processes within specific processing architectures provides mechanisms that can mediate psychological rationality and illustrate such embodiment by discussing the relationship between practical reasoning and the associative-cybernetic model of goal-directed action.

ERP correlates of associative learning.

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Rose, M; Verleger, R; Wascher, E

2001-05-01

We examined changes of event-related potentials (ERPs) while participants learned stimulus-to-stimulus relations in an S1-S2 task. The design allowed for separating processes of associative learning from nonspecific effects. Participants had to respond to S2 by a left or right key-press dependent on S2 identity (letter W or M). Preparation for S2 could be improved by using the associative information given by S1. The S1 was an arrow pointing to the left or right. In combination with its color, arrow direction was informative about location and identity of S2, but participants were not informed about the relevance of color. Arrows in two of the colors were fully predictive for the S2 whereas the third color gave no valid information. This third stimulus controlled for habituation and procedural learning. Six blocks with 200 trials each and all three S1 colors in random order were presented. Behavioral and ERP differences in each block between "learning" and control trials were used to identify processes of associative learning. Several effects of associative learning were identified indicating the involvement of specific stages of information processing: a continuous increase of P3 amplitude evoked by S1 was accompanied by a decrease of P3 evoked by S2. These changes reflected the modifications of stimulus weights for response selection and the strengthened association between the two stimulus complexes in the time course of learning. The related motor preparation benefited from learning too, expressed in a decrease of CNV amplitude and an increase of LRP amplitude. Finally a decrease of N1 amplitude evoked by S2 indicated the reduced need to allocate spatial attention to the S2 location according to the learned meaning of S1.

Accounting for individual differences in human associative learning.

Science.gov (United States)

Byrom, Nicola C

2013-09-04

Associative learning has provided fundamental insights to understanding psychopathology. However, psychopathology occurs along a continuum and as such, identification of disruptions in processes of associative learning associated with aspects of psychopathology illustrates a general flexibility in human associative learning. A handful of studies have looked specifically at individual differences in human associative learning, but while much work has concentrated on accounting for flexibility in learning caused by external factors, there has been limited work considering how to model the influence of dispositional factors. This review looks at the range of individual differences in human associative learning that have been explored and the attempts to account for, and model, this flexibility. To fully understand human associative learning, further research needs to attend to the causes of variation in human learning.

Accounting for Individual Differences in Human Associative Learning.

Directory of Open Access Journals (Sweden)

Nicola C Byrom

2013-09-01

Full Text Available Associative learning has provided fundamental insights to understanding psychopathology. However, psychopathology occurs along a continuum and as such, identification of disruptions in processes of associative learning associated with aspects of psychopathology illustrates a general flexibility in human associative learning. A handful of studies have looked specifically at individual differences in human associative learning, but while much work has concentrated on accounting for flexibility in learning caused by external factors, there has been limited work considering how to model the influence of dispositional factors. This review looks at the range of individual differences in human associative learning that have been explored and the attempts to account for, and model, this flexibility. To fully understand human associative learning, further research needs to attend to the causes of variation in human learning.

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

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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.

Associative Learning Through Acquired Salience.

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Treviño, Mario

2015-01-01

Most associative learning studies describe the salience of stimuli as a fixed learning-rate parameter. Presumptive saliency signals, however, have also been linked to motivational and attentional processes. An interesting possibility, therefore, is that discriminative stimuli could also acquire salience as they become powerful predictors of outcomes. To explore this idea, we first characterized and extracted the learning curves from mice trained with discriminative images offering varying degrees of structural similarity. Next, we fitted a linear model of associative learning coupled to a series of mathematical representations for stimulus salience. We found that the best prediction, from the set of tested models, was one in which the visual salience depended on stimulus similarity and a non-linear function of the associative strength. Therefore, these analytic results support the idea that the net salience of a stimulus depends both on the items' effective salience and the motivational state of the subject that learns about it. Moreover, this dual salience model can explain why learning about a stimulus not only depends on the effective salience during acquisition but also on the specific learning trajectory that was used to reach this state. Our mathematical description could be instrumental for understanding aberrant salience acquisition under stressful situations and in neuropsychiatric disorders like schizophrenia, obsessive-compulsive disorder, and addiction.

Recognition of abstract objects via neural oscillators: interaction among topological organization, associative memory and gamma band synchronization.

Science.gov (United States)

Ursino, Mauro; Magosso, Elisa; Cuppini, Cristiano

2009-02-01

Synchronization of neural activity in the gamma band is assumed to play a significant role not only in perceptual processing, but also in higher cognitive functions. Here, we propose a neural network of Wilson-Cowan oscillators to simulate recognition of abstract objects, each represented as a collection of four features. Features are ordered in topological maps of oscillators connected via excitatory lateral synapses, to implement a similarity principle. Experience on previous objects is stored in long-range synapses connecting the different topological maps, and trained via timing dependent Hebbian learning (previous knowledge principle). Finally, a downstream decision network detects the presence of a reliable object representation, when all features are oscillating in synchrony. Simulations performed giving various simultaneous objects to the network (from 1 to 4), with some missing and/or modified properties suggest that the network can reconstruct objects, and segment them from the other simultaneously present objects, even in case of deteriorated information, noise, and moderate correlation among the inputs (one common feature). The balance between sensitivity and specificity depends on the strength of the Hebbian learning. Achieving a correct reconstruction in all cases, however, requires ad hoc selection of the oscillation frequency. The model represents an attempt to investigate the interactions among topological maps, autoassociative memory, and gamma-band synchronization, for recognition of abstract objects.

Integrating the behavioral and neural dynamics of response selection in a dual-task paradigm: a dynamic neural field model of Dux et al. (2009).

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Buss, Aaron T; Wifall, Tim; Hazeltine, Eliot; Spencer, John P

2014-02-01

People are typically slower when executing two tasks than when only performing a single task. These dual-task costs are initially robust but are reduced with practice. Dux et al. (2009) explored the neural basis of dual-task costs and learning using fMRI. Inferior frontal junction (IFJ) showed a larger hemodynamic response on dual-task trials compared with single-task trial early in learning. As dual-task costs were eliminated, dual-task hemodynamics in IFJ reduced to single-task levels. Dux and colleagues concluded that the reduction of dual-task costs is accomplished through increased efficiency of information processing in IFJ. We present a dynamic field theory of response selection that addresses two questions regarding these results. First, what mechanism leads to the reduction of dual-task costs and associated changes in hemodynamics? We show that a simple Hebbian learning mechanism is able to capture the quantitative details of learning at both the behavioral and neural levels. Second, is efficiency isolated to cognitive control areas such as IFJ, or is it also evident in sensory motor areas? To investigate this, we restrict Hebbian learning to different parts of the neural model. None of the restricted learning models showed the same reductions in dual-task costs as the unrestricted learning model, suggesting that efficiency is distributed across cognitive control and sensory motor processing systems.

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

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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

Accounting for individual differences in human associative learning

OpenAIRE

Byrom, Nicola C.

2013-01-01

Associative learning has provided fundamental insights to understanding psychopathology. However, psychopathology occurs along a continuum and as such, identification of disruptions in processes of associative learning associated with aspects of psychopathology illustrates a general flexibility in human associative learning. A handful of studies have looked specifically at individual differences in human associative learning, but while much work has concentrated on accounting for flexibility ...

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

Prefrontal Dopamine in Associative Learning and Memory

Science.gov (United States)

Puig, M. Victoria; Antzoulatos, Evan G.; Miller, Earl K.

2014-01-01

Learning to associate specific objects or actions with rewards and remembering the associations are everyday tasks crucial for our flexible adaptation to the environment. These higher-order cognitive processes depend on the prefrontal cortex (PFC) and frontostriatal circuits that connect areas in the frontal lobe with the striatum in the basal ganglia. Both structures are densely innervated by dopamine (DA) afferents that originate in the midbrain. Although the activity of DA neurons is thought to be important for learning, the exact role of DA transmission in frontostriatal circuits during learning-related tasks is still unresolved. Moreover, the neural substrates of this modulation are poorly understood. Here, we review our recent work in monkeys utilizing local pharmacology of DA agents in the PFC to investigate the cellular mechanisms of DA modulation of associative learning and memory. We show that blocking both D1 and D2 receptors in the lateral PFC impairs learning of new stimulus-response associations and cognitive flexibility, but not the memory of highly familiar associations. In addition, D2 receptors may also contribute to motivation. The learning deficits correlated with reductions of neural information about the associations in PFC neurons, alterations in global excitability and spike synchronization, and exaggerated alpha and beta neural oscillations. Our findings provide new insights into how DA transmission modulate associative learning and memory processes in frontostriatal systems. PMID:25241063

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...

Neuroimaging of Fear-Associated Learning

Science.gov (United States)

Greco, John A; Liberzon, Israel

2016-01-01

Fear conditioning has been commonly used as a model of emotional learning in animals and, with the introduction of functional neuroimaging techniques, has proven useful in establishing the neurocircuitry of emotional learning in humans. Studies of fear acquisition suggest that regions such as amygdala, insula, anterior cingulate cortex, and hippocampus play an important role in acquisition of fear, whereas studies of fear extinction suggest that the amygdala is also crucial for safety learning. Extinction retention testing points to the ventromedial prefrontal cortex as an essential region in the recall of the safety trace, and explicit learning of fear and safety associations recruits additional cortical and subcortical regions. Importantly, many of these findings have implications in our understanding of the pathophysiology of psychiatric disease. Recent studies using clinical populations have lent insight into the changes in regional activity in specific disorders, and treatment studies have shown how pharmaceutical and other therapeutic interventions modulate brain activation during emotional learning. Finally, research investigating individual differences in neurotransmitter receptor genotypes has highlighted the contribution of these systems in fear-associated learning. PMID:26294108

Prefrontal dopamine in associative learning and memory.

Science.gov (United States)

Puig, M V; Antzoulatos, E G; Miller, E K

2014-12-12

Learning to associate specific objects or actions with rewards and remembering the associations are everyday tasks crucial for our flexible adaptation to the environment. These higher-order cognitive processes depend on the prefrontal cortex (PFC) and frontostriatal circuits that connect areas in the frontal lobe with the striatum in the basal ganglia. Both structures are densely innervated by dopamine (DA) afferents that originate in the midbrain. Although the activity of DA neurons is thought to be important for learning, the exact role of DA transmission in frontostriatal circuits during learning-related tasks is still unresolved. Moreover, the neural substrates of this modulation are poorly understood. Here, we review our recent work in monkeys utilizing local pharmacology of DA agents in the PFC to investigate the cellular mechanisms of DA modulation of associative learning and memory. We show that blocking both D1 and D2 receptors in the lateral PFC impairs learning of new stimulus-response associations and cognitive flexibility, but not the memory of highly familiar associations. In addition, D2 receptors may also contribute to motivation. The learning deficits correlated with reductions of neural information about the associations in PFC neurons, alterations in global excitability and spike synchronization, and exaggerated alpha and beta neural oscillations. Our findings provide new insights into how DA transmission modulates associative learning and memory processes in frontostriatal systems. Copyright © 2014 IBRO. Published by Elsevier Ltd. All rights reserved.

Bayesian Inference and Online Learning in Poisson Neuronal Networks.

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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.

Associative learning for a robot intelligence

CERN Document Server

Andreae, John H

1998-01-01

The explanation of brain functioning in terms of the association of ideas has been popular since the 17th century. Recently, however, the process of association has been dismissed as computationally inadequate by prominent cognitive scientists. In this book, a sharper definition of the term "association" is used to revive the process by showing that associative learning can indeed be computationally powerful. Within an appropriate organization, associative learning can be embodied in a robot to realize a human-like intelligence, which sets its own goals, exhibits unique unformalizable behaviou

Comparison of Seven Methods for Boolean Factor Analysis and Their Evaluation by Information Gain

Czech Academy of Sciences Publication Activity Database

Frolov, A.; Húsek, Dušan; Polyakov, P.Y.

2016-01-01

Roč. 27, č. 3 (2016), s. 538-550 ISSN 2162-237X R&D Projects: GA MŠk ED1.1.00/02.0070 Institutional support: RVO:67985807 Keywords : associative memory * bars problem (BP) * Boolean factor analysis (BFA) * data mining * dimension reduction * Hebbian learning rule * information gain * likelihood maximization (LM) * neural network application * recurrent neural network * statistics Subject RIV: IN - Informatics, Computer Science Impact factor: 6.108, year: 2016

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.

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

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.

Time and Associative Learning.

Science.gov (United States)

Balsam, Peter D; Drew, Michael R; Gallistel, C R

2010-01-01

In a basic associative learning paradigm, learning is said to have occurred when the conditioned stimulus evokes an anticipatory response. This learning is widely believed to depend on the contiguous presentation of conditioned and unconditioned stimulus. However, what it means to be contiguous has not been rigorously defined. Here we examine the empirical bases for these beliefs and suggest an alternative view based on the hypothesis that learning about the temporal relationships between events determines the speed of emergence, vigor and form of conditioned behavior. This temporal learning occurs very rapidly and prior to the appearance of the anticipatory response. The temporal relations are learned even when no anticipatory response is evoked. The speed with which an anticipatory response emerges is proportional to the informativeness of the predictive cue (CS) regarding the rate of occurrence of the predicted event (US). This analysis gives an account of what we mean by "temporal pairing" and is in accord with the data on speed of acquisition and basic findings in the cue competition literature. In this account, learning depends on perceiving and encoding temporal regularities rather than stimulus contiguities.

Visual attention to features by associative learning.

Science.gov (United States)

Gozli, Davood G; Moskowitz, Joshua B; Pratt, Jay

2014-11-01

Expecting a particular stimulus can facilitate processing of that stimulus over others, but what is the fate of other stimuli that are known to co-occur with the expected stimulus? This study examined the impact of learned association on feature-based attention. The findings show that the effectiveness of an uninformative color transient in orienting attention can change by learned associations between colors and the expected target shape. In an initial acquisition phase, participants learned two distinct sequences of stimulus-response-outcome, where stimuli were defined by shape ('S' vs. 'H'), responses were localized key-presses (left vs. right), and outcomes were colors (red vs. green). Next, in a test phase, while expecting a target shape (80% probable), participants showed reliable attentional orienting to the color transient associated with the target shape, and showed no attentional orienting with the color associated with the alternative target shape. This bias seemed to be driven by learned association between shapes and colors, and not modulated by the response. In addition, the bias seemed to depend on observing target-color conjunctions, since encountering the two features disjunctively (without spatiotemporal overlap) did not replicate the findings. We conclude that associative learning - likely mediated by mechanisms underlying visual object representation - can extend the impact of goal-driven attention to features associated with a target stimulus. Copyright © 2014 Elsevier B.V. All rights reserved.

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.

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.

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

Audiovisual Association Learning in the Absence of Primary Visual Cortex.

Science.gov (United States)

Seirafi, Mehrdad; De Weerd, Peter; Pegna, Alan J; de Gelder, Beatrice

2015-01-01

Learning audiovisual associations is mediated by the primary cortical areas; however, recent animal studies suggest that such learning can take place even in the absence of the primary visual cortex. Other studies have demonstrated the involvement of extra-geniculate pathways and especially the superior colliculus (SC) in audiovisual association learning. Here, we investigated such learning in a rare human patient with complete loss of the bilateral striate cortex. We carried out an implicit audiovisual association learning task with two different colors of red and purple (the latter color known to minimally activate the extra-genicular pathway). Interestingly, the patient learned the association between an auditory cue and a visual stimulus only when the unseen visual stimulus was red, but not when it was purple. The current study presents the first evidence showing the possibility of audiovisual association learning in humans with lesioned striate cortex. Furthermore, in line with animal studies, it supports an important role for the SC in audiovisual associative learning.

A model of olfactory associative learning

Science.gov (United States)

Tavoni, Gaia; Balasubramanian, Vijay

We propose a mechanism, rooted in the known anatomy and physiology of the vertebrate olfactory system, by which presentations of rewarded and unrewarded odors lead to formation of odor-valence associations between piriform cortex (PC) and anterior olfactory nucleus (AON) which, in concert with neuromodulators release in the bulb, entrains a direct feedback from the AON representation of valence to a group of mitral cells (MCs). The model makes several predictions concerning MC activity during and after associative learning: (a) AON feedback produces synchronous divergent responses in a localized subset of MCs; (b) such divergence propagates to other MCs by lateral inhibition; (c) after learning, MC responses reconverge; (d) recall of the newly formed associations in the PC increases feedback inhibition in the MCs. These predictions have been confirmed in disparate experiments which we now explain in a unified framework. For cortex, our model further predicts that the response divergence developed during learning reshapes odor representations in the PC, with the effects of (a) decorrelating PC representations of odors with different valences, (b) increasing the size and reliability of those representations, and enabling recall correction and redundancy reduction after learning. Simons Foundation for Mathematical Modeling of Living Systems.

Working memory and reward association learning impairments in obesity.

Science.gov (United States)

Coppin, Géraldine; Nolan-Poupart, Sarah; Jones-Gotman, Marilyn; Small, Dana M

2014-12-01

Obesity has been associated with impaired executive functions including working memory. Less explored is the influence of obesity on learning and memory. In the current study we assessed stimulus reward association learning, explicit learning and memory and working memory in healthy weight, overweight and obese individuals. Explicit learning and memory did not differ as a function of group. In contrast, working memory was significantly and similarly impaired in both overweight and obese individuals compared to the healthy weight group. In the first reward association learning task the obese, but not healthy weight or overweight participants consistently formed paradoxical preferences for a pattern associated with a negative outcome (fewer food rewards). To determine if the deficit was specific to food reward a second experiment was conducted using money. Consistent with Experiment 1, obese individuals selected the pattern associated with a negative outcome (fewer monetary rewards) more frequently than healthy weight individuals and thus failed to develop a significant preference for the most rewarded patterns as was observed in the healthy weight group. Finally, on a probabilistic learning task, obese compared to healthy weight individuals showed deficits in negative, but not positive outcome learning. Taken together, our results demonstrate deficits in working memory and stimulus reward learning in obesity and suggest that obese individuals are impaired in learning to avoid negative outcomes. Copyright © 2014 Elsevier Ltd. All rights reserved.

New BFA Method Based on Attractor Neural Network and Likelihood Maximization

Czech Academy of Sciences Publication Activity Database

Frolov, A. A.; Húsek, Dušan; Polyakov, P.Y.; Snášel, V.

2014-01-01

Roč. 132, 20 May (2014), s. 14-29 ISSN 0925-2312 Grant - others:GA MŠk(CZ) ED1.1.00/02.0070; GA MŠk(CZ) EE.2.3.20.0073 Program:ED Institutional support: RVO:67985807 Keywords : recurrent neural network * associative memory * Hebbian learning rule * neural network application * data mining * statistics * Boolean factor analysis * information gain * dimension reduction * likelihood-maximization * bars problem Subject RIV: IN - Informatics, Computer Science Impact factor: 2.083, year: 2014

[Factors associated with self-directed learning among medical students].

Science.gov (United States)

Spormann R, Camila; Pérez V, Cristhian; Fasce H, Eduardo; Ortega B, Javiera; Bastías V, Nancy; Bustamante D, Carolina; Ibáñez G, Pilar

2015-03-01

Self-directed learning is a skill that must be taught and evaluated in future physicians. To analyze the association between self-directed learning, self-esteem, self-efficacy, time management and academic commitment among medical students. The self-directed learning, Rosemberg self-esteem, general self- efficacy, time management and Utrecht work engagement scales were applied to 297 first year medical students. A multiple regression analysis showed a significant association between self-efficacy, time management and academic commitment with self-directed learning. Self-esteem and satisfaction with studies did not enter in the model. self-esteem, academic commitment and a good time management were associated with self-directed learning in these students.

Impaired associative learning with food rewards in obese women.

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Zhang, Zhihao; Manson, Kirk F; Schiller, Daniela; Levy, Ifat

2014-08-04

Obesity is a major epidemic in many parts of the world. One of the main factors contributing to obesity is overconsumption of high-fat and high-calorie food, which is driven by the rewarding properties of these types of food. Previous studies have suggested that dysfunction in reward circuits may be associated with overeating and obesity. The nature of this dysfunction, however, is still unknown. Here, we demonstrate impairment in reward-based associative learning specific to food in obese women. Normal-weight and obese participants performed an appetitive reversal learning task in which they had to learn and modify cue-reward associations. To test whether any learning deficits were specific to food reward or were more general, we used a between-subject design in which half of the participants received food reward and the other half received money reward. Our results reveal a marked difference in associative learning between normal-weight and obese women when food was used as reward. Importantly, no learning deficits were observed with money reward. Multiple regression analyses also established a robust negative association between body mass index and learning performance in the food domain in female participants. Interestingly, such impairment was not observed in obese men. These findings suggest that obesity may be linked to impaired reward-based associative learning and that this impairment may be specific to the food domain. Copyright © 2014 Elsevier Ltd. All rights reserved.

Associative learning and the control of human dietary behavior.

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Brunstrom, Jeffrey M

2007-07-01

Most of our food likes and disliked are learned. Relevant forms of associative learning have been identified in animals. However, observations of the same associative processes are relatively scarce in humans. The first section of this paper outlines reasons why this might be the case. Emphasis is placed on recent research exploring individual differences and the importance or otherwise of hunger and contingency awareness. The second section briefly considers the effect of learning on meal size, and the author revisits the question of how learned associations might come to influence energy intake in humans.

The endocannabinoid system and associative learning and memory in zebrafish.

Science.gov (United States)

Ruhl, Tim; Moesbauer, Kirstin; Oellers, Nadine; von der Emde, Gerhard

2015-09-01

In zebrafish the medial pallium of the dorsal telencephalon represents an amygdala homolog structure, which is crucially involved in emotional associative learning and memory. Similar to the mammalian amygdala, the medial pallium contains a high density of endocannabinoid receptor CB1. To elucidate the role of the zebrafish endocannabinoid system in associative learning, we tested the influence of acute and chronic administration of receptor agonists (THC, WIN55,212-2) and antagonists (Rimonabant, AM-281) on two different learning paradigms. In an appetitively motivated two-alternative choice paradigm, animals learned to associate a certain color with a food reward. In a second set-up, a fish shuttle-box, animals associated the onset of a light stimulus with the occurrence of a subsequent electric shock (avoidance conditioning). Once fish successfully had learned to solve these behavioral tasks, acute receptor activation or inactivation had no effect on memory retrieval, suggesting that established associative memories were stable and not alterable by the endocannabinoid system. In both learning tasks, chronic treatment with receptor antagonists improved acquisition learning, and additionally facilitated reversal learning during color discrimination. In contrast, chronic CB1 activation prevented aversively motivated acquisition learning, while different effects were found on appetitively motivated acquisition learning. While THC significantly improved behavioral performance, WIN55,212-2 significantly impaired color association. Our findings suggest that the zebrafish endocannabinoid system can modulate associative learning and memory. Stimulation of the CB1 receptor might play a more specific role in acquisition and storage of aversive learning and memory, while CB1 blocking induces general enhancement of cognitive functions. Copyright © 2015 Elsevier B.V. All rights reserved.

No trade-off between learning speed and associative flexibility in bumblebees: a reversal learning test with multiple colonies.

Directory of Open Access Journals (Sweden)

Nigel E Raine

Full Text Available Potential trade-offs between learning speed and memory-related performance could be important factors in the evolution of learning. Here, we test whether rapid learning interferes with the acquisition of new information using a reversal learning paradigm. Bumblebees (Bombus terrestris were trained to associate yellow with a floral reward. Subsequently the association between colour and reward was reversed, meaning bees then had to learn to visit blue flowers. We demonstrate that individuals that were fast to learn yellow as a predictor of reward were also quick to reverse this association. Furthermore, overnight memory retention tests suggest that faster learning individuals are also better at retaining previously learned information. There is also an effect of relatedness: colonies whose workers were fast to learn the association between yellow and reward also reversed this association rapidly. These results are inconsistent with a trade-off between learning speed and the reversal of a previously made association. On the contrary, they suggest that differences in learning performance and cognitive (behavioural flexibility could reflect more general differences in colony learning ability. Hence, this study provides additional evidence to support the idea that rapid learning and behavioural flexibility have adaptive value.

Learning Recruits Neurons Representing Previously Established Associations in the Corvid Endbrain.

Science.gov (United States)

Veit, Lena; Pidpruzhnykova, Galyna; Nieder, Andreas

2017-10-01

Crows quickly learn arbitrary associations. As a neuronal correlate of this behavior, single neurons in the corvid endbrain area nidopallium caudolaterale (NCL) change their response properties during association learning. In crows performing a delayed association task that required them to map both familiar and novel sample pictures to the same two choice pictures, NCL neurons established a common, prospective code for associations. Here, we report that neuronal tuning changes during learning were not distributed equally in the recorded population of NCL neurons. Instead, such learning-related changes relied almost exclusively on neurons which were already encoding familiar associations. Only in such neurons did behavioral improvements during learning of novel associations coincide with increasing selectivity over the learning process. The size and direction of selectivity for familiar and newly learned associations were highly correlated. These increases in selectivity for novel associations occurred only late in the delay period. Moreover, NCL neurons discriminated correct from erroneous trial outcome based on feedback signals at the end of the trial, particularly in newly learned associations. Our results indicate that task-relevant changes during association learning are not distributed within the population of corvid NCL neurons but rather are restricted to a specific group of association-selective neurons. Such association neurons in the multimodal cognitive integration area NCL likely play an important role during highly flexible behavior in corvids.

An associative account of the development of word learning.

Science.gov (United States)

Sloutsky, Vladimir M; Yim, Hyungwook; Yao, Xin; Dennis, Simon

2017-09-01

Word learning is a notoriously difficult induction problem because meaning is underdetermined by positive examples. How do children solve this problem? Some have argued that word learning is achieved by means of inference: young word learners rely on a number of assumptions that reduce the overall hypothesis space by favoring some meanings over others. However, these approaches have difficulty explaining how words are learned from conversations or text, without pointing or explicit instruction. In this research, we propose an associative mechanism that can account for such learning. In a series of experiments, 4-year-olds and adults were presented with sets of words that included a single nonsense word (e.g. dax). Some lists were taxonomic (i.,e., all items were members of a given category), some were associative (i.e., all items were associates of a given category, but not members), and some were mixed. Participants were asked to indicate whether the nonsense word was an animal or an artifact. Adults exhibited evidence of learning when lists consisted of either associatively or taxonomically related items. In contrast, children exhibited evidence of word learning only when lists consisted of associatively related items. These results present challenges to several extant models of word learning, and a new model based on the distinction between syntagmatic and paradigmatic associations is proposed. Copyright © 2017 Elsevier Inc. All rights reserved.

Continuous Online Sequence Learning with an Unsupervised Neural Network Model.

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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.

Association of Amine-Receptor DNA Sequence Variants with Associative Learning in the Honeybee.

Science.gov (United States)

Lagisz, Malgorzata; Mercer, Alison R; de Mouzon, Charlotte; Santos, Luana L S; Nakagawa, Shinichi

2016-03-01

Octopamine- and dopamine-based neuromodulatory systems play a critical role in learning and learning-related behaviour in insects. To further our understanding of these systems and resulting phenotypes, we quantified DNA sequence variations at six loci coding octopamine-and dopamine-receptors and their association with aversive and appetitive learning traits in a population of honeybees. We identified 79 polymorphic sequence markers (mostly SNPs and a few insertions/deletions) located within or close to six candidate genes. Intriguingly, we found that levels of sequence variation in the protein-coding regions studied were low, indicating that sequence variation in the coding regions of receptor genes critical to learning and memory is strongly selected against. Non-coding and upstream regions of the same genes, however, were less conserved and sequence variations in these regions were weakly associated with between-individual differences in learning-related traits. While these associations do not directly imply a specific molecular mechanism, they suggest that the cross-talk between dopamine and octopamine signalling pathways may influence olfactory learning and memory in the honeybee.

Phenotypic transformation affects associative learning in the desert locust.

Science.gov (United States)

Simões, Patrício M V; Niven, Jeremy E; Ott, Swidbert R

2013-12-02

In desert locusts, increased population densities drive phenotypic transformation from the solitarious to the gregarious phase within a generation [1-4]. Here we show that when presented with odor-food associations, the two extreme phases differ in aversive but not appetitive associative learning, with solitarious locusts showing a conditioned aversion more quickly than gregarious locusts. The acquisition of new learned aversions was blocked entirely in acutely crowded solitarious (transiens) locusts, whereas appetitive learning and prior learned associations were unaffected. These differences in aversive learning support phase-specific feeding strategies. Associative training with hyoscyamine, a plant alkaloid found in the locusts' habitat [5, 6], elicits a phase-dependent odor preference: solitarious locusts avoid an odor associated with hyoscyamine, whereas gregarious locusts do not. Remarkably, when solitarious locusts are crowded and then reconditioned with the odor-hyoscyamine pairing as transiens, the specific blockade of aversive acquisition enables them to override their prior aversive memory with an appetitive one. Under fierce food competition, as occurs during crowding in the field, this provides a neuroecological mechanism enabling locusts to reassign an appetitive value to an odor that they learned previously to avoid. Copyright © 2013 Elsevier Ltd. All rights reserved.

The Development of Associate Learning in School Age Children

Science.gov (United States)

Harel, Brian T.; Pietrzak, Robert H.; Snyder, Peter J.; Thomas, Elizabeth; Mayes, Linda C.; Maruff, Paul

2014-01-01

Associate learning is fundamental to the acquisition of knowledge and plays a critical role in the everyday functioning of the developing child, though the developmental course is still unclear. This study investigated the development of visual associate learning in 125 school age children using the Continuous Paired Associate Learning task. As hypothesized, younger children made more errors than older children across all memory loads and evidenced decreased learning efficiency as memory load increased. Results suggest that age-related differences in performance largely reflect continued development of executive function in the context of relatively developed memory processes. PMID:25014755

The development of associate learning in school age children.

Directory of Open Access Journals (Sweden)

Brian T Harel

Full Text Available Associate learning is fundamental to the acquisition of knowledge and plays a critical role in the everyday functioning of the developing child, though the developmental course is still unclear. This study investigated the development of visual associate learning in 125 school age children using the Continuous Paired Associate Learning task. As hypothesized, younger children made more errors than older children across all memory loads and evidenced decreased learning efficiency as memory load increased. Results suggest that age-related differences in performance largely reflect continued development of executive function in the context of relatively developed memory processes.

Acquisition of automatic imitation is sensitive to sensorimotor contingency.

Science.gov (United States)

Cook, Richard; Press, Clare; Dickinson, Anthony; Heyes, Cecilia

2010-08-01

The associative sequence learning model proposes that the development of the mirror system depends on the same mechanisms of associative learning that mediate Pavlovian and instrumental conditioning. To test this model, two experiments used the reduction of automatic imitation through incompatible sensorimotor training to assess whether mirror system plasticity is sensitive to contingency (i.e., the extent to which activation of one representation predicts activation of another). In Experiment 1, residual automatic imitation was measured following incompatible training in which the action stimulus was a perfect predictor of the response (contingent) or not at all predictive of the response (noncontingent). A contingency effect was observed: There was less automatic imitation indicative of more learning in the contingent group. Experiment 2 replicated this contingency effect and showed that, as predicted by associative learning theory, it can be abolished by signaling trials in which the response occurs in the absence of an action stimulus. These findings support the view that mirror system development depends on associative learning and indicate that this learning is not purely Hebbian. If this is correct, associative learning theory could be used to explain, predict, and intervene in mirror system development.

Awake, Offline Processing during Associative Learning.

Science.gov (United States)

Bursley, James K; Nestor, Adrian; Tarr, Michael J; Creswell, J David

2016-01-01

Offline processing has been shown to strengthen memory traces and enhance learning in the absence of conscious rehearsal or awareness. Here we evaluate whether a brief, two-minute offline processing period can boost associative learning and test a memory reactivation account for these offline processing effects. After encoding paired associates, subjects either completed a distractor task for two minutes or were immediately tested for memory of the pairs in a counterbalanced, within-subjects functional magnetic resonance imaging study. Results showed that brief, awake, offline processing improves memory for associate pairs. Moreover, multi-voxel pattern analysis of the neuroimaging data suggested reactivation of encoded memory representations in dorsolateral prefrontal cortex during offline processing. These results signify the first demonstration of awake, active, offline enhancement of associative memory and suggest that such enhancement is accompanied by the offline reactivation of encoded memory representations.

Active Learning Not Associated with Student Learning in a Random Sample of College Biology Courses

Science.gov (United States)

Andrews, T. M.; Leonard, M. J.; Colgrove, C. A.; Kalinowski, S. T.

2011-01-01

Previous research has suggested that adding active learning to traditional college science lectures substantially improves student learning. However, this research predominantly studied courses taught by science education researchers, who are likely to have exceptional teaching expertise. The present study investigated introductory biology courses randomly selected from a list of prominent colleges and universities to include instructors representing a broader population. We examined the relationship between active learning and student learning in the subject area of natural selection. We found no association between student learning gains and the use of active-learning instruction. Although active learning has the potential to substantially improve student learning, this research suggests that active learning, as used by typical college biology instructors, is not associated with greater learning gains. We contend that most instructors lack the rich and nuanced understanding of teaching and learning that science education researchers have developed. Therefore, active learning as designed and implemented by typical college biology instructors may superficially resemble active learning used by education researchers, but lacks the constructivist elements necessary for improving learning. PMID:22135373

Temporal maps and informativeness in associative learning.

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Balsam, Peter D; Gallistel, C Randy

2009-02-01

Neurobiological research on learning assumes that temporal contiguity is essential for association formation, but what constitutes temporal contiguity has never been specified. We review evidence that learning depends, instead, on learning a temporal map. Temporal relations between events are encoded even from single experiences. The speed with which an anticipatory response emerges is proportional to the informativeness of the encoded relation between a predictive stimulus or event and the event it predicts. This principle yields a quantitative account of the heretofore undefined, but theoretically crucial, concept of temporal pairing, an account in quantitative accord with surprising experimental findings. The same principle explains the basic results in the cue competition literature, which motivated the Rescorla-Wagner model and most other contemporary models of associative learning. The essential feature of a memory mechanism in this account is its ability to encode quantitative information.

Erratum to: The blocking effect in associative learning involves learned biases in rapid attentional capture.

Science.gov (United States)

2018-04-01

Luque, D., Vadillo, M, A., Gutiérrez-Cobo, M, J., Le Pelley, M, E. (2018). The blocking effect in associative learning involves learned biases in rapid attentional capture. Quarterly Journal of Experimental Psychology, 71, 522-544. doi: 10.1080/17470218.2016.1262435. The above article is part of the Special Issue on Associative Learning (in honour of Nick Mackintosh) and was inadvertently published in the February 2018 issue of Quarterly Journal of Experimental Psychology. After publication of the Special Issue, an online collection on Associative Learning will be created on SAGE Journals and this paper will be included in that collection. The Publisher apologises for this error.

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

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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.

[Associative Learning between Orientation and Color in Early Visual Areas].

Science.gov (United States)

Amano, Kaoru; Shibata, Kazuhisa; Kawato, Mitsuo; Sasaki, Yuka; Watanabe, Takeo

2017-08-01

Associative learning is an essential neural phenomenon where the contingency of different items increases after training. Although associative learning has been found to occur in many brain regions, there is no clear evidence that associative learning of visual features occurs in early visual areas. Here, we developed an associative decoded functional magnetic resonance imaging (fMRI) neurofeedback (A-DecNef) to determine whether associative learning of color and orientation can be induced in early visual areas. During the three days' training, A-DecNef induced fMRI signal patterns that corresponded to a specific target color (red) mostly in early visual areas while a vertical achromatic grating was simultaneously, physically presented to participants. Consequently, participants' perception of "red" was significantly more frequently than that of "green" in an achromatic vertical grating. This effect was also observed 3 to 5 months after training. These results suggest that long-term associative learning of two different visual features such as color and orientation, was induced most likely in early visual areas. This newly extended technique that induces associative learning may be used as an important tool for understanding and modifying brain function, since associations are fundamental and ubiquitous with respect to brain function.

Learning to associate orientation with color in early visual areas by associative decoded fMRI neurofeedback

Science.gov (United States)

Amano, Kaoru; Shibata, Kazuhisa; Kawato, Mitsuo; Sasaki, Yuka; Watanabe, Takeo

2016-01-01

Summary Associative learning is an essential brain process where the contingency of different items increases after training. Associative learning has been found to occur in many brain regions [1-4]. However, there is no clear evidence that associative learning of visual features occurs in early visual areas, although a number of studies have indicated that learning of a single visual feature (perceptual learning) involves early visual areas [5-8]. Here, via decoded functional magnetic resonance imaging (fMRI) neurofeedback, termed “DecNef” [9], we tested whether associative learning of color and orientation can be created in early visual areas. During three days' training, DecNef induced fMRI signal patterns that corresponded to a specific target color (red) mostly in early visual areas while a vertical achromatic grating was physically presented to participants. As a result, participants came to perceive “red” significantly more frequently than “green” in an achromatic vertical grating. This effect was also observed 3 to 5 months after the training. These results suggest that long-term associative learning of the two different visual features such as color and orientation was created most likely in early visual areas. This newly extended technique that induces associative learning is called “A(ssociative)-DecNef” and may be used as an important tool for understanding and modifying brain functions, since associations are fundamental and ubiquitous functions in the brain. PMID:27374335

Learning to Associate Orientation with Color in Early Visual Areas by Associative Decoded fMRI Neurofeedback.

Science.gov (United States)

Amano, Kaoru; Shibata, Kazuhisa; Kawato, Mitsuo; Sasaki, Yuka; Watanabe, Takeo

2016-07-25

Associative learning is an essential brain process where the contingency of different items increases after training. Associative learning has been found to occur in many brain regions [1-4]. However, there is no clear evidence that associative learning of visual features occurs in early visual areas, although a number of studies have indicated that learning of a single visual feature (perceptual learning) involves early visual areas [5-8]. Here, via decoded fMRI neurofeedback termed "DecNef" [9], we tested whether associative learning of orientation and color can be created in early visual areas. During 3 days of training, DecNef induced fMRI signal patterns that corresponded to a specific target color (red) mostly in early visual areas while a vertical achromatic grating was physically presented to participants. As a result, participants came to perceive "red" significantly more frequently than "green" in an achromatic vertical grating. This effect was also observed 3-5 months after the training. These results suggest that long-term associative learning of two different visual features such as orientation and color was created, most likely in early visual areas. This newly extended technique that induces associative learning is called "A-DecNef," and it may be used as an important tool for understanding and modifying brain functions because associations are fundamental and ubiquitous functions in the brain. Copyright © 2016 Elsevier Ltd. All rights reserved.

Neural dynamics of learning sound-action associations.

Directory of Open Access Journals (Sweden)

Adam McNamara

Full Text Available A motor component is pre-requisite to any communicative act as one must inherently move to communicate. To learn to make a communicative act, the brain must be able to dynamically associate arbitrary percepts to the neural substrate underlying the pre-requisite motor activity. We aimed to investigate whether brain regions involved in complex gestures (ventral pre-motor cortex, Brodmann Area 44 were involved in mediating association between novel abstract auditory stimuli and novel gestural movements. In a functional resonance imaging (fMRI study we asked participants to learn associations between previously unrelated novel sounds and meaningless gestures inside the scanner. We use functional connectivity analysis to eliminate the often present confound of 'strategic covert naming' when dealing with BA44 and to rule out effects of non-specific reductions in signal. Brodmann Area 44, a region incorporating Broca's region showed strong, bilateral, negative correlation of BOLD (blood oxygen level dependent response with learning of sound-action associations during data acquisition. Left-inferior-parietal-lobule (l-IPL and bilateral loci in and around visual area V5, right-orbital-frontal-gyrus, right-hippocampus, left-para-hippocampus, right-head-of-caudate, right-insula and left-lingual-gyrus also showed decreases in BOLD response with learning. Concurrent with these decreases in BOLD response, an increasing connectivity between areas of the imaged network as well as the right-middle-frontal-gyrus with rising learning performance was revealed by a psychophysiological interaction (PPI analysis. The increasing connectivity therefore occurs within an increasingly energy efficient network as learning proceeds. Strongest learning related connectivity between regions was found when analysing BA44 and l-IPL seeds. The results clearly show that BA44 and l-IPL is dynamically involved in linking gesture and sound and therefore provides evidence that one of

Awake, Offline Processing during Associative Learning.

Directory of Open Access Journals (Sweden)

James K Bursley

Full Text Available Offline processing has been shown to strengthen memory traces and enhance learning in the absence of conscious rehearsal or awareness. Here we evaluate whether a brief, two-minute offline processing period can boost associative learning and test a memory reactivation account for these offline processing effects. After encoding paired associates, subjects either completed a distractor task for two minutes or were immediately tested for memory of the pairs in a counterbalanced, within-subjects functional magnetic resonance imaging study. Results showed that brief, awake, offline processing improves memory for associate pairs. Moreover, multi-voxel pattern analysis of the neuroimaging data suggested reactivation of encoded memory representations in dorsolateral prefrontal cortex during offline processing. These results signify the first demonstration of awake, active, offline enhancement of associative memory and suggest that such enhancement is accompanied by the offline reactivation of encoded memory representations.

Genetic dissection of memory for associative and non-associative learning in Caenorhabditis elegans.

Science.gov (United States)

Lau, H L; Timbers, T A; Mahmoud, R; Rankin, C H

2013-03-01

The distinction between non-associative and associative forms of learning has historically been based on the behavioral training paradigm. Through discovering the molecular mechanisms that mediate learning, we can develop a deeper understanding of the relationships between different forms of learning. Here, we genetically dissect short- and long-term memory for a non-associative form of learning, habituation and an associative form of learning, context conditioning for habituation, in the nematode Caenorhabditis elegans. In short-term chemosensory context conditioning for habituation, worms trained and tested in the presence of either a taste (sodium acetate) or smell (diacetyl) context cue show greater retention of habituation to tap stimuli when compared with animals trained and tested without a salient cue. Long-term memory for olfactory context conditioning was observed 24 h after a training procedure that does not normally induce 24 h memory. Like long-term habituation, this long-term memory was dependent on the transcription factor cyclic AMP-response element-binding protein. Worms with mutations in glr-1 [a non-N-methyl-d-aspartate (NMDA)-type glutamate receptor subunit] showed short-term but not long-term habituation or short- or long-term context conditioning. Worms with mutations in nmr-1 (an NMDA-receptor subunit) showed normal short- and long-term memory for habituation but did not show either short- or long-term context conditioning. Rescue of nmr-1 in the RIM interneurons rescued short- and long-term olfactory context conditioning leading to the hypothesis that these interneurons function to integrate information from chemosensory and mechanosensory systems for associative learning. © 2012 The Authors. Genes, Brain and Behavior © 2012 Blackwell Publishing Ltd and International Behavioural and Neural Genetics Society.

Genetic attack on neural cryptography.

Science.gov (United States)

Ruttor, Andreas; Kinzel, Wolfgang; Naeh, Rivka; Kanter, Ido

2006-03-01

Different scaling properties for the complexity of bidirectional synchronization and unidirectional learning are essential for the security of neural cryptography. Incrementing the synaptic depth of the networks increases the synchronization time only polynomially, but the success of the geometric attack is reduced exponentially and it clearly fails in the limit of infinite synaptic depth. This method is improved by adding a genetic algorithm, which selects the fittest neural networks. The probability of a successful genetic attack is calculated for different model parameters using numerical simulations. The results show that scaling laws observed in the case of other attacks hold for the improved algorithm, too. The number of networks needed for an effective attack grows exponentially with increasing synaptic depth. In addition, finite-size effects caused by Hebbian and anti-Hebbian learning are analyzed. These learning rules converge to the random walk rule if the synaptic depth is small compared to the square root of the system size.

Genetic attack on neural cryptography

International Nuclear Information System (INIS)

Ruttor, Andreas; Kinzel, Wolfgang; Naeh, Rivka; Kanter, Ido

2006-01-01

Different scaling properties for the complexity of bidirectional synchronization and unidirectional learning are essential for the security of neural cryptography. Incrementing the synaptic depth of the networks increases the synchronization time only polynomially, but the success of the geometric attack is reduced exponentially and it clearly fails in the limit of infinite synaptic depth. This method is improved by adding a genetic algorithm, which selects the fittest neural networks. The probability of a successful genetic attack is calculated for different model parameters using numerical simulations. The results show that scaling laws observed in the case of other attacks hold for the improved algorithm, too. The number of networks needed for an effective attack grows exponentially with increasing synaptic depth. In addition, finite-size effects caused by Hebbian and anti-Hebbian learning are analyzed. These learning rules converge to the random walk rule if the synaptic depth is small compared to the square root of the system size

Genetic attack on neural cryptography

Science.gov (United States)

Ruttor, Andreas; Kinzel, Wolfgang; Naeh, Rivka; Kanter, Ido

2006-03-01

Different scaling properties for the complexity of bidirectional synchronization and unidirectional learning are essential for the security of neural cryptography. Incrementing the synaptic depth of the networks increases the synchronization time only polynomially, but the success of the geometric attack is reduced exponentially and it clearly fails in the limit of infinite synaptic depth. This method is improved by adding a genetic algorithm, which selects the fittest neural networks. The probability of a successful genetic attack is calculated for different model parameters using numerical simulations. The results show that scaling laws observed in the case of other attacks hold for the improved algorithm, too. The number of networks needed for an effective attack grows exponentially with increasing synaptic depth. In addition, finite-size effects caused by Hebbian and anti-Hebbian learning are analyzed. These learning rules converge to the random walk rule if the synaptic depth is small compared to the square root of the system size.

Perception of collaborative learning in associate degree students in Hong Kong.

Science.gov (United States)

Shek, Daniel T L; Shek, Moses M W

2013-01-01

Although collaborative learning has been widely researched in Western contexts, no study has been carried out to understand how associate degree students look at collaborative learning in Hong Kong. In this study, perceptions of and attitudes to collaborative learning among associate degree students were studied. A total of 44 associate degree students completed an online questionnaire including measures of perceived benefits and attitudes to collaborative learning, and social-emotional competence. Results showed that there were no significant differences between male and female students on perceived benefits of and attitudes towards collaborative learning. Social-emotional competence was related to perceived benefits of and attitudes to collaborative learning. Attitudes were also related to perceived benefits of collaborative learning. This paper is the first known study looking at the relationships among perceived benefits and attitudes to collaborative learning and social-emotional competence in Chinese associate degree students in different Chinese contexts.

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.

Cortical plasticity associated with Braille learning.

Science.gov (United States)

Hamilton, R H; Pascual-Leone, A

1998-05-01

Blind subjects who learn to read Braille must acquire the ability to extract spatial information from subtle tactile stimuli. In order to accomplish this, neuroplastic changes appear to take place. During Braille learning, the sensorimotor cortical area devoted to the representation of the reading finger enlarges. This enlargement follows a two-step process that can be demonstrated with transcranial magnetic stimulation mapping and suggests initial unmasking of existing connections and eventual establishment of more stable structural changes. In addition, Braille learning appears to be associated with the recruitment of parts of the occipital, formerly `visual', cortex (V1 and V2) for tactile information processing. In blind, proficient Braille readers, the occipital cortex can be shown not only to be associated with tactile Braille reading but also to be critical for reading accuracy. Recent studies suggest the possibility of applying non-invasive neurophysiological techniques to guide and improve functional outcomes of these plastic changes. Such interventions might provide a means of accelerating functional adjustment to blindness.

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.

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.

The power of associative learning and the ontogeny of optimal behaviour.

Science.gov (United States)

Enquist, Magnus; Lind, Johan; Ghirlanda, Stefano

2016-11-01

Behaving efficiently (optimally or near-optimally) is central to animals' adaptation to their environment. Much evolutionary biology assumes, implicitly or explicitly, that optimal behavioural strategies are genetically inherited, yet the behaviour of many animals depends crucially on learning. The question of how learning contributes to optimal behaviour is largely open. Here we propose an associative learning model that can learn optimal behaviour in a wide variety of ecologically relevant circumstances. The model learns through chaining, a term introduced by Skinner to indicate learning of behaviour sequences by linking together shorter sequences or single behaviours. Our model formalizes the concept of conditioned reinforcement (the learning process that underlies chaining) and is closely related to optimization algorithms from machine learning. Our analysis dispels the common belief that associative learning is too limited to produce 'intelligent' behaviour such as tool use, social learning, self-control or expectations of the future. Furthermore, the model readily accounts for both instinctual and learned aspects of behaviour, clarifying how genetic evolution and individual learning complement each other, and bridging a long-standing divide between ethology and psychology. We conclude that associative learning, supported by genetic predispositions and including the oft-neglected phenomenon of conditioned reinforcement, may suffice to explain the ontogeny of optimal behaviour in most, if not all, non-human animals. Our results establish associative learning as a more powerful optimizing mechanism than acknowledged by current opinion.

Audiovisual Association Learning in the Absence of Primary Visual Cortex

OpenAIRE

Seirafi, Mehrdad; De Weerd, Peter; Pegna, Alan J.; de Gelder, Beatrice

2016-01-01

Learning audiovisual associations is mediated by the primary cortical areas; however, recent animal studies suggest that such learning can take place even in the absence of the primary visual cortex. Other studies have demonstrated the involvement of extra-geniculate pathways and especially the superior colliculus (SC) in audiovisual association learning. Here, we investigated such learning in a rare human patient with complete loss of the bilateral striate cortex. We carried out an implicit ...

The power of associative learning and the ontogeny of optimal behaviour

Science.gov (United States)

Enquist, Magnus; Lind, Johan

2016-01-01

Behaving efficiently (optimally or near-optimally) is central to animals' adaptation to their environment. Much evolutionary biology assumes, implicitly or explicitly, that optimal behavioural strategies are genetically inherited, yet the behaviour of many animals depends crucially on learning. The question of how learning contributes to optimal behaviour is largely open. Here we propose an associative learning model that can learn optimal behaviour in a wide variety of ecologically relevant circumstances. The model learns through chaining, a term introduced by Skinner to indicate learning of behaviour sequences by linking together shorter sequences or single behaviours. Our model formalizes the concept of conditioned reinforcement (the learning process that underlies chaining) and is closely related to optimization algorithms from machine learning. Our analysis dispels the common belief that associative learning is too limited to produce ‘intelligent’ behaviour such as tool use, social learning, self-control or expectations of the future. Furthermore, the model readily accounts for both instinctual and learned aspects of behaviour, clarifying how genetic evolution and individual learning complement each other, and bridging a long-standing divide between ethology and psychology. We conclude that associative learning, supported by genetic predispositions and including the oft-neglected phenomenon of conditioned reinforcement, may suffice to explain the ontogeny of optimal behaviour in most, if not all, non-human animals. Our results establish associative learning as a more powerful optimizing mechanism than acknowledged by current opinion. PMID:28018662

No association of the BDNF val66met polymorphism with implicit associative vocabulary and motor learning.

Directory of Open Access Journals (Sweden)

Nils Freundlieb

Full Text Available Brain-derived neurotrophic factor (BDNF has been suggested to play a major role in plasticity, neurogenesis and learning in the adult brain. The BDNF gene contains a common val66met polymorphism associated with decreased activity-dependent excretion of BDNF and a potential influence on behaviour, more specifically, on motor learning. The objective of this study was to determine the influence of the BDNF val66met polymorphism on short-term implicit associative learning and whether its influence is cognitive domain-specific (motor vs. language. A sample of 38 young healthy participants was genotyped, screened for background and neuropsychological differences, and tested with two associative implicit learning paradigms in two different cognitive domains, i.e., motor and vocabulary learning. Subjects performed the serial reaction time task (SRTT to determine implicit motor learning and a recently established associative vocabulary learning task (AVL for implicit learning of action and object words. To determine the influence of the BDNF polymorphism on domain-specific implicit learning, behavioural improvements in the two tasks were compared between val/val (n = 22 and met carriers (val/met: n = 15 and met/met: n = 1. There was no evidence for an impact of the BDNF val66met polymorphism on the behavioural outcome in implicit short-term learning paradigms in young healthy subjects. Whether this polymorphism plays a relevant role in long-term training paradigms or in subjects with impaired neuronal plasticity or reduced learning capacity, such as aged individuals, demented patients or patients with brain lesions, has to be determined in future studies.

The role of within-compound associations in learning about absent cues.

Science.gov (United States)

Witnauer, James E; Miller, Ralph R

2011-05-01

When two cues are reinforced together (in compound), most associative models assume that animals learn an associative network that includes direct cue-outcome associations and a within-compound association. All models of associative learning subscribe to the importance of cue-outcome associations, but most models assume that within-compound associations are irrelevant to each cue's subsequent behavioral control. In the present article, we present an extension of Van Hamme and Wasserman's (Learning and Motivation 25:127-151, 1994) model of retrospective revaluation based on learning about absent cues that are retrieved through within-compound associations. The model was compared with a model lacking retrieval through within-compound associations. Simulations showed that within-compound associations are necessary for the model to explain higher-order retrospective revaluation and the observed greater retrospective revaluation after partial reinforcement than after continuous reinforcement alone. These simulations suggest that the associability of an absent stimulus is determined by the extent to which the stimulus is activated through the within-compound association.

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.

Functionally segregated neural substrates for arbitrary audiovisual paired-association learning.

Science.gov (United States)

Tanabe, Hiroki C; Honda, Manabu; Sadato, Norihiro

2005-07-06

To clarify the neural substrates and their dynamics during crossmodal association learning, we conducted functional magnetic resonance imaging (MRI) during audiovisual paired-association learning of delayed matching-to-sample tasks. Thirty subjects were involved in the study; 15 performed an audiovisual paired-association learning task, and the remainder completed a control visuo-visual task. Each trial consisted of the successive presentation of a pair of stimuli. Subjects were asked to identify predefined audiovisual or visuo-visual pairs by trial and error. Feedback for each trial was given regardless of whether the response was correct or incorrect. During the delay period, several areas showed an increase in the MRI signal as learning proceeded: crossmodal activity increased in unimodal areas corresponding to visual or auditory areas, and polymodal responses increased in the occipitotemporal junction and parahippocampal gyrus. This pattern was not observed in the visuo-visual intramodal paired-association learning task, suggesting that crossmodal associations might be formed by binding unimodal sensory areas via polymodal regions. In both the audiovisual and visuo-visual tasks, the MRI signal in the superior temporal sulcus (STS) in response to the second stimulus and feedback peaked during the early phase of learning and then decreased, indicating that the STS might be key to the creation of paired associations, regardless of stimulus type. In contrast to the activity changes in the regions discussed above, there was constant activity in the frontoparietal circuit during the delay period in both tasks, implying that the neural substrates for the formation and storage of paired associates are distinct from working memory circuits.

Associative Cognitive CREED for Successful Grammar Learning

Directory of Open Access Journals (Sweden)

Andrias Tri Susanto

2016-06-01

Full Text Available This research article reports a qualitative study which was conducted to investigate ways successful EFL learners learned English grammar. The subjects of this research were eight successful EFL learners from six different countries in Asia: China, Indonesia, Japan, South Korea, Thailand, and Vietnam. The data was collected by interviewing each subject in person individually at an agreed time and place. The result showed that all the grammar learning processes described by the subjects were closely linked to the framework of Associative Cognitive CREED. There were also some contributing factors that could be integrally combined salient to the overall grammar learning process. However, interestingly, each subject emphasized different aspects of learning.

Word, nonword and visual paired associate learning in Dutch dyslexic children

NARCIS (Netherlands)

Messbauer, V.C.S.; de Jong, P.F.

2003-01-01

Verbal and non-verbal learning were investigated in 21 8-11-year-old dyslexic children and chronological-age controls, and in 21 7-9-year-old reading-age controls. Tasks involved the paired associate learning of words, nonwords, or symbols with pictures. Both learning and retention of associations

Semantic and associative factors in probability learning with words.

Science.gov (United States)

Schipper, L M; Hanson, B L; Taylor, G; Thorpe, J A

1973-09-01

Using a probability-learning technique with a single word as the cue and with the probability of a given event following this word fixed at .80, it was found (1) that neither high nor low associates to the original word and (2) that neither synonyms nor antonyms showed differential learning curves subsequent to original learning when the probability for the following event was shifted to .20. In a second study when feedback, in the form of knowledge of results, was withheld, there was a clear-cut similarity of predictions to the originally trained word and the synonyms of both high and low association value and a dissimilarity of these words to a set of antonyms of both high and low association value. Two additional studies confirmed the importance of the semantic dimension as compared with association value as traditionally measured.

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.

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

Learned Interval Time Facilitates Associate Memory Retrieval

Science.gov (United States)

van de Ven, Vincent; Kochs, Sarah; Smulders, Fren; De Weerd, Peter

2017-01-01

The extent to which time is represented in memory remains underinvestigated. We designed a time paired associate task (TPAT) in which participants implicitly learned cue-time-target associations between cue-target pairs and specific cue-target intervals. During subsequent memory testing, participants showed increased accuracy of identifying…

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.

Challenges Associated with Teaching and Learning of English ...

African Journals Online (AJOL)

Challenges Associated with Teaching and Learning of English Grammar in Nigerian Secondary Schools. ... Abstract. This paper discussed the challenges which are associated with the teaching and ... AJOL African Journals Online. HOW TO ...

Aversive learning of odor-heat associations in ants.

Science.gov (United States)

Desmedt, Lucie; Baracchi, David; Devaud, Jean-Marc; Giurfa, Martin; d'Ettorre, Patrizia

2017-12-15

Ants have recently emerged as useful models for the study of olfactory learning. In this framework, the development of a protocol for the appetitive conditioning of the maxilla-labium extension response (MaLER) provided the possibility of studying Pavlovian odor-food learning in a controlled environment. Here we extend these studies by introducing the first Pavlovian aversive learning protocol for harnessed ants in the laboratory. We worked with carpenter ants Camponotus aethiops and first determined the capacity of different temperatures applied to the body surface to elicit the typical aversive mandible opening response (MOR). We determined that 75°C is the optimal temperature to induce MOR and chose the hind legs as the stimulated body region because of their high sensitivity. We then studied the ability of ants to learn and remember odor-heat associations using 75°C as the unconditioned stimulus. We studied learning and short-term retention after absolute (one odor paired with heat) and differential conditioning (a punished odor versus an unpunished odor). Our results show that ants successfully learn the odor-heat association under a differential-conditioning regime and thus exhibit a conditioned MOR to the punished odor. Yet, their performance under an absolute-conditioning regime is poor. These results demonstrate that ants are capable of aversive learning and confirm previous findings about the different attentional resources solicited by differential and absolute conditioning in general. © 2017. Published by The Company of Biologists Ltd.

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.

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

Early onset marijuana use is associated with learning inefficiencies.

Science.gov (United States)

Schuster, Randi Melissa; Hoeppner, Susanne S; Evins, A Eden; Gilman, Jodi M

2016-05-01

Verbal memory difficulties are the most widely reported and persistent cognitive deficit associated with early onset marijuana use. Yet, it is not known what memory stages are most impaired in those with early marijuana use. Forty-eight young adults, aged 18-25, who used marijuana at least once per week and 48 matched nonusing controls (CON) completed the California Verbal Learning Test, Second Edition (CVLT-II). Marijuana users were stratified by age of initial use: early onset users (EMJ), who started using marijuana at or before age 16 (n = 27), and late onset marijuana user group (LMJ), who started using marijuana after age 16 (n = 21). Outcome variables included trial immediate recall, total learning, clustering strategies (semantic clustering, serial clustering, ratio of semantic to serial clustering, and total number of strategies used), delayed recall, and percent retention. Learning improved with repetition, with no group effect on the learning slope. EMJ learned fewer words overall than LMJ or CON. There was no difference between LMJ and CON in total number of words learned. Reduced overall learning mediated the effect on reduced delayed recall among EMJ, but not CON or LMJ. Learning improved with greater use of semantic versus serial encoding, but this did not vary between groups. EMJ was not related to delayed recall after adjusting for encoding. Young adults reporting early onset marijuana use had learning weaknesses, which accounted for the association between early onset marijuana use and delayed recall. No amnestic effect of marijuana use was observed. (PsycINFO Database Record (c) 2016 APA, all rights reserved).

A Functional Genomic Analysis of NF1-Associated Learning Disabilities

National Research Council Canada - National Science Library

Tang, Shao-Jun

2006-01-01

Learning disabilities severely deteriorate the life of many NFI patients. However, the pathogenic process for NFI-associated learning disabilities has not been fully understood and an effective therapy is not available...

Learning the association between a context and a target location in infancy.

Science.gov (United States)

Bertels, Julie; San Anton, Estibaliz; Gebuis, Titia; Destrebecqz, Arnaud

2017-07-01

Extracting the statistical regularities present in the environment is a central learning mechanism in infancy. For instance, infants are able to learn the associations between simultaneously or successively presented visual objects (Fiser & Aslin, ; Kirkham, Slemmer & Johnson, ). The present study extends these results by investigating whether infants can learn the association between a target location and the context in which it is presented. With this aim, we used a visual associative learning procedure inspired by the contextual cuing paradigm, with infants from 8 to 12 months of age. In two experiments, in which we varied the complexity of the stimuli, we first habituated infants to several scenes where the location of a target (a cartoon character) was consistently associated with a context, namely a specific configuration of geometrical shapes. Second, we examined whether infants learned the covariation between the target location and the context by measuring looking times at scenes that either respected or violated the association. In both experiments, results showed that infants learned the target-context associations, as they looked longer at the familiar scenes than at the novel ones. In particular, infants selected clusters of co-occurring contextual shapes and learned the covariation between the target location and this subset. These results support the existence of a powerful and versatile statistical learning mechanism that may influence the orientation of infants' visual attention toward areas of interest in their environment during early developmental stages. A video abstract of this article can be viewed at: https://www.youtube.com/watch?v=9Hm1unyLBn0. © 2016 John Wiley & Sons Ltd.

Recommendation System Based On Association Rules For Distributed E-Learning Management Systems

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Mihai, Gabroveanu

2015-09-01

Traditional Learning Management Systems are installed on a single server where learning materials and user data are kept. To increase its performance, the Learning Management System can be installed on multiple servers; learning materials and user data could be distributed across these servers obtaining a Distributed Learning Management System. In this paper is proposed the prototype of a recommendation system based on association rules for Distributed Learning Management System. Information from LMS databases is analyzed using distributed data mining algorithms in order to extract the association rules. Then the extracted rules are used as inference rules to provide personalized recommendations. The quality of provided recommendations is improved because the rules used to make the inferences are more accurate, since these rules aggregate knowledge from all e-Learning systems included in Distributed Learning Management System.

Mirror Neurons from Associative Learning

OpenAIRE

Catmur, Caroline; Press, Clare; Heyes, Cecilia

2016-01-01

Mirror neurons fire both when executing actions and observing others perform similar actions. Their sensorimotor matching properties have generally been considered a genetic adaptation for social cognition; however, in the present chapter we argue that the evidence in favor of this account is not compelling. Instead we present evidence supporting an alternative account: that mirror neurons’ matching properties arise from associative learning during individual development. Notably, this proces...

Neuronal representations of stimulus associations develop in the temporal lobe during learning.

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Messinger, A; Squire, L R; Zola, S M; Albright, T D

2001-10-09

Visual stimuli that are frequently seen together become associated in long-term memory, such that the sight of one stimulus readily brings to mind the thought or image of the other. It has been hypothesized that acquisition of such long-term associative memories proceeds via the strengthening of connections between neurons representing the associated stimuli, such that a neuron initially responding only to one stimulus of an associated pair eventually comes to respond to both. Consistent with this hypothesis, studies have demonstrated that individual neurons in the primate inferior temporal cortex tend to exhibit similar responses to pairs of visual stimuli that have become behaviorally associated. In the present study, we investigated the role of these areas in the formation of conditional visual associations by monitoring the responses of individual neurons during the learning of new stimulus pairs. We found that many neurons in both area TE and perirhinal cortex came to elicit more similar neuronal responses to paired stimuli as learning proceeded. Moreover, these neuronal response changes were learning-dependent and proceeded with an average time course that paralleled learning. This experience-dependent plasticity of sensory representations in the cerebral cortex may underlie the learning of associations between objects.

Critical evidence for the prediction error theory in associative learning.

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Terao, Kanta; Matsumoto, Yukihisa; Mizunami, Makoto

2015-03-10

In associative learning in mammals, it is widely accepted that the discrepancy, or error, between actual and predicted reward determines whether learning occurs. Complete evidence for the prediction error theory, however, has not been obtained in any learning systems: Prediction error theory stems from the finding of a blocking phenomenon, but blocking can also be accounted for by other theories, such as the attentional theory. We demonstrated blocking in classical conditioning in crickets and obtained evidence to reject the attentional theory. To obtain further evidence supporting the prediction error theory and rejecting alternative theories, we constructed a neural model to match the prediction error theory, by modifying our previous model of learning in crickets, and we tested a prediction from the model: the model predicts that pharmacological intervention of octopaminergic transmission during appetitive conditioning impairs learning but not formation of reward prediction itself, and it thus predicts no learning in subsequent training. We observed such an "auto-blocking", which could be accounted for by the prediction error theory but not by other competitive theories to account for blocking. This study unambiguously demonstrates validity of the prediction error theory in associative learning.

A Functional Genomic Analysis of NF1-Associated Learning Disabilities

National Research Council Canada - National Science Library

Tang, Shao-Jun

2008-01-01

Learning disabilities severely deteriorate the life of many NF1 patients. However, the pathogenic process for NF1-associated learning disabilities has not been fully understood and an effective therapy is not available...

A Functional Genomic Analysis of NF1-Associated Learning Disabilities

National Research Council Canada - National Science Library

Tang, Shao-Jun

2007-01-01

Learning disabilities severely deteriorate the life of many NF1 patients. However, the pathogenic process for NF1-associated learning disabilities has not been fully understood and an effective therapy is not available...

Path integration of head direction: updating a packet of neural activity at the correct speed using neuronal time constants.

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Walters, D M; Stringer, S M

2010-07-01

A key question in understanding the neural basis of path integration is how individual, spatially responsive, neurons may self-organize into networks that can, through learning, integrate velocity signals to update a continuous representation of location within an environment. It is of vital importance that this internal representation of position is updated at the correct speed, and in real time, to accurately reflect the motion of the animal. In this article, we present a biologically plausible model of velocity path integration of head direction that can solve this problem using neuronal time constants to effect natural time delays, over which associations can be learned through associative Hebbian learning rules. The model comprises a linked continuous attractor network and competitive network. In simulation, we show that the same model is able to learn two different speeds of rotation when implemented with two different values for the time constant, and without the need to alter any other model parameters. The proposed model could be extended to path integration of place in the environment, and path integration of spatial view.

Preexposure effects in spatial learning: From gestaltic to associative and attentional cognitive maps

Directory of Open Access Journals (Sweden)

Edward S. Redhead

2002-01-01

Full Text Available In this paper a series of studies and theoretical proposals about how preexposure to environmental cues affects subsequent spatial learning are reviewed. Traditionally, spatial learning had been thought to depend on gestaltic non-associative processes, and well established phenomena such as latent learning or instantaneous transfer have been taken to provide evidence for this sort of cognitive mapping. However, reviewing the literature examining these effects reveals that there is no need to advocate for gestaltic processes since standard associative learning theory provides an adequate framework for accounting for navigation skills. Recent studies reveal that attentional processes play a role in spatial learning. The need for an integrated attentional and associative approach to explain spatial learning is discussed.

Visual and olfactory associative learning in the malaria vector Anopheles gambiae sensu stricto

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Chilaka Nora

2012-01-01

Full Text Available Abstract Background Memory and learning are critical aspects of the ecology of insect vectors of human pathogens because of their potential effects on contacts between vectors and their hosts. Despite this epidemiological importance, there have been only a limited number of studies investigating associative learning in insect vector species and none on Anopheline mosquitoes. Methods A simple behavioural assays was developed to study visual and olfactory associative learning in Anopheles gambiae, the main vector of malaria in Africa. Two contrasted membrane qualities or levels of blood palatability were used as reinforcing stimuli for bi-directional conditioning during blood feeding. Results Under such experimental conditions An. gambiae females learned very rapidly to associate visual (chequered and white patterns and olfactory cues (presence and absence of cheese or Citronella smell with the reinforcing stimuli (bloodmeal quality and remembered the association for up to three days. Associative learning significantly increased with the strength of the conditioning stimuli used. Importantly, learning sometimes occurred faster when a positive reinforcing stimulus (palatable blood was associated with an innately preferred cue (such as a darker visual pattern. However, the use of too attractive a cue (e.g. Shropshire cheese smell was counter-productive and decreased learning success. Conclusions The results address an important knowledge gap in mosquito ecology and emphasize the role of associative memory for An. gambiae's host finding and blood-feeding behaviour with important potential implications for vector control.

Incidental Learning of Rewarded Associations Bolsters Learning on an Associative Task

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Freedberg, Michael; Schacherer, Jonathan; Hazeltine, Eliot

2016-01-01

Reward has been shown to change behavior as a result of incentive learning (by motivating the individual to increase their effort) and instrumental learning (by increasing the frequency of a particular behavior). However, Palminteri et al. (2011) demonstrated that reward can also improve the incidental learning of a motor skill even when…

Biologically Predisposed Learning and Selective Associations in Amygdalar Neurons

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Chung, Ain; Barot, Sabiha K.; Kim, Jeansok J.; Bernstein, Ilene L.

2011-01-01

Modern views on learning and memory accept the notion of biological constraints--that the formation of association is not uniform across all stimuli. Yet cellular evidence of the encoding of selective associations is lacking. Here, conditioned stimuli (CSs) and unconditioned stimuli (USs) commonly employed in two basic associative learning…

Social learning of an associative foraging task in zebrafish

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Zala, Sarah M.; Määttänen, Ilmari

2013-05-01

The zebrafish ( Danio rerio) is increasingly becoming an important model species for studies on the genetic and neural mechanisms controlling behaviour and cognition. Here, we utilized a conditioned place preference (CPP) paradigm to study social learning in zebrafish. We tested whether social interactions with conditioned demonstrators enhance the ability of focal naïve individuals to learn an associative foraging task. We found that the presence of conditioned demonstrators improved focal fish foraging behaviour through the process of social transmission, whereas the presence of inexperienced demonstrators interfered with the learning of the control focal fish. Our results indicate that zebrafish use social learning for finding food and that this CPP paradigm is an efficient assay to study social learning and memory in zebrafish.

Theta synchronization between medial prefrontal cortex and cerebellum is associated with adaptive performance of associative learning behavior

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Chen, Hao; Wang, Yi-jie; Yang, Li; Sui, Jian-feng; Hu, Zhi-an; Hu, Bo

2016-01-01

Associative learning is thought to require coordinated activities among distributed brain regions. For example, to direct behavior appropriately, the medial prefrontal cortex (mPFC) must encode and maintain sensory information and then interact with the cerebellum during trace eyeblink conditioning (TEBC), a commonly-used associative learning model. However, the mechanisms by which these two distant areas interact remain elusive. By simultaneously recording local field potential (LFP) signals from the mPFC and the cerebellum in guinea pigs undergoing TEBC, we found that theta-frequency (5.0–12.0 Hz) oscillations in the mPFC and the cerebellum became strongly synchronized following presentation of auditory conditioned stimulus. Intriguingly, the conditioned eyeblink response (CR) with adaptive timing occurred preferentially in the trials where mPFC-cerebellum theta coherence was stronger. Moreover, both the mPFC-cerebellum theta coherence and the adaptive CR performance were impaired after the disruption of endogenous orexins in the cerebellum. Finally, association of the mPFC -cerebellum theta coherence with adaptive CR performance was time-limited occurring in the early stage of associative learning. These findings suggest that the mPFC and the cerebellum may act together to contribute to the adaptive performance of associative learning behavior by means of theta synchronization. PMID:26879632

You see what you have learned. Evidence for an interrelation of associative learning and visual selective attention.

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Feldmann-Wüstefeld, Tobias; Uengoer, Metin; Schubö, Anna

2015-11-01

Besides visual salience and observers' current intention, prior learning experience may influence deployment of visual attention. Associative learning models postulate that observers pay more attention to stimuli previously experienced as reliable predictors of specific outcomes. To investigate the impact of learning experience on deployment of attention, we combined an associative learning task with a visual search task and measured event-related potentials of the EEG as neural markers of attention deployment. In the learning task, participants categorized stimuli varying in color/shape with only one dimension being predictive of category membership. In the search task, participants searched a shape target while disregarding irrelevant color distractors. Behavioral results showed that color distractors impaired performance to a greater degree when color rather than shape was predictive in the learning task. Neurophysiological results show that the amplified distraction was due to differential attention deployment (N2pc). Experiment 2 showed that when color was predictive for learning, color distractors captured more attention in the search task (ND component) and more suppression of color distractor was required (PD component). The present results thus demonstrate that priority in visual attention is biased toward predictive stimuli, which allows learning experience to shape selection. We also show that learning experience can overrule strong top-down control (blocked tasks, Experiment 3) and that learning experience has a longer-term effect on attention deployment (tasks on two successive days, Experiment 4). © 2015 Society for Psychophysiological Research.

Evolutionary Pseudo-Relaxation Learning Algorithm for Bidirectional Associative Memory

Institute of Scientific and Technical Information of China (English)

Sheng-Zhi Du; Zeng-Qiang Chen; Zhu-Zhi Yuan

2005-01-01

This paper analyzes the sensitivity to noise in BAM (Bidirectional Associative Memory), and then proves the noise immunity of BAM relates not only to the minimum absolute value of net inputs (MAV) but also to the variance of weights associated with synapse connections. In fact, it is a positive monotonically increasing function of the quotient of MAV divided by the variance of weights. Besides, the performance of pseudo-relaxation method depends on learning parameters (λ and ζ), but the relation of them is not linear. So it is hard to find a best combination of λ and ζ which leads to the best BAM performance. And it is obvious that pseudo-relaxation is a kind of local optimization method, so it cannot guarantee to get the global optimal solution. In this paper, a novel learning algorithm EPRBAM (evolutionary psendo-relaxation learning algorithm for bidirectional association memory) employing genetic algorithm and pseudo-relaxation method is proposed to get feasible solution of BAM weight matrix. This algorithm uses the quotient as the fitness of each individual and employs pseudo-relaxation method to adjust individual solution when it does not satisfy constraining condition any more after genetic operation. Experimental results show this algorithm improves noise immunity of BAM greatly. At the same time, EPRBAM does not depend on learning parameters and can get global optimal solution.

Binary Factorization in Hopfield-Like Neural Networks: Single-Step Approximation and Computer Simulations

Czech Academy of Sciences Publication Activity Database

Frolov, A. A.; Sirota, A.M.; Húsek, Dušan; Muraviev, I. P.

2004-01-01

Roč. 14, č. 2 (2004), s. 139-152 ISSN 1210-0552 R&D Projects: GA ČR GA201/01/1192 Grant - others:BARRANDE(EU) 99010-2/99053; Intellectual computer Systems(EU) Grant 2.45 Institutional research plan: CEZ:AV0Z1030915 Keywords : nonlinear binary factor analysis * feature extraction * recurrent neural network * Single-Step approximation * neurodynamics simulation * attraction basins * Hebbian learning * unsupervised learning * neuroscience * brain function modeling Subject RIV: BA - General Mathematics

Mini-review: Prediction errors, attention and associative learning.

Science.gov (United States)

Holland, Peter C; Schiffino, Felipe L

2016-05-01

Most modern theories of associative learning emphasize a critical role for prediction error (PE, the difference between received and expected events). One class of theories, exemplified by the Rescorla-Wagner (1972) model, asserts that PE determines the effectiveness of the reinforcer or unconditioned stimulus (US): surprising reinforcers are more effective than expected ones. A second class, represented by the Pearce-Hall (1980) model, argues that PE determines the associability of conditioned stimuli (CSs), the rate at which they may enter into new learning: the surprising delivery or omission of a reinforcer enhances subsequent processing of the CSs that were present when PE was induced. In this mini-review we describe evidence, mostly from our laboratory, for PE-induced changes in the associability of both CSs and USs, and the brain systems involved in the coding, storage and retrieval of these altered associability values. This evidence favors a number of modifications to behavioral models of how PE influences event processing, and suggests the involvement of widespread brain systems in animals' responses to PE. Copyright © 2016 Elsevier Inc. All rights reserved.

The power of associative learning and the ontogeny of optimal behaviour

OpenAIRE

Enquist, Magnus; Lind, Johan; Ghirlanda, Stefano

2016-01-01

Behaving efficiently (optimally or near-optimally) is central to animals' adaptation to their environment. Much evolutionary biology assumes, implicitly or explicitly, that optimal behavioural strategies are genetically inherited, yet the behaviour of many animals depends crucially on learning. The question of how learning contributes to optimal behaviour is largely open. Here we propose an associative learning model that can learn optimal behaviour in a wide variety of ecologically relevant ...

Olfactory Perceptual Learning Requires Action of Noradrenaline in the Olfactory Bulb: Comparison with Olfactory Associative Learning

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Vinera, Jennifer; Kermen, Florence; Sacquet, Joëlle; Didier, Anne; Mandairon, Nathalie; Richard, Marion

2015-01-01

Noradrenaline contributes to olfactory-guided behaviors but its role in olfactory learning during adulthood is poorly documented. We investigated its implication in olfactory associative and perceptual learning using local infusion of mixed a1-ß adrenergic receptor antagonist (labetalol) in the adult mouse olfactory bulb. We reported that…

Powerful Tests for Multi-Marker Association Analysis Using Ensemble Learning.

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Badri Padhukasahasram

Full Text Available Multi-marker approaches have received a lot of attention recently in genome wide association studies and can enhance power to detect new associations under certain conditions. Gene-, gene-set- and pathway-based association tests are increasingly being viewed as useful supplements to the more widely used single marker association analysis which have successfully uncovered numerous disease variants. A major drawback of single-marker based methods is that they do not look at the joint effects of multiple genetic variants which individually may have weak or moderate signals. Here, we describe novel tests for multi-marker association analyses that are based on phenotype predictions obtained from machine learning algorithms. Instead of assuming a linear or logistic regression model, we propose the use of ensembles of diverse machine learning algorithms for prediction. We show that phenotype predictions obtained from ensemble learning algorithms provide a new framework for multi-marker association analysis. They can be used for constructing tests for the joint association of multiple variants, adjusting for covariates and testing for the presence of interactions. To demonstrate the power and utility of this new approach, we first apply our method to simulated SNP datasets. We show that the proposed method has the correct Type-1 error rates and can be considerably more powerful than alternative approaches in some situations. Then, we apply our method to previously studied asthma-related genes in 2 independent asthma cohorts to conduct association tests.

Reconciling genetic evolution and the associative learning account of mirror neurons through data-acquisition mechanisms.

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Lotem, Arnon; Kolodny, Oren

2014-04-01

An associative learning account of mirror neurons should not preclude genetic evolution of its underlying mechanisms. On the contrary, an associative learning framework for cognitive development should seek heritable variation in the learning rules and in the data-acquisition mechanisms that construct associative networks, demonstrating how small genetic modifications of associative elements can give rise to the evolution of complex cognition.

Attention Cueing and Activity Equally Reduce False Alarm Rate in Visual-Auditory Associative Learning through Improving Memory.

Science.gov (United States)

Nikouei Mahani, Mohammad-Ali; Haghgoo, Hojjat Allah; Azizi, Solmaz; Nili Ahmadabadi, Majid

2016-01-01

In our daily life, we continually exploit already learned multisensory associations and form new ones when facing novel situations. Improving our associative learning results in higher cognitive capabilities. We experimentally and computationally studied the learning performance of healthy subjects in a visual-auditory sensory associative learning task across active learning, attention cueing learning, and passive learning modes. According to our results, the learning mode had no significant effect on learning association of congruent pairs. In addition, subjects' performance in learning congruent samples was not correlated with their vigilance score. Nevertheless, vigilance score was significantly correlated with the learning performance of the non-congruent pairs. Moreover, in the last block of the passive learning mode, subjects significantly made more mistakes in taking non-congruent pairs as associated and consciously reported lower confidence. These results indicate that attention and activity equally enhanced visual-auditory associative learning for non-congruent pairs, while false alarm rate in the passive learning mode did not decrease after the second block. We investigated the cause of higher false alarm rate in the passive learning mode by using a computational model, composed of a reinforcement learning module and a memory-decay module. The results suggest that the higher rate of memory decay is the source of making more mistakes and reporting lower confidence in non-congruent pairs in the passive learning mode.

Reduced autobiographical memory specificity is associated with impaired discrimination learning in anxiety disorder patients

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Lenaert, Bert; Boddez, Yannick; Vervliet, Bram; Schruers, Koen; Hermans, Dirk

2015-01-01

Associative learning plays an important role in the development of anxiety disorders, but a thorough understanding of the variables that impact such learning is still lacking. We investigated whether individual differences in autobiographical memory specificity are related to discrimination learning and generalization. In an associative learning task, participants learned the association between two pictures of female faces and a non-aversive outcome. Subsequently, six morphed pictures functioning as generalization stimuli (GSs) were introduced. In a sample of healthy participants (Study 1), we did not find evidence for differences in discrimination learning as a function of memory specificity. In a sample of anxiety disorder patients (Study 2), individuals who were characterized by low memory specificity showed deficient discrimination learning relative to high specific individuals. In contrast to previous findings, results revealed no effect of memory specificity on generalization. These results indicate that impaired discrimination learning, previously shown in patients suffering from an anxiety disorder, may be—in part—due to limited memory specificity. Together, these studies emphasize the importance of incorporating cognitive variables in associative learning theories and their implications for the development of anxiety disorders. In addition, re-analyses of the data (Study 3) showed that patients suffering from panic disorder showed higher outcome expectancies in the presence of the stimulus that was never followed by an outcome during discrimination training, relative to patients suffering from other anxiety disorders and healthy participants. Because we used a neutral, non-aversive outcome (i.e., drawing of a lightning bolt), these data suggest that learning abnormalities in panic disorder may not be restricted to fear learning, but rather reflect a more general associative learning deficit that also manifests in fear irrelevant contexts. PMID

Differential Recruitment of Distinct Amygdalar Nuclei across Appetitive Associative Learning

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Cole, Sindy; Powell, Daniel J.; Petrovich, Gorica D.

2013-01-01

The amygdala is important for reward-associated learning, but how distinct cell groups within this heterogeneous structure are recruited during appetitive learning is unclear. Here we used Fos induction to map the functional amygdalar circuitry recruited during early and late training sessions of Pavlovian appetitive conditioning. We found that a…

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

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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.

Using an improved association rules mining optimization algorithm in web-based mobile-learning system

Science.gov (United States)

Huang, Yin; Chen, Jianhua; Xiong, Shaojun

2009-07-01

Mobile-Learning (M-learning) makes many learners get the advantages of both traditional learning and E-learning. Currently, Web-based Mobile-Learning Systems have created many new ways and defined new relationships between educators and learners. Association rule mining is one of the most important fields in data mining and knowledge discovery in databases. Rules explosion is a serious problem which causes great concerns, as conventional mining algorithms often produce too many rules for decision makers to digest. Since Web-based Mobile-Learning System collects vast amounts of student profile data, data mining and knowledge discovery techniques can be applied to find interesting relationships between attributes of learners, assessments, the solution strategies adopted by learners and so on. Therefore ,this paper focus on a new data-mining algorithm, combined with the advantages of genetic algorithm and simulated annealing algorithm , called ARGSA(Association rules based on an improved Genetic Simulated Annealing Algorithm), to mine the association rules. This paper first takes advantage of the Parallel Genetic Algorithm and Simulated Algorithm designed specifically for discovering association rules. Moreover, the analysis and experiment are also made to show the proposed method is superior to the Apriori algorithm in this Mobile-Learning system.

Finding Influential Users in Social Media Using Association Rule Learning

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Fredrik Erlandsson

2016-04-01

Full Text Available Influential users play an important role in online social networks since users tend to have an impact on one other. Therefore, the proposed work analyzes users and their behavior in order to identify influential users and predict user participation. Normally, the success of a social media site is dependent on the activity level of the participating users. For both online social networking sites and individual users, it is of interest to find out if a topic will be interesting or not. In this article, we propose association learning to detect relationships between users. In order to verify the findings, several experiments were executed based on social network analysis, in which the most influential users identified from association rule learning were compared to the results from Degree Centrality and Page Rank Centrality. The results clearly indicate that it is possible to identify the most influential users using association rule learning. In addition, the results also indicate a lower execution time compared to state-of-the-art methods.

Associative memory for online learning in noisy environments using self-organizing incremental neural network.

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Sudo, Akihito; Sato, Akihiro; Hasegawa, Osamu

2009-06-01

Associative memory operating in a real environment must perform well in online incremental learning and be robust to noisy data because noisy associative patterns are presented sequentially in a real environment. We propose a novel associative memory that satisfies these requirements. Using the proposed method, new associative pairs that are presented sequentially can be learned accurately without forgetting previously learned patterns. The memory size of the proposed method increases adaptively with learning patterns. Therefore, it suffers neither redundancy nor insufficiency of memory size, even in an environment in which the maximum number of associative pairs to be presented is unknown before learning. Noisy inputs in real environments are classifiable into two types: noise-added original patterns and faultily presented random patterns. The proposed method deals with two types of noise. To our knowledge, no conventional associative memory addresses noise of both types. The proposed associative memory performs as a bidirectional one-to-many or many-to-one associative memory and deals not only with bipolar data, but also with real-valued data. Results demonstrate that the proposed method's features are important for application to an intelligent robot operating in a real environment. The originality of our work consists of two points: employing a growing self-organizing network for an associative memory, and discussing what features are necessary for an associative memory for an intelligent robot and proposing an associative memory that satisfies those requirements.

Age-related changes in contextual associative learning.

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Luu, Trinh T; Pirogovsky, Eva; Gilbert, Paul E

2008-01-01

The hippocampus plays a critical role in processing contextual information. Although age-related changes in the hippocampus are well documented in humans, nonhuman primates, and rodents, few studies have examined contextual learning deficits in old rats. The present study investigated age-related differences in contextual associative learning in young (6 mo) and old (24 mo) rats using olfactory stimuli. Stimuli consisted of common odors mixed in sand and placed in clear plastic cups. Testing was conducted in two boxes that represented two different contexts (Context 1 and Context 2). The contexts varied based on environmental features of the box such as color (black vs. white), visual cues on the walls of the box, and flooring texture. Each rat was simultaneously presented with two cups, one filled with Odor A and one filled with Odor B in each context. In Context 1, the rat received a food reward for digging in the cup containing Odor A, but did not receive a food reward for digging in the cup containing Odor B. In Context 2, the rat was rewarded for digging in the cup containing Odor B, but did receive a reward for digging in the cup containing Odor A. Therefore, the rat learned to associate Context 1 with Odor A and Context 2 with Odor B. The rat was tested for eight days using the same odor problem throughout all days of testing. The results showed no significant difference between young and old rats on the first two days of testing; however, young rats significantly outperformed old rats on Day 3. Young rats continued to maintain superior performance compared to old rats on Days 4-8. The results suggest that aging results in functional impairments in brain regions that support memory for associations between specific cues and their respective context.

I. P. PAVLOV: 100 YEARS OF RESERACH ON ASSOCIATIVE LEARNING

Directory of Open Access Journals (Sweden)

GERMÁN GUTIÉRREZ

2005-07-01

Full Text Available A biographical summary of Ivan Pavlov is presented, emphasizing his academic formation and achievements, and hiscontributions to general science and psychology. His main findings on associative learning are described and three areasof current development in this area are discussed: the study of behavioral mechanisms, the study of neurobiologicalmechanisms and the functional role of learning.

Normal brain activation in schizophrenia patients during associative emotional learning

NARCIS (Netherlands)

Swart, Marte; Liemburg, Edith Jantine; Kortekaas, Rudie; Wiersma, Durk; Bruggeman, Richard; Aleman, Andre

2013-01-01

Emotional deficits are among the core features of schizophrenia and both associative emotional learning and the related ability to verbalize emotions can be reduced. We investigated whether schizophrenia patients demonstrated impaired function of limbic and prefrontal areas during associative

Attentional control of associative learning--a possible role of the central cholinergic system.

Science.gov (United States)

Pauli, Wolfgang M; O'Reilly, Randall C

2008-04-02

How does attention interact with learning? Kruschke [Kruschke, J.K. (2001). Toward a unified Model of Attention in Associative Learning. J. Math. Psychol. 45, 812-863.] proposed a model (EXIT) that captures Mackintosh's [Mackintosh, N.J. (1975). A theory of attention: Variations in the associability of stimuli with reinforcement. Psychological Review, 82(4), 276-298.] framework for attentional modulation of associative learning. We developed a computational model that showed analogous interactions between selective attention and associative learning, but is significantly simplified and, in contrast to EXIT, is motivated by neurophysiological findings. Competition among input representations in the internal representation layer, which increases the contrast between stimuli, is critical for simulating these interactions in human behavior. Furthermore, this competition is modulated in a way that might be consistent with the phasic activation of the central cholinergic system, which modulates activity in sensory cortices. Specifically, phasic increases in acetylcholine can cause increased excitability of both pyramidal excitatory neurons in cortical layers II/III and cortical GABAergic inhibitory interneurons targeting the same pyramidal neurons. These effects result in increased attentional contrast in our model. This model thus represents an initial attempt to link human attentional learning data with underlying neural substrates.

Dynamically stable associative learning: a neurobiologically based ANN and its applications

Science.gov (United States)

Vogl, Thomas P.; Blackwell, Kim L.; Barbour, Garth; Alkon, Daniel L.

1992-07-01

Most currently popular artificial neural networks (ANN) are based on conceptions of neuronal properties that date back to the 1940s and 50s, i.e., to the ideas of McCullough, Pitts, and Hebb. Dystal is an ANN based on current knowledge of neurobiology at the cellular and subcellular level. Networks based on these neurobiological insights exhibit the following advantageous properties: (1) A theoretical storage capacity of bN non-orthogonal memories, where N is the number of output neurons sharing common inputs and b is the number of distinguishable (gray shade) levels. (2) The ability to learn, store, and recall associations among noisy, arbitrary patterns. (3) A local synaptic learning rule (learning depends neither on the output of the post-synaptic neuron nor on a global error term), some of whose consequences are: (4) Feed-forward, lateral, and feed-back connections (as well as time-sensitive connections) are possible without alteration of the learning algorithm; (5) Storage allocation (patch creation) proceeds dynamically as associations are learned (self- organizing); (6) The number of training set presentations required for learning is small (different expressions and/or corrupted by noise, and on reading hand-written digits (98% accuracy) and hand-printed Japanese Kanji (90% accuracy) is demonstrated.

LEAP: biomarker inference through learning and evaluating association patterns.

Science.gov (United States)

Jiang, Xia; Neapolitan, Richard E

2015-03-01

Single nucleotide polymorphism (SNP) high-dimensional datasets are available from Genome Wide Association Studies (GWAS). Such data provide researchers opportunities to investigate the complex genetic basis of diseases. Much of genetic risk might be due to undiscovered epistatic interactions, which are interactions in which combination of several genes affect disease. Research aimed at discovering interacting SNPs from GWAS datasets proceeded in two directions. First, tools were developed to evaluate candidate interactions. Second, algorithms were developed to search over the space of candidate interactions. Another problem when learning interacting SNPs, which has not received much attention, is evaluating how likely it is that the learned SNPs are associated with the disease. A complete system should provide this information as well. We develop such a system. Our system, called LEAP, includes a new heuristic search algorithm for learning interacting SNPs, and a Bayesian network based algorithm for computing the probability of their association. We evaluated the performance of LEAP using 100 1,000-SNP simulated datasets, each of which contains 15 SNPs involved in interactions. When learning interacting SNPs from these datasets, LEAP outperformed seven others methods. Furthermore, only SNPs involved in interactions were found to be probable. We also used LEAP to analyze real Alzheimer's disease and breast cancer GWAS datasets. We obtained interesting and new results from the Alzheimer's dataset, but limited results from the breast cancer dataset. We conclude that our results support that LEAP is a useful tool for extracting candidate interacting SNPs from high-dimensional datasets and determining their probability. © 2015 The Authors. *Genetic Epidemiology published by Wiley Periodicals, Inc.

Associative and sensorimotor learning for parenting involves mirror neurons under the influence of oxytocin.

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Ho, S Shaun; Macdonald, Adam; Swain, James E

2014-04-01

Mirror neuron-based associative learning may be understood according to associative learning theories, in addition to sensorimotor learning theories. This is important for a comprehensive understanding of the role of mirror neurons and related hormone modulators, such as oxytocin, in complex social interactions such as among parent-infant dyads and in examples of mirror neuron function that involve abnormal motor systems such as depression.

Associative and sensorimotor learning for parenting involves mirror neurons under the influence of oxytocin

OpenAIRE

Ho, S. Shaun; MacDonald, Adam; Swain, James E.

2014-01-01

Mirror neuron–based associative learning may be understood according to associative learning theories, in addition to sensorimotor learning theories. This is important for a comprehensive understanding of the role of mirror neurons and related hormone modulators, such as oxytocin, in complex social interactions such as among parent–infant dyads and in examples of mirror neuron function that involve abnormal motor systems such as depression.

Comfort and experience with online learning: trends over nine years and associations with knowledge.

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Cook, David A; Thompson, Warren G

2014-07-01

Some evidence suggests that attitude toward computer-based instruction is an important determinant of success in online learning. We sought to determine how comfort using computers and perceptions of prior online learning experiences have changed over the past decade, and how these associate with learning outcomes. Each year from 2003-2011 we conducted a prospective trial of online learning. As part of each year's study, we asked medicine residents about their comfort using computers and if their previous experiences with online learning were favorable. We assessed knowledge using a multiple-choice test. We used regression to analyze associations and changes over time. 371 internal medicine and family medicine residents participated. Neither comfort with computers nor perceptions of prior online learning experiences showed a significant change across years (p > 0.61), with mean comfort rating 3.96 (maximum 5 = very comfortable) and mean experience rating 4.42 (maximum 6 = strongly agree [favorable]). Comfort showed no significant association with knowledge scores (p = 0.39) but perceptions of prior experiences did, with a 1.56% rise in knowledge score for a 1-point rise in experience score (p = 0.02). Correlations among comfort, perceptions of prior experiences, and number of prior experiences were all small and not statistically significant. Comfort with computers and perceptions of prior experience with online learning remained stable over nine years. Prior good experiences (but not comfort with computers) demonstrated a modest association with knowledge outcomes, suggesting that prior course satisfaction may influence subsequent learning.

The "proactive" model of learning: Integrative framework for model-free and model-based reinforcement learning utilizing the associative learning-based proactive brain concept.

Science.gov (United States)

Zsuga, Judit; Biro, Klara; Papp, Csaba; Tajti, Gabor; Gesztelyi, Rudolf

2016-02-01

Reinforcement learning (RL) is a powerful concept underlying forms of associative learning governed by the use of a scalar reward signal, with learning taking place if expectations are violated. RL may be assessed using model-based and model-free approaches. Model-based reinforcement learning involves the amygdala, the hippocampus, and the orbitofrontal cortex (OFC). The model-free system involves the pedunculopontine-tegmental nucleus (PPTgN), the ventral tegmental area (VTA) and the ventral striatum (VS). Based on the functional connectivity of VS, model-free and model based RL systems center on the VS that by integrating model-free signals (received as reward prediction error) and model-based reward related input computes value. Using the concept of reinforcement learning agent we propose that the VS serves as the value function component of the RL agent. Regarding the model utilized for model-based computations we turned to the proactive brain concept, which offers an ubiquitous function for the default network based on its great functional overlap with contextual associative areas. Hence, by means of the default network the brain continuously organizes its environment into context frames enabling the formulation of analogy-based association that are turned into predictions of what to expect. The OFC integrates reward-related information into context frames upon computing reward expectation by compiling stimulus-reward and context-reward information offered by the amygdala and hippocampus, respectively. Furthermore we suggest that the integration of model-based expectations regarding reward into the value signal is further supported by the efferent of the OFC that reach structures canonical for model-free learning (e.g., the PPTgN, VTA, and VS). (c) 2016 APA, all rights reserved).

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

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

Observational Word Learning: Beyond Propose-But-Verify and Associative Bean Counting.

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Roembke, Tanja; McMurray, Bob

2016-04-01

Learning new words is difficult. In any naming situation, there are multiple possible interpretations of a novel word. Recent approaches suggest that learners may solve this problem by tracking co-occurrence statistics between words and referents across multiple naming situations (e.g. Yu & Smith, 2007), overcoming the ambiguity in any one situation. Yet, there remains debate around the underlying mechanisms. We conducted two experiments in which learners acquired eight word-object mappings using cross-situational statistics while eye-movements were tracked. These addressed four unresolved questions regarding the learning mechanism. First, eye-movements during learning showed evidence that listeners maintain multiple hypotheses for a given word and bring them all to bear in the moment of naming. Second, trial-by-trial analyses of accuracy suggested that listeners accumulate continuous statistics about word/object mappings, over and above prior hypotheses they have about a word. Third, consistent, probabilistic context can impede learning, as false associations between words and highly co-occurring referents are formed. Finally, a number of factors not previously considered in prior analysis impact observational word learning: knowledge of the foils, spatial consistency of the target object, and the number of trials between presentations of the same word. This evidence suggests that observational word learning may derive from a combination of gradual statistical or associative learning mechanisms and more rapid real-time processes such as competition, mutual exclusivity and even inference or hypothesis testing.

Sampling capacity underlies individual differences in human associative learning.

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Byrom, Nicola C; Murphy, Robin A

2014-04-01

Though much work has studied how external factors, such as stimulus properties, influence generalization of associative strength, there has been limited exploration of the influence that internal dispositions may contribute to stimulus processing. Here we report 2 studies using a modified negative patterning discrimination to test the relationship between global processing and generalization. Global processing was associated with stronger negative patterning discrimination, indicative of limited generalization between distinct stimulus compounds and their constituent elements. In Experiment 2, participants pretrained to adopt global processing similarly showed strong negative patterning discrimination. These results demonstrate considerable individual difference in capacity to engage in negative patterning discrimination and suggest that the tendency toward global processing may be one factor explaining this variability. The need for models of learning to account for this variability in learning is discussed.

Acute psychophysiological stress impairs human associative learning.

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Ehlers, M R; Todd, R M

2017-11-01

Addiction is increasingly discussed asa disorder of associative learning processes, with both operant and classical conditioning contributing to the development of maladaptive habits. Stress has long been known to promote drug taking and relapse and has further been shown to shift behavior from goal-directed actions towards more habitual ones. However, it remains to be investigated how acute stress may influence simple associative learning processes that occur before a habit can be established. In the present study, healthy young adults were exposed to either acute stress or a control condition half an hour before performing simple classical and operant conditioning tasks. Psychophysiological measures confirmed successful stress induction. Results of the operant conditioning task revealed reduced instrumental responding under delayed acute stress that resembled behavioral responses to lower levels of reward. The classical conditioning experiment revealed successful conditioning in both experimental groups; however, explicit knowledge of conditioning as indicated by stimulus ratings differentiated the stress and control groups. These findings suggest that operant and classical conditioning are differentially influenced by the delayed effects of acute stress with important implications for the understanding of how new habitual behaviors are initially established. Copyright © 2017 Elsevier Inc. All rights reserved.

Implicit versus explicit associative learning and experimentally induced placebo hypoalgesia

Directory of Open Access Journals (Sweden)

Andrea L Martin-Pichora

2011-03-01

Full Text Available Andrea L Martin-Pichora1,2, Tsipora D. Mankovsky-Arnold3, Joel Katz11Department of Psychology, York University, Toronto, ON, Canada; 2Centre for Student Development and Counseling, Ryerson University, Toronto, ON, Canada; 3Department of Psychology, McGill University, Montreal, QC, CanadaAbstract: The present study examined whether 1 placebo hypoalgesia can be generated through implicit associative learning (ie, conditioning in the absence of conscious awareness and 2 the magnitude of placebo hypoalgesia changes when expectations about pain are made explicit. The temperature of heat pain stimuli was surreptitiously lowered during conditioning trials for the placebo cream and the magnitude of the placebo effect was assessed during a subsequent set of trials when the temperature was the same for both placebo and control conditions. To assess whether placebo hypoalgesia could be generated from an implicit tactile stimulus, a 2 × 2 design was used with direction of cream application as one factor and verbal information about which cream was being applied as the second factor. A significant placebo effect was observed when participants received verbal information about which cream was being applied but not following implicit conditioning alone. However, 87.5% of those who showed a placebo response as the result of implicit conditioning were able to accurately guess the order of cream application during the final trial, despite a lack of awareness about the sensory manipulation and low confidence in their ratings, suggesting implicit learning in some participants. In summary, implicit associative learning was evident in some participants but it was not sufficient to produce a placebo effect suggesting some level of explicit expectation or cognitive mediation may be necessary. Notably, the placebo response was abolished when expectations were made explicit, suggesting a delicate interplay between attention and expectation.Keywords: placebo hypoalgesia

Morvan's syndrome and the sustained absence of all sleep rhythms for months or years: An hypothesis.

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Touzet, Claude

2016-09-01

Despite the predation costs, sleep is ubiquitous in the animal realm. Humans spend a third of their life sleeping, and the quality of sleep has been related to co-morbidity, Alzheimer disease, etc. Excessive wakefulness induces rapid changes in cognitive performances, and it is claimed that one could die of sleep deprivation as quickly as by absence of water. In this context, the fact that a few people are able to go without sleep for months, even years, without displaying any cognitive troubles requires explanations. Theories ascribing sleep to memory consolidation are unable to explain such observations. It is not the case of the theory of sleep as the hebbian reinforcement of the inhibitory synapses (ToS-HRIS). Hebbian learning (Long Term Depression - LTD) guarantees that an efficient inhibitory synapse will lose its efficiency just because it is efficient at avoiding the activation of the post-synaptic neuron. This erosion of the inhibition is replenished by hebbian learning (Long Term Potentiation - LTP) when pre and post-synaptic neurons are active together - which is exactly what happens with the travelling depolarization waves of the slow-wave sleep (SWS). The best documented cases of months-long insomnia are reports of patients with Morvan's syndrome. This syndrome has an autoimmune cause that impedes - among many things - the potassium channels of the post-synaptic neurons, increasing LTP and decreasing LTD. We hypothesize that the absence of inhibitory efficiency erosion during wakefulness (thanks to a decrease of inhibitory LTD) is the cause for an absence of slow-wave sleep (SWS), which results also in the absence of REM sleep. Copyright © 2016 Elsevier Ltd. All rights reserved.

On Informational Characteristics of Willshaw-Like Auto-Associative Memory

Czech Academy of Sciences Publication Activity Database

Frolov, A. A.; Rachkovskij, D.; Húsek, Dušan

2002-01-01

Roč. 12, č. 2 (2002), s. 141-157 ISSN 1210-0552 R&D Projects: GA ČR GA201/01/1192; GA ČR GA201/00/1031 Institutional research plan: AV0Z1030915 Keywords : Willshaw-like * space encoding * attractor neural network * binary Hebbian synapses * computer simulations * constant * informational capacity * attraction basis size * single-step approximation * Gibson-Robinson approximation Subject RIV: BA - General Mathematics

Learning the association between a context and a target location in infancy

NARCIS (Netherlands)

Bertels, Julie; San Anton, Estibaliz; Gebuis, Titia; Destrebecqz, Arnaud

2017-01-01

Extracting the statistical regularities present in the environment is a central learning mechanism in infancy. For instance, infants are able to learn the associations between simultaneously or successively presented visual objects (Fiser & Aslin,; Kirkham, Slemmer & Johnson,). The present study

Exploration of Learning Strategies Associated With Aha Learning Moments.

Science.gov (United States)

Pilcher, Jobeth W

2016-01-01

Educators recognize aha moments as powerful aspects of learning. Yet limited research has been performed regarding how to promote these learning moments. This article describes an exploratory study of aha learning moments as experienced and described by participants. Findings showed use of visuals, scenarios, storytelling, Socratic questions, and expert explanation led to aha learning moments. The findings provide guidance regarding the types of learning strategies that can be used to promote aha moments.

Paired-Associate and Feedback-Based Weather Prediction Tasks Support Multiple Category Learning Systems

OpenAIRE

Li, Kaiyun; Fu, Qiufang; Sun, Xunwei; Zhou, Xiaoyan; Fu, Xiaolan

2016-01-01

It remains unclear whether probabilistic category learning in the feedback-based weather prediction task (FB-WPT) can be mediated by a non-declarative or procedural learning system. To address this issue, we compared the effects of training time and verbal working memory, which influence the declarative learning system but not the non-declarative learning system, in the FB and paired-associate (PA) WPTs, as the PA task recruits a declarative learning system. The results of Experiment 1 showed...

Fuzzy OLAP association rules mining-based modular reinforcement learning approach for multiagent systems.

Science.gov (United States)

Kaya, Mehmet; Alhajj, Reda

2005-04-01

Multiagent systems and data mining have recently attracted considerable attention in the field of computing. Reinforcement learning is the most commonly used learning process for multiagent systems. However, it still has some drawbacks, including modeling other learning agents present in the domain as part of the state of the environment, and some states are experienced much less than others, or some state-action pairs are never visited during the learning phase. Further, before completing the learning process, an agent cannot exhibit a certain behavior in some states that may be experienced sufficiently. In this study, we propose a novel multiagent learning approach to handle these problems. Our approach is based on utilizing the mining process for modular cooperative learning systems. It incorporates fuzziness and online analytical processing (OLAP) based mining to effectively process the information reported by agents. First, we describe a fuzzy data cube OLAP architecture which facilitates effective storage and processing of the state information reported by agents. This way, the action of the other agent, not even in the visual environment. of the agent under consideration, can simply be predicted by extracting online association rules, a well-known data mining technique, from the constructed data cube. Second, we present a new action selection model, which is also based on association rules mining. Finally, we generalize not sufficiently experienced states, by mining multilevel association rules from the proposed fuzzy data cube. Experimental results obtained on two different versions of a well-known pursuit domain show the robustness and effectiveness of the proposed fuzzy OLAP mining based modular learning approach. Finally, we tested the scalability of the approach presented in this paper and compared it with our previous work on modular-fuzzy Q-learning and ordinary Q-learning.

Face-name association learning and brain structural substrates in alcoholism.

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Pitel, Anne-Lise; Chanraud, Sandra; Rohlfing, Torsten; Pfefferbaum, Adolf; Sullivan, Edith V

2012-07-01

Associative learning is required for face-name association and is impaired in alcoholism, but the cognitive processes and brain structural components underlying this deficit remain unclear. It is also unknown whether prompting alcoholics to implement a deep level of processing during face-name encoding would enhance performance. Abstinent alcoholics and controls performed a levels-of-processing face-name learning task. Participants indicated whether the face was that of an honest person (deep encoding) or that of a man (shallow encoding). Retrieval was examined using an associative (face-name) recognition task and a single-item (face or name only) recognition task. Participants also underwent 3T structural MRI. Compared with controls, alcoholics had poorer associative and single-item learning and performed at similar levels. Level of processing at encoding had little effect on recognition performance but affected reaction time (RT). Correlations with brain volumes were generally modest and based primarily on RT in alcoholics, where the deeper the processing at encoding, the more restricted the correlations with brain volumes. In alcoholics, longer control task RTs correlated modestly with smaller tissue volumes across several anterior to posterior brain regions; shallow encoding correlated with calcarine and striatal volumes; deep encoding correlated with precuneus and parietal volumes; and associative recognition RT correlated with cerebellar volumes. In controls, poorer associative recognition with deep encoding correlated significantly with smaller volumes of frontal and striatal structures. Despite prompting, alcoholics did not take advantage of encoding memoranda at a deep level to enhance face-name recognition accuracy. Nonetheless, conditions of deeper encoding resulted in faster RTs and more specific relations with regional brain volumes than did shallow encoding. The normal relation between associative recognition and corticostriatal volumes was not

The clinical associate curriculum . the learning theory underpinning ...

African Journals Online (AJOL)

The Bachelor of Clinical Medical Practice (BCMP) is a new degree at the University of Pretoria (UP), designed to create a new category of mid-level medical workers, namely clinical associates. UP produced its first 44 graduates in 2011. The BCMP created the opportunity to innovate learning and teaching through ...

Associative Learning during Early Adulthood Enhances Later Memory Retention in Honeybees

Science.gov (United States)

Arenas, Andrés; Fernández, Vanesa M.; Farina, Walter M.

2009-01-01

Background Cognitive experiences during the early stages of life play an important role in shaping the future behavior in mammals but also in insects, in which precocious learning can directly modify behaviors later in life depending on both the timing and the rearing environment. However, whether olfactory associative learning acquired early in the adult stage of insects affect memorizing of new learning events has not been studied yet. Methodology Groups of adult honeybee workers that experienced an odor paired with a sucrose solution 5 to 8 days or 9 to 12 days after emergence were previously exposed to (i) a rewarded experience through the offering of scented food, or (ii) a non-rewarded experience with a pure volatile compound in the rearing environment. Principal Findings Early rewarded experiences (either at 1–4 or 5–8 days of adult age) enhanced retention performance in 9–12-day-conditioned bees when they were tested at 17 days of age. The highest retention levels at this age, which could not be improved with prior rewarded experiences, were found for memories established at 5–8 days of adult age. Associative memories acquired at 9–12 days of age showed a weak effect on retention for some pure pre-exposed volatile compounds; whereas the sole exposure of an odor at any younger age did not promote long-term effects on learning performance. Conclusions The associative learning events that occurred a few days after adult emergence improved memorizing in middle-aged bees. In addition, both the timing and the nature of early sensory inputs interact to enhance retention of new learning events acquired later in life, an important matter in the social life of honeybees. PMID:19956575

Associative learning during early adulthood enhances later memory retention in honeybees.

Directory of Open Access Journals (Sweden)

Andrés Arenas

Full Text Available BACKGROUND: Cognitive experiences during the early stages of life play an important role in shaping the future behavior in mammals but also in insects, in which precocious learning can directly modify behaviors later in life depending on both the timing and the rearing environment. However, whether olfactory associative learning acquired early in the adult stage of insects affect memorizing of new learning events has not been studied yet. METHODOLOGY: Groups of adult honeybee workers that experienced an odor paired with a sucrose solution 5 to 8 days or 9 to 12 days after emergence were previously exposed to (i a rewarded experience through the offering of scented food, or (ii a non-rewarded experience with a pure volatile compound in the rearing environment. PRINCIPAL FINDINGS: Early rewarded experiences (either at 1-4 or 5-8 days of adult age enhanced retention performance in 9-12-day-conditioned bees when they were tested at 17 days of age. The highest retention levels at this age, which could not be improved with prior rewarded experiences, were found for memories established at 5-8 days of adult age. Associative memories acquired at 9-12 days of age showed a weak effect on retention for some pure pre-exposed volatile compounds; whereas the sole exposure of an odor at any younger age did not promote long-term effects on learning performance. CONCLUSIONS: The associative learning events that occurred a few days after adult emergence improved memorizing in middle-aged bees. In addition, both the timing and the nature of early sensory inputs interact to enhance retention of new learning events acquired later in life, an important matter in the social life of honeybees.

How synapses can enhance sensibility of a neural network

Science.gov (United States)

Protachevicz, P. R.; Borges, F. S.; Iarosz, K. C.; Caldas, I. L.; Baptista, M. S.; Viana, R. L.; Lameu, E. L.; Macau, E. E. N.; Batista, A. M.

2018-02-01

In this work, we study the dynamic range in a neural network modelled by cellular automaton. We consider deterministic and non-deterministic rules to simulate electrical and chemical synapses. Chemical synapses have an intrinsic time-delay and are susceptible to parameter variations guided by learning Hebbian rules of behaviour. The learning rules are related to neuroplasticity that describes change to the neural connections in the brain. Our results show that chemical synapses can abruptly enhance sensibility of the neural network, a manifestation that can become even more predominant if learning rules of evolution are applied to the chemical synapses.

Failing to learn from negative prediction errors: Obesity is associated with alterations in a fundamental neural learning mechanism.

Science.gov (United States)

Mathar, David; Neumann, Jane; Villringer, Arno; Horstmann, Annette

2017-10-01

Prediction errors (PEs) encode the difference between expected and actual action outcomes in the brain via dopaminergic modulation. Integration of these learning signals ensures efficient behavioral adaptation. Obesity has recently been linked to altered dopaminergic fronto-striatal circuits, thus implying impairments in cognitive domains that rely on its integrity. 28 obese and 30 lean human participants performed an implicit stimulus-response learning paradigm inside an fMRI scanner. Computational modeling and psycho-physiological interaction (PPI) analysis was utilized for assessing PE-related learning and associated functional connectivity. We show that human obesity is associated with insufficient incorporation of negative PEs into behavioral adaptation even in a non-food context, suggesting differences in a fundamental neural learning mechanism. Obese subjects were less efficient in using negative PEs to improve implicit learning performance, despite proper coding of PEs in striatum. We further observed lower functional coupling between ventral striatum and supplementary motor area in obese subjects subsequent to negative PEs. Importantly, strength of functional coupling predicted task performance and negative PE utilization. These findings show that obesity is linked to insufficient behavioral adaptation specifically in response to negative PEs, and to associated alterations in function and connectivity within the fronto-striatal system. Recognition of neural differences as a central characteristic of obesity hopefully paves the way to rethink established intervention strategies: Differential behavioral sensitivity to negative and positive PEs should be considered when designing intervention programs. Measures relying on penalization of unwanted behavior may prove less effective in obese subjects than alternative approaches. Copyright © 2017 Elsevier Ltd. All rights reserved.

Probabilistic Category Learning in Developmental Dyslexia: Evidence from Feedback and Paired-Associate Weather Prediction Tasks

Science.gov (United States)

Gabay, Yafit; Vakil, Eli; Schiff, Rachel; Holt, Lori L.

2015-01-01

Objective Developmental dyslexia is presumed to arise from specific phonological impairments. However, an emerging theoretical framework suggests that phonological impairments may be symptoms stemming from an underlying dysfunction of procedural learning. Method We tested procedural learning in adults with dyslexia (n=15) and matched-controls (n=15) using two versions of the Weather Prediction Task: Feedback (FB) and Paired-associate (PA). In the FB-based task, participants learned associations between cues and outcomes initially by guessing and subsequently through feedback indicating the correctness of response. In the PA-based learning task, participants viewed the cue and its associated outcome simultaneously without overt response or feedback. In both versions, participants trained across 150 trials. Learning was assessed in a subsequent test without presentation of the outcome, or corrective feedback. Results The Dyslexia group exhibited impaired learning compared with the Control group on both the FB and PA versions of the weather prediction task. Conclusions The results indicate that the ability to learn by feedback is not selectively impaired in dyslexia. Rather it seems that the probabilistic nature of the task, shared by the FB and PA versions of the weather prediction task, hampers learning in those with dyslexia. Results are discussed in light of procedural learning impairments among participants with dyslexia. PMID:25730732

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

Associative learning of odor with food- or blood-meal by Culex quinquefasciatus Say (Diptera: Culicidae)

Science.gov (United States)

Tomberlin, Jeffery K.; Rains, Glen C.; Allan, Sandy A.; Sanford, Michelle R.; Lewis, W. Joe

2006-11-01

The ability of many insects to learn has been documented. However, a limited number of studies examining associative learning in medically important arthropods has been published. Investigations into the associative learning capabilities of Culex quinquefasciatus Say were conducted by adapting methods commonly used in experiments involving Hymenoptera. Male and female mosquitoes were able to learn a conditioned stimulus that consisted of an odor not normally encountered in nature (synthetic strawberry or vanilla extracts) in association with an unconditioned stimulus consisting of either a sugar (males and females) or blood (females) meal. Such information could lead to a better understanding of the ability of mosquitoes to locate and select host and food resources in nature.

Rapid Association Learning in the Primate Prefrontal Cortex in the Absence of Behavioral Reversals

Science.gov (United States)

Cromer, Jason A.; Machon, Michelle; Miller, Earl K.

2011-01-01

The PFC plays a central role in our ability to learn arbitrary rules, such as "green means go." Previous experiments from our laboratory have used conditional association learning to show that slow, gradual changes in PFC neural activity mirror monkeys' slow acquisition of associations. These previous experiments required monkeys to repeatedly…

The Adult Learning Open University Determinants (ALOUD) study: Biological and psychological factors associated with learning performance in adult distance education

NARCIS (Netherlands)

Neroni, Joyce; Gijselaers, Jérôme; Kirschner, Paul A.; De Groot, Renate

2017-01-01

Learning is crucial for everyone. The association between biological (eg, sleep, nutrition) and psychological factors (eg, test anxiety, goal orientation) and learning performance has been well established for children, adolescents and college students in traditional education. Evidence for these

Association between classroom ventilation mode and learning outcome in Danish schools

DEFF Research Database (Denmark)

Toftum, Jørn; Kjeldsen, Birthe Uldahl; Wargocki, Pawel

2015-01-01

Associations between learning, ventilation mode, and other classroom characteristics were investigated with data from a Danish test scheme and two widespread cross-sectional studies examining air quality in Danish schools. An academic achievement indicator as a measure of the learning outcome...... concentrations and temperatures in 820 classrooms in 389 schools were available. In 56% and 66% of the classrooms included in the two studies, the measured CO2 concentration was higher than 1000ppm. The findings of this study add to the growing evidence that insufficient classroom ventilation have impacts...... on learning outcomes....

COMT Val158Met genotype is associated with reward learning: A replication study and meta-analysis

Science.gov (United States)

Corral-Frías, Nadia S.; Pizzagalli, Diego A.; Carré, Justin; Michalski, Lindsay J; Nikolova, Yuliya S.; Perlis, Roy H.; Fagerness, Jesen; Lee, Mary R.; Conley, Emily Drabant; Lancaster, Thomas M.; Haddad, Stephen; Wolf, Aaron; Smoller, Jordan W.; Hariri, Ahmad R.; Bogdan, Ryan

2016-01-01

Identifying mechanisms through which individual differences in reward learning emerge offers an opportunity to understand both a fundamental form of adaptive responding as well as etiological pathways through which aberrant reward learning may contribute to maladaptive behaviors and psychopathology. One candidate mechanism through which individual differences in reward learning may emerge is variability in dopaminergic reinforcement signaling. A common functional polymorphism within the catechol-O-methyl transferase gene (COMT; rs4680, Val158Met) has been linked to reward learning where homozygosity for the Met allele (associated with heightened prefrontal dopamine function and decreased dopamine synthesis in the midbrain) has been associated with relatively increased reward learning. Here, we used a probabilistic reward learning task to asses response bias, a behavioral form of reward learning, across 3 separate samples that were combined for analyses (age: 21.80 ± 3.95; n=392; 268 female; European-American, n=208). We replicate prior reports that COMT rs4680 Met allele homozygosity is associated with increased reward learning in European-American participants (β=0.20, t= 2.75, p< 0.01; ΔR2= 0.04). Moreover, a meta-analysis of 4 studies, including the current one, confirmed the association between COMT rs4680 genotype and reward learning (95% CI −0.11 to −0.03; z=3.2; p<0.01). These results suggest that variability in dopamine signaling associated with COMT rs4680 influences individual differences in reward which may potentially contribute to psychopathology characterized by reward dysfunction. PMID:27138112

Larger error signals in major depression are associated with better avoidance learning

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James F eCavanagh

2011-11-01

Full Text Available The medial prefrontal cortex (mPFC is particularly reactive to signals of error, punishment, and conflict in the service of behavioral adaptation and it is consistently implicated in the etiology of Major Depressive Disorder (MDD. This association makes conceptual sense, given that MDD has been associated with hyper-reactivity in neural systems associated with punishment processing. Yet in practice, depression-related variance in measures of mPFC functioning often fails to relate to performance. For example, neuroelectric reflections of mediofrontal error signals are often found to be larger in MDD, but a deficit in post-error performance suggests that these error signals are not being used to rapidly adapt behavior. Thus, it remains unknown if depression-related variance in error signals reflects a meaningful alteration in the use of error or punishment information. However, larger mediofrontal error signals have also been related to another behavioral tendency: increased accuracy in avoidance learning. The integrity of this error-avoidance system remains untested in MDD. In this study, EEG was recorded as 21 symptomatic, drug-free participants with current or past MDD and 24 control participants performed a probabilistic reinforcement learning task. Depressed participants had larger mPFC EEG responses to error feedback than controls. The direct relationship between error signal amplitudes and avoidance learning accuracy was replicated. Crucially, this relationship was stronger in depressed participants for high conflict lose-lose situations, demonstrating a selective alteration of avoidance learning. This investigation provided evidence that larger error signal amplitudes in depression are associated with increased avoidance learning, identifying a candidate mechanistic model for hypersensitivity to negative outcomes in depression.

Event timing in associative learning: from biochemical reaction dynamics to behavioural observations.

Directory of Open Access Journals (Sweden)

Ayse Yarali

Full Text Available Associative learning relies on event timing. Fruit flies for example, once trained with an odour that precedes electric shock, subsequently avoid this odour (punishment learning; if, on the other hand the odour follows the shock during training, it is approached later on (relief learning. During training, an odour-induced Ca(++ signal and a shock-induced dopaminergic signal converge in the Kenyon cells, synergistically activating a Ca(++-calmodulin-sensitive adenylate cyclase, which likely leads to the synaptic plasticity underlying the conditioned avoidance of the odour. In Aplysia, the effect of serotonin on the corresponding adenylate cyclase is bi-directionally modulated by Ca(++, depending on the relative timing of the two inputs. Using a computational approach, we quantitatively explore this biochemical property of the adenylate cyclase and show that it can generate the effect of event timing on associative learning. We overcome the shortage of behavioural data in Aplysia and biochemical data in Drosophila by combining findings from both systems.

Visual-motor association learning in undergraduate students as a function of the autism-spectrum quotient.

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Parkington, Karisa B; Clements, Rebecca J; Landry, Oriane; Chouinard, Philippe A

2015-10-01

We examined how performance on an associative learning task changes in a sample of undergraduate students as a function of their autism-spectrum quotient (AQ) score. The participants, without any prior knowledge of the Japanese language, learned to associate hiragana characters with button responses. In the novel condition, 50 participants learned visual-motor associations without any prior exposure to the stimuli's visual attributes. In the familiar condition, a different set of 50 participants completed a session in which they first became familiar with the stimuli's visual appearance prior to completing the visual-motor association learning task. Participants with higher AQ scores had a clear advantage in the novel condition; the amount of training required reaching learning criterion correlated negatively with AQ. In contrast, participants with lower AQ scores had a clear advantage in the familiar condition; the amount of training required to reach learning criterion correlated positively with AQ. An examination of how each of the AQ subscales correlated with these learning patterns revealed that abilities in visual discrimination-which is known to depend on the visual ventral-stream system-may have afforded an advantage in the novel condition for the participants with the higher AQ scores, whereas abilities in attention switching-which are known to require mechanisms in the prefrontal cortex-may have afforded an advantage in the familiar condition for the participants with the lower AQ scores.

Quantum-Inspired Multidirectional Associative Memory With a Self-Convergent Iterative Learning.

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Masuyama, Naoki; Loo, Chu Kiong; Seera, Manjeevan; Kubota, Naoyuki

2018-04-01

Quantum-inspired computing is an emerging research area, which has significantly improved the capabilities of conventional algorithms. In general, quantum-inspired hopfield associative memory (QHAM) has demonstrated quantum information processing in neural structures. This has resulted in an exponential increase in storage capacity while explaining the extensive memory, and it has the potential to illustrate the dynamics of neurons in the human brain when viewed from quantum mechanics perspective although the application of QHAM is limited as an autoassociation. We introduce a quantum-inspired multidirectional associative memory (QMAM) with a one-shot learning model, and QMAM with a self-convergent iterative learning model (IQMAM) based on QHAM in this paper. The self-convergent iterative learning enables the network to progressively develop a resonance state, from inputs to outputs. The simulation experiments demonstrate the advantages of QMAM and IQMAM, especially the stability to recall reliability.

SPAN: spike pattern association neuron for learning spatio-temporal sequences

OpenAIRE

Mohemmed, A; Schliebs, S; Matsuda, S; Kasabov, N

2012-01-01

Spiking Neural Networks (SNN) were shown to be suitable tools for the processing of spatio-temporal information. However, due to their inherent complexity, the formulation of efficient supervised learning algorithms for SNN is difficult and remains an important problem in the research area. This article presents SPAN — a spiking neuron that is able to learn associations of arbitrary spike trains in a supervised fashion allowing the processing of spatio-temporal information encoded in the prec...

Imitation, empathy, and mirror neurons.

Science.gov (United States)

Iacoboni, Marco

2009-01-01

There is a convergence between cognitive models of imitation, constructs derived from social psychology studies on mimicry and empathy, and recent empirical findings from the neurosciences. The ideomotor framework of human actions assumes a common representational format for action and perception that facilitates imitation. Furthermore, the associative sequence learning model of imitation proposes that experience-based Hebbian learning forms links between sensory processing of the actions of others and motor plans. Social psychology studies have demonstrated that imitation and mimicry are pervasive, automatic, and facilitate empathy. Neuroscience investigations have demonstrated physiological mechanisms of mirroring at single-cell and neural-system levels that support the cognitive and social psychology constructs. Why were these neural mechanisms selected, and what is their adaptive advantage? Neural mirroring solves the "problem of other minds" (how we can access and understand the minds of others) and makes intersubjectivity possible, thus facilitating social behavior.

Adaptive memory: animacy effects persist in paired-associate learning.

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VanArsdall, Joshua E; Nairne, James S; Pandeirada, Josefa N S; Cogdill, Mindi

2015-01-01

Recent evidence suggests that animate stimuli are remembered better than matched inanimate stimuli. Two experiments tested whether this animacy effect persists in paired-associate learning of foreign words. Experiment 1 randomly paired Swahili words with matched animate and inanimate English words. Participants were told simply to learn the English "translations" for a later test. Replicating earlier findings using free recall, a strong animacy advantage was found in this cued-recall task. Concerned that the effect might be due to enhanced accessibility of the individual responses (e.g., animates represent a more accessible category), Experiment 2 selected animate and inanimate English words from two more constrained categories (four-legged animals and furniture). Once again, an advantage was found for pairs using animate targets. These results argue against organisational accounts of the animacy effect and potentially have implications for foreign language vocabulary learning.

Motivated strategies for learning and their association with academic ...

African Journals Online (AJOL)

Background. Most instruments, including the well-known Motivated Strategies for Learning Questionnaire (MSLQ), have been designed in western homogeneous settings. Use of the MSLQ in health professions education is limited. Objective. To assess the MSLQ and its association with the academic performance of a ...

Effects of Learning Experience on Forgetting Rates of Item and Associative Memories

Science.gov (United States)

Yang, Jiongjiong; Zhan, Lexia; Wang, Yingying; Du, Xiaoya; Zhou, Wenxi; Ning, Xueling; Sun, Qing; Moscovitch, Morris

2016-01-01

Are associative memories forgotten more quickly than item memories, and does the level of original learning differentially influence forgetting rates? In this study, we addressed these questions by having participants learn single words and word pairs once (Experiment 1), three times (Experiment 2), and six times (Experiment 3) in a massed…

Associations of learning style with cultural values and demographics in nursing students in Iran and Malaysia.

Science.gov (United States)

Abdollahimohammad, Abdolghani; Ja'afar, Rogayah

2015-01-01

The goal of the current study was to identify associations between the learning style of nursing students and their cultural values and demographic characteristics. A non-probability purposive sampling method was used to gather data from two populations. All 156 participants were female, Muslim, and full-time degree students. Data were collected from April to June 2010 using two reliable and validated questionnaires: the Learning Style Scales and the Values Survey Module 2008 (VSM 08). A simple linear regression was run for each predictor before conducting multiple linear regression analysis. The forward selection method was used for variable selection. P-values ≤0.05 and ≤0.1 were considered to indicate significance and marginal significance, respectively. Moreover, multi-group confirmatory factor analysis was performed to determine the invariance of the Farsi and English versions of the VSM 08. The perceptive learning style was found to have a significant negative relationship with the power distance and monumentalism indices of the VSM 08. Moreover, a significant negative association was observed between the solitary learning style and the power distance index. However, no significant association was found between the analytic, competitive, and imaginative learning styles and cultural values (P>0.05). Likewise, no significant associations were observed between learning style, including the perceptive, solitary, analytic, competitive, and imaginative learning styles, and year of study or age (P>0.05). Students who reported low values on the power distance and monumentalism indices are more likely to prefer perceptive and solitary learning styles. Within each group of students in our study sample from the same school the year of study and age did not show any significant associations with learning style.

Cocaine self-administration abolishes associative neural encoding in the nucleus accumbens necessary for higher-order learning.

Science.gov (United States)

Saddoris, Michael P; Carelli, Regina M

2014-01-15

Cocaine use is often associated with diminished cognitive function, persisting even after abstinence from the drug. Likely targets for these changes are the core and shell of the nucleus accumbens (NAc), which are critical for mediating the rewarding aspects of drugs of abuse as well as supporting associative learning. To understand this deficit, we recorded neural activity in the NAc of rats with a history of cocaine self-administration or control subjects while they learned Pavlovian first- and second-order associations. Rats were trained for 2 weeks to self-administer intravenous cocaine or water. Later, rats learned a first-order Pavlovian discrimination where a conditioned stimulus (CS)+ predicted food, and a control (CS-) did not. Rats then learned a second-order association where, absent any food reinforcement, a novel cued (SOC+) predicted the CS+ and another (SOC-) predicted the CS-. Electrophysiological recordings were taken during performance of these tasks in the NAc core and shell. Both control subjects and cocaine-experienced rats learned the first-order association, but only control subjects learned the second-order association. Neural recordings indicated that core and shell neurons encoded task-relevant information that correlated with behavioral performance, whereas this type of encoding was abolished in cocaine-experienced rats. The NAc core and shell perform complementary roles in supporting normal associative learning, functions that are impaired after cocaine experience. This impoverished encoding of motivational behavior, even after abstinence from the drug, might provide a key mechanism to understand why addiction remains a chronically relapsing disorder despite repeated attempts at sobriety. Copyright © 2014 Society of Biological Psychiatry. Published by Elsevier Inc. All rights reserved.

Visual paired-associate learning: in search of material-specific effects in adult patients who have undergone temporal lobectomy.

Science.gov (United States)

Smith, Mary Lou; Bigel, Marla; Miller, Laurie A

2011-02-01

The mesial temporal lobes are important for learning arbitrary associations. It has previously been demonstrated that left mesial temporal structures are involved in learning word pairs, but it is not yet known whether comparable lesions in the right temporal lobe impair visually mediated associative learning. Patients who had undergone left (n=16) or right (n=18) temporal lobectomy for relief of intractable epilepsy and healthy controls (n=13) were administered two paired-associate learning tasks assessing their learning and memory of pairs of abstract designs or pairs of symbols in unique locations. Both patient groups had deficits in learning the designs, but only the right temporal group was impaired in recognition. For the symbol location task, differences were not found in learning, but again a recognition deficit was found for the right temporal group. The findings implicate the mesial temporal structures in relational learning. They support a material-specific effect for recognition but not for learning and recall of arbitrary visual and visual-spatial associative information. Copyright © 2010 Elsevier Inc. All rights reserved.

Ising formulation of associative memory models and quantum annealing recall

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Santra, Siddhartha; Shehab, Omar; Balu, Radhakrishnan

2017-12-01

Associative memory models, in theoretical neuro- and computer sciences, can generally store at most a linear number of memories. Recalling memories in these models can be understood as retrieval of the energy minimizing configuration of classical Ising spins, closest in Hamming distance to an imperfect input memory, where the energy landscape is determined by the set of stored memories. We present an Ising formulation for associative memory models and consider the problem of memory recall using quantum annealing. We show that allowing for input-dependent energy landscapes allows storage of up to an exponential number of memories (in terms of the number of neurons). Further, we show how quantum annealing may naturally be used for recall tasks in such input-dependent energy landscapes, although the recall time may increase with the number of stored memories. Theoretically, we obtain the radius of attractor basins R (N ) and the capacity C (N ) of such a scheme and their tradeoffs. Our calculations establish that for randomly chosen memories the capacity of our model using the Hebbian learning rule as a function of problem size can be expressed as C (N ) =O (eC1N) , C1≥0 , and succeeds on randomly chosen memory sets with a probability of (1 -e-C2N) , C2≥0 with C1+C2=(0.5-f ) 2/(1 -f ) , where f =R (N )/N , 0 ≤f ≤0.5 , is the radius of attraction in terms of the Hamming distance of an input probe from a stored memory as a fraction of the problem size. We demonstrate the application of this scheme on a programmable quantum annealing device, the D-wave processor.

C. elegans positive butanone learning, short-term, and long-term associative memory assays.

Science.gov (United States)

Kauffman, Amanda; Parsons, Lance; Stein, Geneva; Wills, Airon; Kaletsky, Rachel; Murphy, Coleen

2011-03-11

The memory of experiences and learned information is critical for organisms to make choices that aid their survival. C. elegans navigates its environment through neuron-specific detection of food and chemical odors, and can associate nutritive states with chemical odors, temperature, and the pathogenicity of a food source. Here, we describe assays of C. elegans associative learning and short- and long-term associative memory. We modified an aversive olfactory learning paradigm to instead produce a positive response; the assay involves starving ~400 worms, then feeding the worms in the presence of the AWC neuron-sensed volatile chemoattractant butanone at a concentration that elicits a low chemotactic index (similar to Toroyama et al.). A standard population chemotaxis assay1 tests the worms' attraction to the odorant immediately or minutes to hours after conditioning. After conditioning, wild-type animals' chemotaxis to butanone increases ~0.6 Chemotaxis Index units, its "Learning Index". Associative learning is dependent on the presence of both food and butanone during training. Pairing food and butanone for a single conditioning period ("massed training") produces short-term associative memory that lasts ~2 hours. Multiple conditioning periods with rest periods between ("spaced training") yields long-term associative memory (long-term memory across species. Our protocol also includes image analysis methods for quick and accurate determination of chemotaxis indices. High-contrast images of animals on chemotaxis assay plates are captured and analyzed by worm counting software in MatLab. The software corrects for uneven background using a morphological tophat transformation. Otsu's method is then used to determine a threshold to separate worms from the background. Very small particles are removed automatically and larger non-worm regions (plate edges or agar punches) are removed by manual selection. The software then estimates the size of single worm by ignoring

Associations among smoking, anhedonia, and reward learning in depression.

Science.gov (United States)

Liverant, Gabrielle I; Sloan, Denise M; Pizzagalli, Diego A; Harte, Christopher B; Kamholz, Barbara W; Rosebrock, Laina E; Cohen, Andrew L; Fava, Maurizio; Kaplan, Gary B

2014-09-01

Depression and cigarette smoking co-occur at high rates. However, the etiological mechanisms that contribute to this relationship remain unclear. Anhedonia and associated impairments in reward learning are key features of depression, which also have been linked to the onset and maintenance of cigarette smoking. However, few studies have investigated differences in anhedonia and reward learning among depressed smokers and depressed nonsmokers. The goal of this study was to examine putative differences in anhedonia and reward learning in depressed smokers (n=36) and depressed nonsmokers (n=44). To this end, participants completed self-report measures of anhedonia and behavioral activation (BAS reward responsiveness scores) and as well as a probabilistic reward task rooted in signal detection theory, which measures reward learning (Pizzagalli, Jahn, & O'Shea, 2005). When considering self-report measures, depressed smokers reported higher trait anhedonia and reduced BAS reward responsiveness scores compared to depressed nonsmokers. In contrast to self-report measures, nicotine-satiated depressed smokers demonstrated greater acquisition of reward-based learning compared to depressed nonsmokers as indexed by the probabilistic reward task. Findings may point to a potential mechanism underlying the frequent co-occurrence of smoking and depression. These results highlight the importance of continued investigation of the role of anhedonia and reward system functioning in the co-occurrence of depression and nicotine abuse. Results also may support the use of treatments targeting reward learning (e.g., behavioral activation) to enhance smoking cessation among individuals with depression. Copyright © 2014. Published by Elsevier Ltd.

Hippocampal synapsin I, growth-associated protein-43, and microtubule-associated protein-2 immunoreactivity in learned helplessness rats and antidepressant-treated rats.

Science.gov (United States)

Iwata, M; Shirayama, Y; Ishida, H; Kawahara, R

2006-09-01

Learned helplessness rats are thought to be an animal model of depression. To study the role of synapse plasticity in depression, we examined the effects of learned helplessness and antidepressant treatments on synapsin I (a marker of presynaptic terminals), growth-associated protein-43 (GAP-43; a marker of growth cones), and microtubule-associated protein-2 (MAP-2; a marker of dendrites) in the hippocampus by immunolabeling. (1) Learned helplessness rats showed significant increases in the expression of synapsin I two days after the attainment of learned helplessness, and significant decreases in the protein expression eight days after the achievement of learned helplessness. Subchronic treatment of naïve rats with imipramine or fluvoxamine significantly decreased the expression of synapsin I. (2) Learned helplessness increased the expression of GAP-43 two days and eight days after learned helplessness training. Subchronic treatment of naïve rats with fluvoxamine but not imipramine showed a tendency to decrease the expression of synapsin I. (3) Learned helplessness rats showed increased expression of MAP-2 eight days after the attainment of learned helplessness. Naïve rats subchronically treated with imipramine showed a tendency toward increased expression of MAP-2, but those treated with fluvoxamine did not. These results indicate that the neuroplasticity-related proteins synapsin I, GAP-43, and MAP-2 may play a role in the pathophysiology of depression and the mechanisms of antidepressants.

Emergence of Functional Specificity in Balanced Networks with Synaptic Plasticity.

Directory of Open Access Journals (Sweden)

Sadra Sadeh

2015-06-01

Full Text Available In rodent visual cortex, synaptic connections between orientation-selective neurons are unspecific at the time of eye opening, and become to some degree functionally specific only later during development. An explanation for this two-stage process was proposed in terms of Hebbian plasticity based on visual experience that would eventually enhance connections between neurons with similar response features. For this to work, however, two conditions must be satisfied: First, orientation selective neuronal responses must exist before specific recurrent synaptic connections can be established. Second, Hebbian learning must be compatible with the recurrent network dynamics contributing to orientation selectivity, and the resulting specific connectivity must remain stable for unspecific background activity. Previous studies have mainly focused on very simple models, where the receptive fields of neurons were essentially determined by feedforward mechanisms, and where the recurrent network was small, lacking the complex recurrent dynamics of large-scale networks of excitatory and inhibitory neurons. Here we studied the emergence of functionally specific connectivity in large-scale recurrent networks with synaptic plasticity. Our results show that balanced random networks, which already exhibit highly selective responses at eye opening, can develop feature-specific connectivity if appropriate rules of synaptic plasticity are invoked within and between excitatory and inhibitory populations. If these conditions are met, the initial orientation selectivity guides the process of Hebbian learning and, as a result, functionally specific and a surplus of bidirectional connections emerge. Our results thus demonstrate the cooperation of synaptic plasticity and recurrent dynamics in large-scale functional networks with realistic receptive fields, highlight the role of inhibition as a critical element in this process, and paves the road for further computational

Learning of arbitrary association between visual and auditory novel stimuli in adults: the "bond effect" of haptic exploration.

Directory of Open Access Journals (Sweden)

Benjamin Fredembach

Full Text Available BACKGROUND: It is well-known that human beings are able to associate stimuli (novel or not perceived in their environment. For example, this ability is used by children in reading acquisition when arbitrary associations between visual and auditory stimuli must be learned. The studies tend to consider it as an "implicit" process triggered by the learning of letter/sound correspondences. The study described in this paper examined whether the addition of the visuo-haptic exploration would help adults to learn more effectively the arbitrary association between visual and auditory novel stimuli. METHODOLOGY/PRINCIPAL FINDINGS: Adults were asked to learn 15 new arbitrary associations between visual stimuli and their corresponding sounds using two learning methods which differed according to the perceptual modalities involved in the exploration of the visual stimuli. Adults used their visual modality in the "classic" learning method and both their visual and haptic modalities in the "multisensory" learning one. After both learning methods, participants showed a similar above-chance ability to recognize the visual and auditory stimuli and the audio-visual associations. However, the ability to recognize the visual-auditory associations was better after the multisensory method than after the classic one. CONCLUSION/SIGNIFICANCE: This study revealed that adults learned more efficiently the arbitrary association between visual and auditory novel stimuli when the visual stimuli were explored with both vision and touch. The results are discussed from the perspective of how they relate to the functional differences of the manual haptic modality and the hypothesis of a "haptic bond" between visual and auditory stimuli.

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

Changes of motor-cortical oscillations associated with motor learning.

Science.gov (United States)

Pollok, B; Latz, D; Krause, V; Butz, M; Schnitzler, A

2014-09-05

Motor learning results from practice but also between practice sessions. After skill acquisition early consolidation results in less interference with other motor tasks and even improved performance of the newly learned skill. A specific significance of the primary motor cortex (M1) for early consolidation has been suggested. Since synchronized oscillatory activity is assumed to facilitate neuronal plasticity, we here investigate alterations of motor-cortical oscillations by means of event-related desynchronization (ERD) at alpha (8-12 Hz) and beta (13-30 Hz) frequencies in healthy humans. Neuromagnetic activity was recorded using a 306-channel whole-head magnetoencephalography (MEG) system. ERD was investigated in 15 subjects during training on a serial reaction time task and 10 min after initial training. The data were compared with performance during a randomly varying sequence serving as control condition. The data reveal a stepwise decline of alpha-band ERD associated with faster reaction times replicating previous findings. The amount of beta-band suppression was significantly correlated with reduction of reaction times. While changes of alpha power have been related to lower cognitive control after initial skill acquisition, the present data suggest that the amount of beta suppression represents a neurophysiological marker of early cortical reorganization associated with motor learning. Copyright © 2014 IBRO. Published by Elsevier Ltd. All rights reserved.

Elemental representation and configural mappings: combining elemental and configural theories of associative learning.

Science.gov (United States)

McLaren, I P L; Forrest, C L; McLaren, R P

2012-09-01

In this article, we present our first attempt at combining an elemental theory designed to model representation development in an associative system (based on McLaren, Kaye, & Mackintosh, 1989) with a configural theory that models associative learning and memory (McLaren, 1993). After considering the possible advantages of such a combination (and some possible pitfalls), we offer a hybrid model that allows both components to produce the phenomena that they are capable of without introducing unwanted interactions. We then successfully apply the model to a range of phenomena, including latent inhibition, perceptual learning, the Espinet effect, and first- and second-order retrospective revaluation. In some cases, we present new data for comparison with our model's predictions. In all cases, the model replicates the pattern observed in our experimental results. We conclude that this line of development is a promising one for arriving at general theories of associative learning and memory.

Associations of learning style with cultural values and demographics in nursing students in Iran and Malaysia

Directory of Open Access Journals (Sweden)

Abdolghani Abdollahimohammad

2015-08-01

Full Text Available Purpose: The goal of the current study was to identify associations between the learning style of nursing students and their cultural values and demographic characteristics. Methods: A non-probability purposive sampling method was used to gather data from two populations. All 156 participants were female, Muslim, and full-time degree students. Data were collected from April to June 2010 using two reliable and validated questionnaires: the Learning Style Scales and the Values Survey Module 2008 (VSM 08. A simple linear regression was run for each predictor before conducting multiple linear regression analysis. The forward selection method was used for variable selection. P-values ≤0.05 and ≤0.1 were considered to indicate significance and marginal significance, respectively. Moreover, multi-group confirmatory factor analysis was performed to determine the invariance of the Farsi and English versions of the VSM 08. Results: The perceptive learning style was found to have a significant negative relationship with the power distance and monumentalism indices of the VSM 08. Moreover, a significant negative association was observed between the solitary learning style and the power distance index. However, no significant association was found between the analytic, competitive, and imaginative learning styles and cultural values (P>0.05. Likewise, no significant associations were observed between learning style, including the perceptive, solitary, analytic, competitive, and imaginative learning styles, and year of study or age (P>0.05. Conclusion: Students who reported low values on the power distance and monumentalism indices are more likely to prefer perceptive and solitary learning styles. Within each group of students in our study sample from the same school the year of study and age did not show any significant associations with learning style.

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....

Functional contributions and interactions between the human hippocampus and subregions of the striatum during arbitrary associative learning and memory

Science.gov (United States)

Mattfeld, Aaron T.; Stark, Craig E. L.

2015-01-01

The hippocampus and striatum are thought to have different functional roles in learning and memory. It is unknown under what experimental conditions their contributions are dissimilar or converge, and the extent to which they interact over the course of learning. In order to evaluate both the functional contributions of as well as the interactions between the human hippocampus and striatum, the present study used high-resolution functional magnetic resonance imaging (fMRI) and variations of a conditional visuomotor associative learning task that either taxed arbitrary associative learning (Experiment 1) or stimulus-response learning (Experiment 2). In the first experiment we observed changes in activity in the hippocampus and anterior caudate that reflect differences between the two regions consistent with distinct computational principles. In the second experiment we observed activity in the putamen that reflected content specific representations during the learning of arbitrary conditional visuomotor associations. In both experiments the hippocampus and ventral striatum demonstrated dynamic functional coupling during the learning of new arbitrary associations, but not during retrieval of well-learned arbitrary associations using control variants of the tasks that did not preferentially tax one system versus the other. These findings suggest that both the hippocampus and subregions of the dorsal striatum contribute uniquely to the learning of arbitrary associations while the hippocampus and ventral striatum interact over the course of learning. PMID:25560298

Associative learning changes cross-modal representations in the gustatory cortex.

Science.gov (United States)

Vincis, Roberto; Fontanini, Alfredo

2016-08-30

A growing body of literature has demonstrated that primary sensory cortices are not exclusively unimodal, but can respond to stimuli of different sensory modalities. However, several questions concerning the neural representation of cross-modal stimuli remain open. Indeed, it is poorly understood if cross-modal stimuli evoke unique or overlapping representations in a primary sensory cortex and whether learning can modulate these representations. Here we recorded single unit responses to auditory, visual, somatosensory, and olfactory stimuli in the gustatory cortex (GC) of alert rats before and after associative learning. We found that, in untrained rats, the majority of GC neurons were modulated by a single modality. Upon learning, both prevalence of cross-modal responsive neurons and their breadth of tuning increased, leading to a greater overlap of representations. Altogether, our results show that the gustatory cortex represents cross-modal stimuli according to their sensory identity, and that learning changes the overlap of cross-modal representations.

Associative visual learning by tethered bees in a controlled visual environment.

Science.gov (United States)

Buatois, Alexis; Pichot, Cécile; Schultheiss, Patrick; Sandoz, Jean-Christophe; Lazzari, Claudio R; Chittka, Lars; Avarguès-Weber, Aurore; Giurfa, Martin

2017-10-10

Free-flying honeybees exhibit remarkable cognitive capacities but the neural underpinnings of these capacities cannot be studied in flying insects. Conversely, immobilized bees are accessible to neurobiological investigation but display poor visual learning. To overcome this limitation, we aimed at establishing a controlled visual environment in which tethered bees walking on a spherical treadmill learn to discriminate visual stimuli video projected in front of them. Freely flying bees trained to walk into a miniature Y-maze displaying these stimuli in a dark environment learned the visual discrimination efficiently when one of them (CS+) was paired with sucrose and the other with quinine solution (CS-). Adapting this discrimination to the treadmill paradigm with a tethered, walking bee was successful as bees exhibited robust discrimination and preferred the CS+ to the CS- after training. As learning was better in the maze, movement freedom, active vision and behavioral context might be important for visual learning. The nature of the punishment associated with the CS- also affects learning as quinine and distilled water enhanced the proportion of learners. Thus, visual learning is amenable to a controlled environment in which tethered bees learn visual stimuli, a result that is important for future neurobiological studies in virtual reality.

Factors associated with student learning processes in primary health care units: a questionnaire study.

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Bos, Elisabeth; Alinaghizadeh, Hassan; Saarikoski, Mikko; Kaila, Päivi

2015-01-01

Clinical placement plays a key role in education intended to develop nursing and caregiving skills. Studies of nursing students' clinical learning experiences show that these dimensions affect learning processes: (i) supervisory relationship, (ii) pedagogical atmosphere, (iii) management leadership style, (iv) premises of nursing care on the ward, and (v) nursing teachers' roles. Few empirical studies address the probability of an association between these dimensions and factors such as student (a) motivation, (b) satisfaction with clinical placement, and (c) experiences with professional role models. The study aimed to investigate factors associated with the five dimensions in clinical learning environments within primary health care units. The Swedish version of Clinical Learning Environment, Supervision and Teacher, a validated evaluation scale, was administered to 356 graduating nursing students after four or five weeks clinical placement in primary health care units. Response rate was 84%. Multivariate analysis of variance is determined if the five dimensions are associated with factors a, b, and c above. The analysis revealed a statistically significant association with the five dimensions and two factors: students' motivation and experiences with professional role models. The satisfaction factor had a statistically significant association (effect size was high) with all dimensions; this clearly indicates that students experienced satisfaction. These questionnaire results show that a good clinical learning experience constitutes a complex whole (totality) that involves several interacting factors. Supervisory relationship and pedagogical atmosphere particularly influenced students' satisfaction and motivation. These results provide valuable decision-support material for clinical education planning, implementation, and management. Copyright © 2014 Elsevier Ltd. All rights reserved.

Second Language Idiom Learning in a Paired-Associate Paradigm: Effects of Direction of Learning, Direction of Testing, Idiom Imageability, and Idiom Transparency

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Steinel, Margarita P.; Hulstijn, Jan H.; Steinel, Wolfgang

2007-01-01

In a paired-associate learning (PAL) task, Dutch university students (n = 129) learned 20 English second language (L2) idioms either receptively or productively (i.e., L2-first language [L1] or L1-L2) and were tested in two directions (i.e., recognition or production) immediately after learning and 3 weeks later. Receptive and productive…

Sharp wave/ripple network oscillations and learning-associated hippocampal maps.

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Csicsvari, Jozsef; Dupret, David

2014-02-05

Sharp wave/ripple (SWR, 150-250 Hz) hippocampal events have long been postulated to be involved in memory consolidation. However, more recent work has investigated SWRs that occur during active waking behaviour: findings that suggest that SWRs may also play a role in cell assembly strengthening or spatial working memory. Do such theories of SWR function apply to animal learning? This review discusses how general theories linking SWRs to memory-related function may explain circuit mechanisms related to rodent spatial learning and to the associated stabilization of new cognitive maps.

Individual personality differences in goats predict their performance in visual learning and non-associative cognitive tasks.

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Nawroth, Christian; Prentice, Pamela M; McElligott, Alan G

2017-01-01

Variation in common personality traits, such as boldness or exploration, is often associated with risk-reward trade-offs and behavioural flexibility. To date, only a few studies have examined the effects of consistent behavioural traits on both learning and cognition. We investigated whether certain personality traits ('exploration' and 'sociability') of individuals were related to cognitive performance, learning flexibility and learning style in a social ungulate species, the goat (Capra hircus). We also investigated whether a preference for feature cues rather than impaired learning abilities can explain performance variation in a visual discrimination task. We found that personality scores were consistent across time and context. Less explorative goats performed better in a non-associative cognitive task, in which subjects had to follow the trajectory of a hidden object (i.e. testing their ability for object permanence). We also found that less sociable subjects performed better compared to more sociable goats in a visual discrimination task. Good visual learning performance was associated with a preference for feature cues, indicating personality-dependent learning strategies in goats. Our results suggest that personality traits predict the outcome in visual discrimination and non-associative cognitive tasks in goats and that impaired performance in a visual discrimination tasks does not necessarily imply impaired learning capacities, but rather can be explained by a varying preference for feature cues. Copyright © 2016 Elsevier B.V. All rights reserved.

Learned reward association improves visual working memory.

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Gong, Mengyuan; Li, Sheng

2014-04-01

Statistical regularities in the natural environment play a central role in adaptive behavior. Among other regularities, reward association is potentially the most prominent factor that influences our daily life. Recent studies have suggested that pre-established reward association yields strong influence on the spatial allocation of attention. Here we show that reward association can also improve visual working memory (VWM) performance when the reward-associated feature is task-irrelevant. We established the reward association during a visual search training session, and investigated the representation of reward-associated features in VWM by the application of a change detection task before and after the training. The results showed that the improvement in VWM was significantly greater for items in the color associated with high reward than for those in low reward-associated or nonrewarded colors. In particular, the results from control experiments demonstrate that the observed reward effect in VWM could not be sufficiently accounted for by attentional capture toward the high reward-associated item. This was further confirmed when the effect of attentional capture was minimized by presenting the items in the sample and test displays of the change detection task with the same color. The results showed significantly larger improvement in VWM performance when the items in a display were in the high reward-associated color than those in the low reward-associated or nonrewarded colors. Our findings suggest that, apart from inducing space-based attentional capture, the learned reward association could also facilitate the perceptual representation of high reward-associated items through feature-based attentional modulation.

BDNF Val66Met Polymorphism Influences Visuomotor Associative Learning and the Sensitivity to Action Observation

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Taschereau-Dumouchel, Vincent; Hétu, Sébastien; Michon, Pierre-Emmanuel; Vachon-Presseau, Etienne; Massicotte, Elsa; De Beaumont, Louis; Fecteau, Shirley; Poirier, Judes; Mercier, Catherine; Chagnon, Yvon C.; Jackson, Philip L.

2016-01-01

Motor representations in the human mirror neuron system are tuned to respond to specific observed actions. This ability is widely believed to be influenced by genetic factors, but no study has reported a genetic variant affecting this system so far. One possibility is that genetic variants might interact with visuomotor associative learning to configure the system to respond to novel observed actions. In this perspective, we conducted a candidate gene study on the Brain-derived neurotrophic factor (BDNF) Val66Met polymorphism, a genetic variant linked to motor learning in regions of the mirror neuron system, and tested the effect of this polymorphism on motor facilitation and visuomotor associative learning. In a single-pulse TMS study carried on 16 Met (Val/Met and Met/Met) and 16 Val/Val participants selected from a large pool of healthy volunteers, Met participants showed significantly less muscle-specific corticospinal sensitivity during action observation, as well as reduced visuomotor associative learning, compared to Val homozygotes. These results are the first evidence of a genetic variant tuning sensitivity to action observation and bring to light the importance of considering the intricate relation between genetics and associative learning in order to further understand the origin and function of the human mirror neuron system. PMID:27703276

Chronological age and its impact on associative learning proficiency and brain structure in middle adulthood.

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Diwadkar, Vaibhav A; Bellani, Marcella; Ahmed, Rizwan; Dusi, Nicola; Rambaldelli, Gianluca; Perlini, Cinzia; Marinelli, Veronica; Ramaseshan, Karthik; Ruggeri, Mirella; Bambilla, Paolo

2016-01-15

The rate of biological change in middle-adulthood is relatively under-studied. Here, we used behavioral testing in conjunction with structural magnetic resonance imaging to examine the effects of chronological age on associative learning proficiency and on brain regions that previous functional MRI studies have closely related to the domain of associative learning. Participants (n=66) completed a previously established associative learning paradigm, and consented to be scanned using structural magnetic resonance imaging. Age-related effects were investigated both across sub-groups in the sample (younger vs. older) and across the entire sample (using regression approaches). Chronological age had substantial effects on learning proficiency (independent of IQ and Education Level), with older adults showing a decrement compared to younger adults. In addition, decreases in estimated gray matter volume were observed in multiple brain regions including the hippocampus and the dorsal prefrontal cortex, both of which are strongly implicated in associative learning. The results suggest that middle adulthood may be a more dynamic period of life-span change than previously believed. The conjunctive application of narrowly focused tasks, with conjointly acquired structural MRI data may allow us to enrich the search for, and the interpretation of, age-related changes in cross-sectional samples. Copyright © 2015 Elsevier B.V. All rights reserved.

Predicting performance on the Raven’s Matrices: The roles of associative learning and retrieval efficiency

OpenAIRE

Lilienthal, Lindsey; Tamez, Elaine; Myerson, Joel; Hale, Sandra

2013-01-01

Previous studies have shown that performance on Williams and Pearlberg’s (2006) complex associative learning task is a good predictor of fluid intelligence. This task is similar in structure to that used in studying the fan effect (Anderson, 1974), as both tasks involve forming multiple associations and require retrieval in the face of interference. The purpose of the present study was to investigate the relations among complex associative learning, working memory, and fluid in...

Hippocampal Structure Predicts Statistical Learning and Associative Inference Abilities during Development.

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Schlichting, Margaret L; Guarino, Katharine F; Schapiro, Anna C; Turk-Browne, Nicholas B; Preston, Alison R

2017-01-01

Despite the importance of learning and remembering across the lifespan, little is known about how the episodic memory system develops to support the extraction of associative structure from the environment. Here, we relate individual differences in volumes along the hippocampal long axis to performance on statistical learning and associative inference tasks-both of which require encoding associations that span multiple episodes-in a developmental sample ranging from ages 6 to 30 years. Relating age to volume, we found dissociable patterns across the hippocampal long axis, with opposite nonlinear volume changes in the head and body. These structural differences were paralleled by performance gains across the age range on both tasks, suggesting improvements in the cross-episode binding ability from childhood to adulthood. Controlling for age, we also found that smaller hippocampal heads were associated with superior behavioral performance on both tasks, consistent with this region's hypothesized role in forming generalized codes spanning events. Collectively, these results highlight the importance of examining hippocampal development as a function of position along the hippocampal axis and suggest that the hippocampal head is particularly important in encoding associative structure across development.

Deciphering mirror neurons: rational decision versus associative learning.

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Khalil, Elias L

2014-04-01

The rational-decision approach is superior to the associative-learning approach of Cook et al. at explaining why mirror neurons fire or do not fire - even when the stimulus is the same. The rational-decision approach is superior because it starts with the analysis of the intention of the organism, that is, with the identification of the specific objective or goal that the organism is trying to maximize.

A Neurocomputational Approach to Trained and Transitive Relations in Equivalence Classes

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Ángel E. Tovar

2017-10-01

Full Text Available A stimulus class can be composed of perceptually different but functionally equivalent stimuli. The relations between the stimuli that are grouped in a class can be learned or derived from other stimulus relations. If stimulus A is equivalent to B, and B is equivalent to C, then the equivalence between A and C can be derived without explicit training. In this work we propose, with a neurocomputational model, a basic learning mechanism for the formation of equivalence. We also describe how the relatedness between the members of an equivalence class is developed for both trained and derived stimulus relations. Three classic studies on stimulus equivalence are simulated covering typical and atypical populations as well as nodal distance effects. This model shows a mechanism by which certain stimulus associations are selectively strengthened even when they are not co-presented in the environment. This model links the field of equivalence classes to accounts of Hebbian learning and categorization, and points to the pertinence of modeling stimulus equivalence to explore the effect of variations in training protocols.

Nicotine disrupts safety learning by enhancing fear associated with a safety cue via the dorsal hippocampus.

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Connor, David A; Kutlu, Munir G; Gould, Thomas J

2017-07-01

Learned safety, a learning process in which a cue becomes associated with the absence of threat, is disrupted in individuals with post-traumatic stress disorder (PTSD). A bi-directional relationship exists between smoking and PTSD and one potential explanation is that nicotine-associated changes in cognition facilitate PTSD emotional dysregulation by disrupting safety associations. Therefore, we investigated whether nicotine would disrupt learned safety by enhancing fear associated with a safety cue. In the present study, C57BL/6 mice were administered acute or chronic nicotine and trained over three days in a differential backward trace conditioning paradigm consisting of five trials of a forward conditioned stimulus (CS)+ (Light) co-terminating with a footshock unconditioned stimulus followed by a backward CS- (Tone) presented 20 s after cessation of the unconditioned stimulus. Summation testing found that acute nicotine disrupted learned safety, but chronic nicotine had no effect. Another group of animals administered acute nicotine showed fear when presented with the backward CS (Light) alone, indicating the formation of a maladaptive fear association with the backward CS. Finally, we investigated the brain regions involved by administering nicotine directly into the dorsal hippocampus, ventral hippocampus, and prelimbic cortex. Infusion of nicotine into the dorsal hippocampus disrupted safety learning.

Prefrontal control of cerebellum-dependent associative motor learning.

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Chen, Hao; Yang, Li; Xu, Yan; Wu, Guang-yan; Yao, Juan; Zhang, Jun; Zhu, Zhi-ru; Hu, Zhi-an; Sui, Jian-feng; Hu, Bo

2014-02-01

Behavioral studies have demonstrated that both medial prefrontal cortex (mPFC) and cerebellum play critical roles in trace eyeblink conditioning. However, little is known regarding the mechanism by which the two brain regions interact. By use of electrical stimulation of the caudal mPFC as a conditioned stimulus, we show evidence that persistent outputs from the mPFC to cerebellum are necessary and sufficient for the acquisition and expression of a trace conditioned response (CR)-like response. Specifically, the persistent outputs of caudal mPFC are relayed to the cerebellum via the rostral part of lateral pontine nuclei. Moreover, interfering with persistent activity by blockade of the muscarinic Ach receptor in the caudal mPFC impairs the expression of learned trace CRs. These results suggest an important way for the caudal mPFC to interact with the cerebellum during associative motor learning.

Learning style, judgements of learning, and learning of verbal and visual information.

Science.gov (United States)

Knoll, Abby R; Otani, Hajime; Skeel, Reid L; Van Horn, K Roger

2017-08-01

The concept of learning style is immensely popular despite the lack of evidence showing that learning style influences performance. This study tested the hypothesis that the popularity of learning style is maintained because it is associated with subjective aspects of learning, such as judgements of learning (JOLs). Preference for verbal and visual information was assessed using the revised Verbalizer-Visualizer Questionnaire (VVQ). Then, participants studied a list of word pairs and a list of picture pairs, making JOLs (immediate, delayed, and global) while studying each list. Learning was tested by cued recall. The results showed that higher VVQ verbalizer scores were associated with higher immediate JOLs for words, and higher VVQ visualizer scores were associated with higher immediate JOLs for pictures. There was no association between VVQ scores and recall or JOL accuracy. As predicted, learning style was associated with subjective aspects of learning but not objective aspects of learning. © 2016 The British Psychological Society.

Associations and propositions: the case for a dual-process account of learning in humans.

Science.gov (United States)

McLaren, I P L; Forrest, C L D; McLaren, R P; Jones, F W; Aitken, M R F; Mackintosh, N J

2014-02-01

We review evidence that supports the conclusion that people can and do learn in two distinct ways - one associative, the other propositional. No one disputes that we solve problems by testing hypotheses and inducing underlying rules, so the issue amounts to deciding whether there is evidence that we (and other animals) also rely on a simpler, associative system, that detects the frequency of occurrence of different events in our environment and the contingencies between them. There is neuroscientific evidence that associative learning occurs in at least some animals (e.g., Aplysia californica), so it must be the case that associative learning has evolved. Since both associative and propositional theories can in principle account for many instances of successful learning, the problem is then to show that there are at least some cases where the two classes of theory predict different outcomes. We offer a demonstration of cue competition effects in humans under incidental conditions as evidence against the argument that all such effects are based on cognitive inference. The latter supposition would imply that if the necessary information is unavailable to inference then no cue competition should occur. We then discuss the case of unblocking by reinforcer omission, where associative theory predicts an irrational solution to the problem, and consider the phenomenon of the Perruchet effect, in which conscious expectancy and conditioned response dissociate. Further discussion makes use of evidence that people will sometimes provide one solution to a problem when it is presented to them in summary form, and another when they are presented in rapid succession with trial-by trial information. We also demonstrate that people trained on a discrimination may show a peak shift (predicted by associative theory), but given the time and opportunity to detect the relationships between S+ and S-, show rule-based behavior instead. Finally, we conclude by presenting evidence that

Distinct roles of the RasGAP family proteins in C. elegans associative learning and memory.

Science.gov (United States)

Gyurkó, M Dávid; Csermely, Péter; Sőti, Csaba; Steták, Attila

2015-10-15

The Ras GTPase activating proteins (RasGAPs) are regulators of the conserved Ras/MAPK pathway. Various roles of some of the RasGAPs in learning and memory have been reported in different model systems, yet, there is no comprehensive study to characterize all gap genes in any organism. Here, using reverse genetics and neurobehavioural tests, we studied the role of all known genes of the rasgap family in C. elegans in associative learning and memory. We demonstrated that their proteins are implicated in different parts of the learning and memory processes. We show that gap-1 contribute redundantly with gap-3 to the chemosensation of volatile compounds, gap-1 plays a major role in associative learning, while gap-2 and gap-3 are predominantly required for short- and long-term associative memory. Our results also suggest that the C. elegans Ras orthologue let-60 is involved in multiple processes during learning and memory. Thus, we show that the different classes of RasGAP proteins are all involved in cognitive function and their complex interplay ensures the proper formation and storage of novel information in C. elegans.

Amygdala's involvement in facilitating associative learning-induced plasticity: a promiscuous role for the amygdala in memory acquisition.

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Chau, Lily S; Galvez, Roberto

2012-01-01

It is widely accepted that the amygdala plays a critical role in acquisition and consolidation of fear-related memories. Some of the more widely employed behavioral paradigms that have assisted in solidifying the amygdala's role in fear-related memories are associative learning paradigms. With most associative learning tasks, a neutral conditioned stimulus (CS) is paired with a salient unconditioned stimulus (US) that elicits an unconditioned response (UR). After multiple CS-US pairings, the subject learns that the CS predicts the onset or delivery of the US, and thus elicits a learned conditioned response (CR). Most fear-related associative paradigms have suggested that an aspect of the fear association is stored in the amygdala; however, some fear-motivated associative paradigms suggest that the amygdala is not a site of storage, but rather facilitates consolidation in other brain regions. Based upon various learning theories, one of the most likely sites for storage of long-term memories is the neocortex. In support of these theories, findings from our laboratory, and others, have demonstrated that trace-conditioning, an associative paradigm where there is a separation in time between the CS and US, induces learning-specific neocortical plasticity. The following review will discuss the amygdala's involvement, either as a site of storage or facilitating storage in other brain regions such as the neocortex, in fear- and non-fear-motivated associative paradigms. In this review, we will discuss recent findings suggesting a broader role for the amygdala in increasing the saliency of behaviorally relevant information, thus facilitating acquisition for all forms of memory, both fear- and non-fear-related. This proposed promiscuous role of the amygdala in facilitating acquisition for all memories further suggests a potential role of the amygdala in general learning disabilities.

Learning Disabilities Association of America

Science.gov (United States)

... provides the most current information on research, practice, theory, issues, and trends to broaden understanding and improve ... These services make LDA the leading resource for information on learning disabilities. Learn more about: Auditory Processing ... Processing Disorder ...

Hyper-Binding across Time: Age Differences in the Effect of Temporal Proximity on Paired-Associate Learning

Science.gov (United States)

Campbell, Karen L.; Trelle, Alexandra; Hasher, Lynn

2014-01-01

Older adults show hyper- (or excessive) binding effects for simultaneously and sequentially presented distraction. Here, we addressed the potential role of hyper-binding in paired-associate learning. Older and younger adults learned a list of word pairs and then received an associative recognition task in which rearranged pairs were formed from…

Bayesian methods for addressing long-standing problems in associative learning: The case of PREE.

Science.gov (United States)

Blanco, Fernando; Moris, Joaquín

2017-07-20

Most associative models typically assume that learning can be understood as a gradual change in associative strength that captures the situation into one single parameter, or representational state. We will call this view single-state learning. However, there is ample evidence showing that under many circumstances different relationships that share features can be learned independently, and animals can quickly switch between expressing one or another. We will call this multiple-state learning. Theoretically, it is understudied because it needs a different data analysis approach from those usually employed. In this paper, we present a Bayesian model of the Partial Reinforcement Extinction Effect (PREE) that can test the predictions of the multiple-state view. This implies estimating the moment of change in the responses (from the acquisition to the extinction performance), both at the individual and at the group levels. We used this model to analyze data from a PREE experiment with three levels of reinforcement during acquisition (100%, 75% and 50%). We found differences in the estimated moment of switch between states during extinction, so that it was delayed after leaner partial reinforcement schedules. The finding is compatible with the multiple-state view. It is the first time, to our knowledge, that the predictions from the multiple-state view are tested directly. The paper also aims to show the benefits that Bayesian methods can bring to the associative learning field.

Online unsupervised formation of cell assemblies for the encoding of multiple cognitive maps.

Science.gov (United States)

Salihoglu, Utku; Bersini, Hugues; Yamaguchi, Yoko; Molter, Colin

2009-01-01

Since their introduction sixty years ago, cell assemblies have proved to be a powerful paradigm for brain information processing. After their introduction in artificial intelligence, cell assemblies became commonly used in computational neuroscience as a neural substrate for content addressable memories. However, the mechanisms underlying their formation are poorly understood and, so far, there is no biologically plausible algorithms which can explain how external stimuli can be online stored in cell assemblies. We addressed this question in a previous paper [Salihoglu, U., Bersini, H., Yamaguchi, Y., Molter, C., (2009). A model for the cognitive map formation: Application of the retroaxonal theory. In Proc. IEEE international joint conference on neural networks], were, based on biologically plausible mechanisms, a novel unsupervised algorithm for online cell assemblies' creation was developed. The procedure involved simultaneously, a fast Hebbian/anti-Hebbian learning of the network's recurrent connections for the creation of new cell assemblies, and a slower feedback signal which stabilized the cell assemblies by learning the feedforward input connections. Here, we first quantify the role played by the retroaxonal feedback mechanism. Then, we show how multiple cognitive maps, composed by a set of orthogonal input stimuli, can be encoded in the network. As a result, when facing a previously learned input, the system is able to retrieve the cognitive map it belongs to. As a consequence, ambiguous inputs which could belong to multiple cognitive maps can be disambiguated by the knowledge of the context, i.e. the cognitive map.

Synaptic neurotransmission depression in ventral tegmental dopamine neurons and cannabinoid-associated addictive learning.

Science.gov (United States)

Liu, Zhiqiang; Han, Jing; Jia, Lintao; Maillet, Jean-Christian; Bai, Guang; Xu, Lin; Jia, Zhengping; Zheng, Qiaohua; Zhang, Wandong; Monette, Robert; Merali, Zul; Zhu, Zhou; Wang, Wei; Ren, Wei; Zhang, Xia

2010-12-20

Drug addiction is an association of compulsive drug use with long-term associative learning/memory. Multiple forms of learning/memory are primarily subserved by activity- or experience-dependent synaptic long-term potentiation (LTP) and long-term depression (LTD). Recent studies suggest LTP expression in locally activated glutamate synapses onto dopamine neurons (local Glu-DA synapses) of the midbrain ventral tegmental area (VTA) following a single or chronic exposure to many drugs of abuse, whereas a single exposure to cannabinoid did not significantly affect synaptic plasticity at these synapses. It is unknown whether chronic exposure of cannabis (marijuana or cannabinoids), the most commonly used illicit drug worldwide, induce LTP or LTD at these synapses. More importantly, whether such alterations in VTA synaptic plasticity causatively contribute to drug addictive behavior has not previously been addressed. Here we show in rats that chronic cannabinoid exposure activates VTA cannabinoid CB1 receptors to induce transient neurotransmission depression at VTA local Glu-DA synapses through activation of NMDA receptors and subsequent endocytosis of AMPA receptor GluR2 subunits. A GluR2-derived peptide blocks cannabinoid-induced VTA synaptic depression and conditioned place preference, i.e., learning to associate drug exposure with environmental cues. These data not only provide the first evidence, to our knowledge, that NMDA receptor-dependent synaptic depression at VTA dopamine circuitry requires GluR2 endocytosis, but also suggest an essential contribution of such synaptic depression to cannabinoid-associated addictive learning, in addition to pointing to novel pharmacological strategies for the treatment of cannabis addiction.

Synaptic neurotransmission depression in ventral tegmental dopamine neurons and cannabinoid-associated addictive learning.

Directory of Open Access Journals (Sweden)

Zhiqiang Liu

2010-12-01

Full Text Available Drug addiction is an association of compulsive drug use with long-term associative learning/memory. Multiple forms of learning/memory are primarily subserved by activity- or experience-dependent synaptic long-term potentiation (LTP and long-term depression (LTD. Recent studies suggest LTP expression in locally activated glutamate synapses onto dopamine neurons (local Glu-DA synapses of the midbrain ventral tegmental area (VTA following a single or chronic exposure to many drugs of abuse, whereas a single exposure to cannabinoid did not significantly affect synaptic plasticity at these synapses. It is unknown whether chronic exposure of cannabis (marijuana or cannabinoids, the most commonly used illicit drug worldwide, induce LTP or LTD at these synapses. More importantly, whether such alterations in VTA synaptic plasticity causatively contribute to drug addictive behavior has not previously been addressed. Here we show in rats that chronic cannabinoid exposure activates VTA cannabinoid CB1 receptors to induce transient neurotransmission depression at VTA local Glu-DA synapses through activation of NMDA receptors and subsequent endocytosis of AMPA receptor GluR2 subunits. A GluR2-derived peptide blocks cannabinoid-induced VTA synaptic depression and conditioned place preference, i.e., learning to associate drug exposure with environmental cues. These data not only provide the first evidence, to our knowledge, that NMDA receptor-dependent synaptic depression at VTA dopamine circuitry requires GluR2 endocytosis, but also suggest an essential contribution of such synaptic depression to cannabinoid-associated addictive learning, in addition to pointing to novel pharmacological strategies for the treatment of cannabis addiction.

Synaptic Neurotransmission Depression in Ventral Tegmental Dopamine Neurons and Cannabinoid-Associated Addictive Learning

Science.gov (United States)

Liu, Zhiqiang; Han, Jing; Jia, Lintao; Maillet, Jean-Christian; Bai, Guang; Xu, Lin; Jia, Zhengping; Zheng, Qiaohua; Zhang, Wandong; Monette, Robert; Merali, Zul; Zhu, Zhou; Wang, Wei; Ren, Wei; Zhang, Xia

2010-01-01

Drug addiction is an association of compulsive drug use with long-term associative learning/memory. Multiple forms of learning/memory are primarily subserved by activity- or experience-dependent synaptic long-term potentiation (LTP) and long-term depression (LTD). Recent studies suggest LTP expression in locally activated glutamate synapses onto dopamine neurons (local Glu-DA synapses) of the midbrain ventral tegmental area (VTA) following a single or chronic exposure to many drugs of abuse, whereas a single exposure to cannabinoid did not significantly affect synaptic plasticity at these synapses. It is unknown whether chronic exposure of cannabis (marijuana or cannabinoids), the most commonly used illicit drug worldwide, induce LTP or LTD at these synapses. More importantly, whether such alterations in VTA synaptic plasticity causatively contribute to drug addictive behavior has not previously been addressed. Here we show in rats that chronic cannabinoid exposure activates VTA cannabinoid CB1 receptors to induce transient neurotransmission depression at VTA local Glu-DA synapses through activation of NMDA receptors and subsequent endocytosis of AMPA receptor GluR2 subunits. A GluR2-derived peptide blocks cannabinoid-induced VTA synaptic depression and conditioned place preference, i.e., learning to associate drug exposure with environmental cues. These data not only provide the first evidence, to our knowledge, that NMDA receptor-dependent synaptic depression at VTA dopamine circuitry requires GluR2 endocytosis, but also suggest an essential contribution of such synaptic depression to cannabinoid-associated addictive learning, in addition to pointing to novel pharmacological strategies for the treatment of cannabis addiction. PMID:21187978

Is problem-based learning associated with students’ motivation? A quantitative and qualitative study

NARCIS (Netherlands)

M. Wijnen (Marit); S.M.M. Loyens (Sofie); L. Wijnia (Lisette); G. Smeets (Guus); M.J. Kroeze (Maarten); H.T. van der Molen (Henk)

2017-01-01

textabstractIn this study, a mixed-method design was employed to investigate the association between a student-centred, problem-based learning (PBL) method and law students’ motivation. Self-determination theory (SDT) states that autonomous motivation, which is associated with higher academic

Change detection and change monitoring of natural and man-made features in multispectral and hyperspectral satellite imagery

Science.gov (United States)

Moody, Daniela Irina

2018-04-17

An approach for land cover classification, seasonal and yearly change detection and monitoring, and identification of changes in man-made features may use a clustering of sparse approximations (CoSA) on sparse representations in learned dictionaries. A Hebbian learning rule may be used to build multispectral or hyperspectral, multiresolution dictionaries that are adapted to regional satellite image data. Sparse image representations of pixel patches over the learned dictionaries may be used to perform unsupervised k-means clustering into land cover categories. The clustering process behaves as a classifier in detecting real variability. This approach may combine spectral and spatial textural characteristics to detect geologic, vegetative, hydrologic, and man-made features, as well as changes in these features over time.

Span: spike pattern association neuron for learning spatio-temporal spike patterns.

Science.gov (United States)

Mohemmed, Ammar; Schliebs, Stefan; Matsuda, Satoshi; Kasabov, Nikola

2012-08-01

Spiking Neural Networks (SNN) were shown to be suitable tools for the processing of spatio-temporal information. However, due to their inherent complexity, the formulation of efficient supervised learning algorithms for SNN is difficult and remains an important problem in the research area. This article presents SPAN - a spiking neuron that is able to learn associations of arbitrary spike trains in a supervised fashion allowing the processing of spatio-temporal information encoded in the precise timing of spikes. The idea of the proposed algorithm is to transform spike trains during the learning phase into analog signals so that common mathematical operations can be performed on them. Using this conversion, it is possible to apply the well-known Widrow-Hoff rule directly to the transformed spike trains in order to adjust the synaptic weights and to achieve a desired input/output spike behavior of the neuron. In the presented experimental analysis, the proposed learning algorithm is evaluated regarding its learning capabilities, its memory capacity, its robustness to noisy stimuli and its classification performance. Differences and similarities of SPAN regarding two related algorithms, ReSuMe and Chronotron, are discussed.

Networks of Learning : Professional Association and the Continuing Education of Teachers of Mathematics in Pakistan

DEFF Research Database (Denmark)

Baber, Sikunder Ali

" and shows how a number of professional associations have become as networks of learning to encourage the continuing professional education of both pre-service and in-service teachers in the context of Pakistan. A case of the Mathematics Association of Pakistan (MAP) as a Network of Learning is presented....... The formation and growth of this network can be viewed as developing insights into the improvement of mathematics education in the developing world. The contributions of the association may also add value to the learning of teacher colleagues in other parts of the world. This sharing of the experience may......Importance of the professional development of teachers has been recognized and research has contributed greatly in terms of proposing variety of approaches for the development of teachers,both pre-service and in-service. Among them, networking among teachers, teacher educators,curriculum developers...

The Acquisition of Simple Associations as Observed in Color-Word Contingency Learning

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Lin, Olivia Y.-H.; MacLeod, Colin M.

2018-01-01

Three experiments investigated the learning of simple associations in a color-word contingency task. Participants responded manually to the print colors of 3 words, with each word associated strongly to 1 of the 3 colors and weakly to the other 2 colors. Despite the words being irrelevant, response times to high-contingency stimuli and to…

Long-term associative learning predicts verbal short-term memory performance.

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Jones, Gary; Macken, Bill

2018-02-01

Studies using tests such as digit span and nonword repetition have implicated short-term memory across a range of developmental domains. Such tests ostensibly assess specialized processes for the short-term manipulation and maintenance of information that are often argued to enable long-term learning. However, there is considerable evidence for an influence of long-term linguistic learning on performance in short-term memory tasks that brings into question the role of a specialized short-term memory system separate from long-term knowledge. Using natural language corpora, we show experimentally and computationally that performance on three widely used measures of short-term memory (digit span, nonword repetition, and sentence recall) can be predicted from simple associative learning operating on the linguistic environment to which a typical child may have been exposed. The findings support the broad view that short-term verbal memory performance reflects the application of long-term language knowledge to the experimental setting.

Amygdala’s involvement in facilitating associative learning-induced plasticity: a promiscuous role for the amygdala in memory acquisition

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Lily S Chau

2012-10-01

Full Text Available It is widely accepted that the amygdala plays a critical role in acquisition and consolidation of fear-related memories. Some of the more widely employed behavioral paradigms that have assisted in solidifying the amygdala’s role in fear-related memories are associative learning paradigms. With most associative learning tasks, a neutral conditioned stimulus (CS is paired with a salient unconditioned stimulus (US that elicits an unconditioned response (UR. After multiple CS-US pairings, the subject learns that the CS predicts the onset or delivery of the US, and thus elicits a learned conditioned response (CR. Most fear-related associative paradigms have suggested that an aspect of the fear association is stored in the amygdala; however, some fear-motivated associative paradigms suggest that the amygdala is not a site of storage, but rather facilitates consolidation in other brain regions. Based upon various learning theories, one of the most likely sites for storage of long-term memories is the neocortex. In support of these theories, findings from our laboratory, and others, have demonstrated that trace-conditioning, an associative paradigm where there is a separation in time between the CS and US, induces learning-specific neocortical plasticity. The following review will discuss the amygdala’s involvement, either as a site of storage or facilitating storage in other brain regions such as the neocortex, in fear- and non-fear-motivated associative paradigms. In this review, we will discuss recent findings suggesting a broader role for the amygdala in increasing the saliency of behaviorally relevant information, thus facilitating acquisition for all forms of memory, both fear- and non-fear-related. This proposed promiscuous role of the amygdala in facilitating acquisition for all memories further suggests a potential role of the amygdala in general learning disabilities.

Children learn spurious associations in their math textbooks: Examples from fraction arithmetic.

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Braithwaite, David W; Siegler, Robert S

2018-04-26

Fraction arithmetic is among the most important and difficult topics children encounter in elementary and middle school mathematics. Braithwaite, Pyke, and Siegler (2017) hypothesized that difficulties learning fraction arithmetic often reflect reliance on associative knowledge-rather than understanding of mathematical concepts and procedures-to guide choices of solution strategies. They further proposed that this associative knowledge reflects distributional characteristics of the fraction arithmetic problems children encounter. To test these hypotheses, we examined textbooks and middle school children in the United States (Experiments 1 and 2) and China (Experiment 3). We asked the children to predict which arithmetic operation would accompany a specified pair of operands, to generate operands to accompany a specified arithmetic operation, and to match operands and operations. In both countries, children's responses indicated that they associated operand pairs having equal denominators with addition and subtraction, and operand pairs having a whole number and a fraction with multiplication and division. The children's associations paralleled the textbook input in both countries, which was consistent with the hypothesis that children learned the associations from the practice problems. Differences in the effects of such associative knowledge on U.S. and Chinese children's fraction arithmetic performance are discussed, as are implications of these differences for educational practice. (PsycINFO Database Record (c) 2018 APA, all rights reserved).

The Association between Motivation, Affect, and Self-regulated Learning When Solving Problems

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Martine Baars

2017-08-01

Full Text Available Self-regulated learning (SRL skills are essential for learning during school years, particularly in complex problem-solving domains, such as biology and math. Although a lot of studies have focused on the cognitive resources that are needed for learning to solve problems in a self-regulated way, affective and motivational resources have received much less research attention. The current study investigated the relation between affect (i.e., Positive Affect and Negative Affect Scale, motivation (i.e., autonomous and controlled motivation, mental effort, SRL skills, and problem-solving performance when learning to solve biology problems in a self-regulated online learning environment. In the learning phase, secondary education students studied video-modeling examples of how to solve hereditary problems, solved hereditary problems which they chose themselves from a set of problems with different complexity levels (i.e., five levels. In the posttest, students solved hereditary problems, self-assessed their performance, and chose a next problem from the set of problems but did not solve these problems. The results from this study showed that negative affect, inaccurate self-assessments during the posttest, and higher perceptions of mental effort during the posttest were negatively associated with problem-solving performance after learning in a self-regulated way.

The Association between Motivation, Affect, and Self-regulated Learning When Solving Problems.

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Baars, Martine; Wijnia, Lisette; Paas, Fred

2017-01-01

Self-regulated learning (SRL) skills are essential for learning during school years, particularly in complex problem-solving domains, such as biology and math. Although a lot of studies have focused on the cognitive resources that are needed for learning to solve problems in a self-regulated way, affective and motivational resources have received much less research attention. The current study investigated the relation between affect (i.e., Positive Affect and Negative Affect Scale), motivation (i.e., autonomous and controlled motivation), mental effort, SRL skills, and problem-solving performance when learning to solve biology problems in a self-regulated online learning environment. In the learning phase, secondary education students studied video-modeling examples of how to solve hereditary problems, solved hereditary problems which they chose themselves from a set of problems with different complexity levels (i.e., five levels). In the posttest, students solved hereditary problems, self-assessed their performance, and chose a next problem from the set of problems but did not solve these problems. The results from this study showed that negative affect, inaccurate self-assessments during the posttest, and higher perceptions of mental effort during the posttest were negatively associated with problem-solving performance after learning in a self-regulated way.

The Association Between Learning Climate and Adverse Obstetrical Outcomes in 16 Nontertiary Obstetrics-Gynecology Departments in the Netherlands.

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Smirnova, Alina; Ravelli, Anita C J; Stalmeijer, Renée E; Arah, Onyebuchi A; Heineman, Maas Jan; van der Vleuten, Cees P M; van der Post, Joris A M; Lombarts, Kiki M J M H

2017-12-01

To investigate the association between learning climate and adverse perinatal and maternal outcomes in obstetrics-gynecology departments. The authors analyzed 23,629 births and 103 learning climate evaluations from 16 nontertiary obstetrics-gynecology departments in the Netherlands in 2013. Multilevel logistic regressions were used to calculate the odds of adverse perinatal and maternal outcomes, by learning climate score tertile, adjusting for maternal and department characteristics. Adverse perinatal outcomes included fetal or early neonatal mortality, five-minute Apgar score Learning climate scores were significantly associated with increased odds of adverse perinatal outcomes (aOR 2.06, 95% CI 1.14-3.72). Compared with the lowest tertile, departments in the middle tertile had 46% greater odds of adverse perinatal outcomes (aOR 1.46, 95% CI 1.09-1.94); departments in the highest tertile had 69% greater odds (aOR 1.69, 95% CI 1.24-2.30). Learning climate was not associated with adverse maternal outcomes (middle vs. lowest tertile: OR 1.04, 95% CI 0.93-1.16; highest vs. lowest tertile: OR 0.98, 95% CI 0.88-1.10). Learning climate was associated with significantly increased odds of adverse perinatal, but not maternal, outcomes. Research in similar clinical contexts is needed to replicate these findings and explore potential mechanisms behind these associations.

Adolescent changes in dopamine D1 receptor expression in orbitofrontal cortex and piriform cortex accompany an associative learning deficit.

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Anna K Garske

Full Text Available The orbitofrontal cortex (OFC and piriform cortex are involved in encoding the predictive value of olfactory stimuli in rats, and neural responses to olfactory stimuli in these areas change as associations are learned. This experience-dependent plasticity mirrors task-related changes previously observed in mesocortical dopamine neurons, which have been implicated in learning the predictive value of cues. Although forms of associative learning can be found at all ages, cortical dopamine projections do not mature until after postnatal day 35 in the rat. We hypothesized that these changes in dopamine circuitry during the juvenile and adolescent periods would result in age-dependent differences in learning the predictive value of environmental cues. Using an odor-guided associative learning task, we found that adolescent rats learn the association between an odor and a palatable reward significantly more slowly than either juvenile or adult rats. Further, adolescent rats displayed greater distractibility during the task than either juvenile or adult rats. Using real-time quantitative PCR and immunohistochemical methods, we observed that the behavioral deficit in adolescence coincides with a significant increase in D1 dopamine receptor expression compared to juvenile rats in both the OFC and piriform cortex. Further, we found that both the slower learning and increased distractibility exhibited in adolescence could be alleviated by experience with the association task as a juvenile, or by an acute administration of a low dose of either the dopamine D1 receptor agonist SKF-38393 or the D2 receptor antagonist eticlopride. These results suggest that dopaminergic modulation of cortical function may be important for learning the predictive value of environmental stimuli, and that developmental changes in cortical dopaminergic circuitry may underlie age-related differences in associative learning.

Explaining neural signals in human visual cortex with an associative learning model.

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Jiang, Jiefeng; Schmajuk, Nestor; Egner, Tobias

2012-08-01

"Predictive coding" models posit a key role for associative learning in visual cognition, viewing perceptual inference as a process of matching (learned) top-down predictions (or expectations) against bottom-up sensory evidence. At the neural level, these models propose that each region along the visual processing hierarchy entails one set of processing units encoding predictions of bottom-up input, and another set computing mismatches (prediction error or surprise) between predictions and evidence. This contrasts with traditional views of visual neurons operating purely as bottom-up feature detectors. In support of the predictive coding hypothesis, a recent human neuroimaging study (Egner, Monti, & Summerfield, 2010) showed that neural population responses to expected and unexpected face and house stimuli in the "fusiform face area" (FFA) could be well-described as a summation of hypothetical face-expectation and -surprise signals, but not by feature detector responses. Here, we used computer simulations to test whether these imaging data could be formally explained within the broader framework of a mathematical neural network model of associative learning (Schmajuk, Gray, & Lam, 1996). Results show that FFA responses could be fit very closely by model variables coding for conditional predictions (and their violations) of stimuli that unconditionally activate the FFA. These data document that neural population signals in the ventral visual stream that deviate from classic feature detection responses can formally be explained by associative prediction and surprise signals.

I Don’t Want to Miss a Thing – Learning Dynamics and Effects of Feedback Type and Monetary Incentive in a Paired Associate Deterministic Learning Task

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Magda Gawlowska

2017-06-01

Full Text Available Effective functioning in a complex environment requires adjusting of behavior according to changing situational demands. To do so, organisms must learn new, more adaptive behaviors by extracting the necessary information from externally provided feedback. Not surprisingly, feedback-guided learning has been extensively studied using multiple research paradigms. The purpose of the present study was to test the newly designed Paired Associate Deterministic Learning task (PADL, in which participants were presented with either positive or negative deterministic feedback. Moreover, we manipulated the level of motivation in the learning process by comparing blocks with strictly cognitive, informative feedback to blocks where participants were additionally motivated by anticipated monetary reward or loss. Our results proved the PADL to be a useful tool not only for studying the learning process in a deterministic environment, but also, due to the varying task conditions, for assessing differences in learning patterns. Particularly, we show that the learning process itself is influenced by manipulating both the type of feedback information and the motivational significance associated with the expected monetary reward.

EEG potentials associated with artificial grammar learning in the primate brain.

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Attaheri, Adam; Kikuchi, Yukiko; Milne, Alice E; Wilson, Benjamin; Alter, Kai; Petkov, Christopher I

2015-09-01

Electroencephalography (EEG) has identified human brain potentials elicited by Artificial Grammar (AG) learning paradigms, which present participants with rule-based sequences of stimuli. Nonhuman animals are sensitive to certain AGs; therefore, evaluating which EEG Event Related Potentials (ERPs) are associated with AG learning in nonhuman animals could identify evolutionarily conserved processes. We recorded EEG potentials during an auditory AG learning experiment in two Rhesus macaques. The animals were first exposed to sequences of nonsense words generated by the AG. Then surface-based ERPs were recorded in response to sequences that were 'consistent' with the AG and 'violation' sequences containing illegal transitions. The AG violations strongly modulated an early component, potentially homologous to the Mismatch Negativity (mMMN), a P200 and a late frontal positivity (P500). The macaque P500 is similar in polarity and time of occurrence to a late EEG positivity reported in human AG learning studies but might differ in functional role. Copyright © 2014 The Authors. Published by Elsevier Inc. All rights reserved.

Advanced Parkinson’s disease effect on goal-directed and habitual processes involved in visuomotor associative learning

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Fadila eHadj-Bouziane

2013-01-01

Full Text Available The present behavioral study readdresses the question of habit learning in Parkinson's disease. Patients were early onset, non-demented, dopa-responsive, candidates for surgical treatment, similar to those we found earlier as suffering greater dopamine depletion in the putamen than in the caudate nucleus. The task was the same conditional associative learning task as that used previously in monkeys and healthy humans to unveil the striatum involvement in habit learning. Sixteen patients and 20 age- and education-matched healthy control subjects learned sets of 3 visuo-motor associations between complex patterns and joystick displacements during two testing sessions separated by a few hours. We distinguished errors preceding versus following the first correct response to compare patients' performance during the earliest phase of learning dominated by goal-directed actions with that observed later on, when responses start to become habitual. The disease significantly retarded both learning phases, especially in patients under sixty years of age. However, only the late phase deficit was disease severity-dependent and persisted on the second testing session. These findings provide the first corroboration in Parkinson patients of two ideas well-established in the animal literature. The first is the idea that associating visual stimuli to motor acts is a form of habit learning that engages the striatum. It is confirmed here by the global impairment in visuo-motor learning induced by Parkinson's disease. The second idea is that goal-directed behaviors are predominantly caudate-dependent whereas habitual responses are primarily putamen-dependent. At the advanced Parkinson's disease stages tested here, dopamine depletion is greater in the putamen than in the caudate nucleus. Accordingly, the late phase of learning corresponding to the emergence of habitual responses was more vulnerable to the disease than the early phase dominated by goal

Appetitive Olfactory Learning and Long-Term Associative Memory in Caenorhabditis elegans

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Ichiro N. Maruyama

2017-05-01

Full Text Available Because of the relative simplicity of its nervous system, Caenorhabditis elegans is a useful model organism to study learning and memory at cellular and molecular levels. For appetitive conditioning in C. elegans, food has exclusively been used as an unconditioned stimulus (US. It may be difficult to analyze neuronal circuits for associative memory since food is a multimodal combination of olfactory, gustatory, and mechanical stimuli. Here, we report classical appetitive conditioning and associative memory in C. elegans, using 1-nonanol as a conditioned stimulus (CS, and potassium chloride (KCl as a US. Before conditioning, C. elegans innately avoided 1-nonanol, an aversive olfactory stimulus, and was attracted by KCl, an appetitive gustatory stimulus, on assay agar plates. Both massed training without an intertrial interval (ITI and spaced training with a 10-min ITI induced significant levels of memory of association regarding the two chemicals. Memory induced by massed training decayed within 6 h, while that induced by spaced training was retained for more than 6 h. Animals treated with inhibitors of transcription or translation formed the memory induced by spaced training less efficiently than untreated animals, whereas the memory induced by massed training was not significantly affected by such treatments. By definition, therefore, memories induced by massed training and spaced training are classified as short-term memory (STM and long-term memory (LTM, respectively. When animals conditioned by spaced training were exposed to 1-nonanol alone, their learning index was lower than that of untreated animals, suggesting that extinction learning occurs in C. elegans. In support of these results, C. elegans mutants defective in nmr-1, encoding an NMDA receptor subunit, formed both STM and LTM less efficiently than wild-type animals, while mutations in crh-1, encoding a ubiquitous transcription factor CREB required for memory consolidation, affected

Contribution Of Brain Tissue Oxidative Damage In Hypothyroidism-associated Learning and Memory Impairments

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Yousef Baghcheghi

2017-01-01

Full Text Available The brain is a critical target organ for thyroid hormones, and modifications in memory and cognition happen with thyroid dysfunction. The exact mechanisms underlying learning and memory impairments due to hypothyroidism have not been understood yet. Therefore, this review was aimed to compress the results of previous studies which have examined the contribution of brain tissues oxidative damage in hypothyroidism-associated learning and memory impairments.

Retrospective analysis of the learning curve associated with laparoscopic ovariectomy in dogs and associated perioperative complication rates.

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Pope, Juliet Frances Anne; Knowles, Toby Grahame

2014-08-01

To assess the learning curve associated with laparoscopic ovariectomy (LOE) in 618 dogs and to report perioperative complication rates. Case series. Dogs (n = 618). Data retrieved from the medical records of bitches admitted for LOE over 42 months included date of surgery, breed, weight (kg), age (months), surgeon, suture material used, intraoperative complications and postoperative complications. Each LOE was defined as "successful" or "unsuccessful" by the absence or presence of an intraoperative complication and "failure" rate described using a CUSUM technique. Follow-up time ranged from 152 to 1,435 days (median, 737 days). Intraoperative complications occurred in 10 dogs (1.6%) and included: splenic laceration (6 dogs; 1%), urinary bladder perforation (3 dogs; 0.5%), and subcutaneous emphysema (1 dog; 0.2%). Postoperative complications occurred in 99 dogs (16%) and included: incisional inflammation treated with antibiotics (87 dogs [14%]; 96/1,854 incisions; 5.1%), incisional seroma (5 dogs [0.8%]; 5/1,854 incisions, 0.3%), incisional hernia (4 dogs [0.6%]; 4/1,854 incisions, 0.2%), and ovarian remnant syndrome (3 dogs; 0.5%). CUSUM charts indicated an initial "learning curve" of ∼80 LOE. LOE is a technique with an initial learning curve but once surgical proficiency is reached after ∼80 procedures then intraoperative complication rates associated with the procedure can be low. © Copyright 2014 by The American College of Veterinary Surgeons.

Nicotinic modulation of hippocampal cell signaling and associated effects on learning and memory.

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Kutlu, Munir Gunes; Gould, Thomas J

2016-03-01

The hippocampus is a key brain structure involved in synaptic plasticity associated with long-term declarative memory formation. Importantly, nicotine and activation of nicotinic acetylcholine receptors (nAChRs) can alter hippocampal plasticity and these changes may occur through modulation of hippocampal kinases and transcription factors. Hippocampal kinases such as cAMP-dependent protein kinase (PKA), calcium/calmodulin-dependent protein kinases (CAMKs), extracellular signal-regulated kinases 1 and 2 (ERK1/2), and c-jun N-terminal kinase 1 (JNK1), and the transcription factor cAMP-response element-binding protein (CREB) that are activated either directly or indirectly by nicotine may modulate hippocampal plasticity and in parallel hippocampus-dependent learning and memory. Evidence suggests that nicotine may alter hippocampus-dependent learning by changing the time and magnitude of activation of kinases and transcription factors normally involved in learning and by recruiting additional cell signaling molecules. Understanding how nicotine alters learning and memory will advance basic understanding of the neural substrates of learning and aid in understanding mental disorders that involve cognitive and learning deficits. Copyright © 2015 Elsevier Inc. All rights reserved.

Identifying associations between pig pathologies using a multi-dimensional machine learning methodology.

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Sanchez-Vazquez, Manuel J; Nielen, Mirjam; Edwards, Sandra A; Gunn, George J; Lewis, Fraser I

2012-08-31

Abattoir detected pathologies are of crucial importance to both pig production and food safety. Usually, more than one pathology coexist in a pig herd although it often remains unknown how these different pathologies interrelate to each other. Identification of the associations between different pathologies may facilitate an improved understanding of their underlying biological linkage, and support the veterinarians in encouraging control strategies aimed at reducing the prevalence of not just one, but two or more conditions simultaneously. Multi-dimensional machine learning methodology was used to identify associations between ten typical pathologies in 6485 batches of slaughtered finishing pigs, assisting the comprehension of their biological association. Pathologies potentially associated with septicaemia (e.g. pericarditis, peritonitis) appear interrelated, suggesting on-going bacterial challenges by pathogens such as Haemophilus parasuis and Streptococcus suis. Furthermore, hepatic scarring appears interrelated with both milk spot livers (Ascaris suum) and bacteria-related pathologies, suggesting a potential multi-pathogen nature for this pathology. The application of novel multi-dimensional machine learning methodology provided new insights into how typical pig pathologies are potentially interrelated at batch level. The methodology presented is a powerful exploratory tool to generate hypotheses, applicable to a wide range of studies in veterinary research.

Dreaming of a Learning Task is Associated with Enhanced Sleep-Dependent Memory Consolidation

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Wamsley, Erin J.; Tucker, Matthew; Payne, Jessica D.; Benavides, Joseph; Stickgold, Robert

2010-01-01

Summary It is now well established that post-learning sleep is beneficial for human memory performance [1–5]. Meanwhile, human and animal studies demonstrate that learning-related neural activity is re-expressed during post-training non-rapid eye movement sleep (NREM) [6–9]. NREM sleep processes appear to be particularly beneficial for hippocampus-dependent forms of memory [1–3, 10]. These observations suggest that learning triggers the reactivation and reorganization of memory traces during sleep, a systems-level process that in turn enhances behavioral performance. Here, we hypothesized that dreaming about a learning experience during NREM sleep would be associated with improved performance on a hippocampus-dependent spatial memory task. Subjects (n=99) were trained on a virtual navigation task, and then retested on the same task 5 hours after initial training. Improved performance at retest was strongly associated with task-related dream imagery during an intervening afternoon nap. Task-related thoughts during wakefulness, in contrast, did not predict improved performance. These observations suggest that sleep-dependent memory consolidation in humans is facilitated by the offline reactivation of recently formed memories, and furthermore, that dream experiences reflect this memory processing. That similar effects were not seen during wakefulness suggests that these mnemonic processes are specific to the sleep state. PMID:20417102

Tailor-made memory: natural differences in associative olfactory learning in two closely related wasp species

NARCIS (Netherlands)

Berg, van den M.

2009-01-01

Learning and memory formation are often seen as traits that are purely beneficial, but they are associated with metabolic costs as well. Since costs and gains of learning and memory are expected to vary between species, the ease and speed with which stable (consolidated) long-term memory (LTM) is

Association between learning style preferences and anatomy assessment outcomes in graduate-entry and undergraduate medical students.

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O'Mahony, Siobhain M; Sbayeh, Amgad; Horgan, Mary; O'Flynn, Siun; O'Tuathaigh, Colm M P

2016-07-08

An improved understanding of the relationship between anatomy learning performance and approaches to learning can lead to the development of a more tailored approach to delivering anatomy teaching to medical students. This study investigated the relationship between learning style preferences, as measured by Visual, Aural, Read/write, and Kinesthetic (VARK) inventory style questionnaire and Honey and Mumford's learning style questionnaire (LSQ), and anatomy and clinical skills assessment performance at an Irish medical school. Additionally, mode of entry to medical school [undergraduate/direct-entry (DEM) vs. graduate-entry (GEM)], was examined in relation to individual learning style, and assessment results. The VARK and LSQ were distributed to first and second year DEM, and first year GEM students. DEM students achieved higher clinical skills marks than GEM students, but anatomy marks did not differ between each group. Several LSQ style preferences were shown to be weakly correlated with anatomy assessment performance in a program- and year-specific manner. Specifically, the "Activist" style was negatively correlated with anatomy scores in DEM Year 2 students (rs = -0.45, P = 0.002). The "Theorist" style demonstrated a weak correlation with anatomy performance in DEM Year 2 (rs = 0.18, P = 0.003). Regression analysis revealed that, among the LSQ styles, the "Activist" was associated with poorer anatomy assessment performance (P learning styles contribute little to variation in academic performance in medical students. Anat Sci Educ 9: 391-399. © 2016 American Association of Anatomists. © 2016 American Association of Anatomists.

Characteristics of health care organizations associated with learning and development: lessons from a pilot study.

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Nyström, Monica

2009-01-01

Characteristics of health care organizations associated with an ability to learn from experiences and to develop and manage change were explored in this study. Understanding of these characteristics is necessary to identify factors influencing success in learning from the past and achieving future health care quality objectives. A literature review of the quality improvement, strategic organizational development and change management, organizational learning, and microsystems fields identified 20 organizational characteristics, grouped under (a) organizational systems, (b) key actors, and (c) change management processes. Qualitative methods, using interviews, focus group reports, and archival records, were applied to find associations between identified characteristics and 6 Swedish health care units externally evaluated as delivering high-quality care. Strong support for a characteristic was defined as units having more than 4 sources describing the characteristic as an important success factor. Eighteen characteristics had strong support from at least 2 units. The strongest evidence was found for the following: (i) key actors have long-term commitment, provide support, and make sense of ambiguous situations; (ii) organizational systems encourage employee commitment, participation, and involvement; and (iii) change management processes are employed systematically. Based on the results, a new model of "characteristics associated with learning and development in health care organizations" is proposed.

An associative model of adaptive inference for learning word-referent mappings.

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Kachergis, George; Yu, Chen; Shiffrin, Richard M

2012-04-01

People can learn word-referent pairs over a short series of individually ambiguous situations containing multiple words and referents (Yu & Smith, 2007, Cognition 106: 1558-1568). Cross-situational statistical learning relies on the repeated co-occurrence of words with their intended referents, but simple co-occurrence counts cannot explain the findings. Mutual exclusivity (ME: an assumption of one-to-one mappings) can reduce ambiguity by leveraging prior experience to restrict the number of word-referent pairings considered but can also block learning of non-one-to-one mappings. The present study first trained learners on one-to-one mappings with varying numbers of repetitions. In late training, a new set of word-referent pairs were introduced alongside pretrained pairs; each pretrained pair consistently appeared with a new pair. Results indicate that (1) learners quickly infer new pairs in late training on the basis of their knowledge of pretrained pairs, exhibiting ME; and (2) learners also adaptively relax the ME bias and learn two-to-two mappings involving both pretrained and new words and objects. We present an associative model that accounts for both results using competing familiarity and uncertainty biases.

An information processing/associative learning account of behavioral disinhibition in externalizing psychopathology.

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Endres, Michael J; Donkin, Chris; Finn, Peter R

2014-04-01

Externalizing psychopathology (EXT) is associated with low executive working memory (EWM) capacity and problems with inhibitory control and decision-making; however, the specific cognitive processes underlying these problems are not well known. This study used a linear ballistic accumulator computational model of go/no-go associative-incentive learning conducted with and without a working memory (WM) load to investigate these cognitive processes in 510 young adults varying in EXT (lifetime problems with substance use, conduct disorder, ADHD, adult antisocial behavior). High scores on an EXT factor were associated with low EWM capacity and higher scores on a latent variable reflecting the cognitive processes underlying disinhibited decision-making (more false alarms, faster evidence accumulation rates for false alarms [vFA], and lower scores on a Response Precision Index [RPI] measure of information processing efficiency). The WM load increased disinhibited decision-making, decisional uncertainty, and response caution for all subjects. Higher EWM capacity was associated with lower scores on the latent disinhibited decision-making variable (lower false alarms, lower vFAs and RPI scores) in both WM load conditions. EWM capacity partially mediated the association between EXT and disinhibited decision-making under no-WM load, and completely mediated this association under WM load. The results underline the role that EWM has in associative-incentive go/no-go learning and indicate that common to numerous types of EXT are impairments in the cognitive processes associated with the evidence accumulation-evaluation-decision process. PsycINFO Database Record (c) 2014 APA, all rights reserved.

Paired-Associate and Feedback-Based Weather Prediction Tasks Support Multiple Category Learning Systems.

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Li, Kaiyun; Fu, Qiufang; Sun, Xunwei; Zhou, Xiaoyan; Fu, Xiaolan

2016-01-01

It remains unclear whether probabilistic category learning in the feedback-based weather prediction task (FB-WPT) can be mediated by a non-declarative or procedural learning system. To address this issue, we compared the effects of training time and verbal working memory, which influence the declarative learning system but not the non-declarative learning system, in the FB and paired-associate (PA) WPTs, as the PA task recruits a declarative learning system. The results of Experiment 1 showed that the optimal accuracy in the PA condition was significantly decreased when the training time was reduced from 7 to 3 s, but this did not occur in the FB condition, although shortened training time impaired the acquisition of explicit knowledge in both conditions. The results of Experiment 2 showed that the concurrent working memory task impaired the optimal accuracy and the acquisition of explicit knowledge in the PA condition but did not influence the optimal accuracy or the acquisition of self-insight knowledge in the FB condition. The apparent dissociation results between the FB and PA conditions suggested that a non-declarative or procedural learning system is involved in the FB-WPT and provided new evidence for the multiple-systems theory of human category learning.

Rapid Associative Learning and Stable Long-Term Memory in the Squid Euprymna scolopes.

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Zepeda, Emily A; Veline, Robert J; Crook, Robyn J

2017-06-01

Learning and memory in cephalopod molluscs have received intensive study because of cephalopods' complex behavioral repertoire and relatively accessible nervous systems. While most of this research has been conducted using octopus and cuttlefish species, there has been relatively little work on squid. Euprymna scolopes Berry, 1913, a sepiolid squid, is a promising model for further exploration of cephalopod cognition. These small squid have been studied in detail for their symbiotic relationship with bioluminescent bacteria, and their short generation time and successful captive breeding through multiple generations make them appealing models for neurobiological research. However, little is known about their behavior or cognitive ability. Using the well-established "prawn-in-the-tube" assay of learning and memory, we show that within a single 10-min trial E. scolopes learns to inhibit its predatory behavior, and after three trials it can retain this memory for at least 12 d. Rapid learning and very long-term retention were apparent under two different training schedules. To our knowledge, this study is the first demonstration of learning and memory in this species as well as the first demonstration of associative learning in any squid.

Modeling eating behaviors: The role of environment and positive food association learning via a Ratatouille effect.

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Murillo, Anarina L; Safan, Muntaser; Castillo-Chavez, Carlos; Phillips, Elizabeth D Capaldi; Wadhera, Devina

2016-08-01

Eating behaviors among a large population of children are studied as a dynamic process driven by nonlinear interactions in the sociocultural school environment. The impact of food association learning on diet dynamics, inspired by a pilot study conducted among Arizona children in Pre-Kindergarten to 8th grades, is used to build simple population-level learning models. Qualitatively, mathematical studies are used to highlight the possible ramifications of instruction, learning in nutrition, and health at the community level. Model results suggest that nutrition education programs at the population-level have minimal impact on improving eating behaviors, findings that agree with prior field studies. Hence, the incorporation of food association learning may be a better strategy for creating resilient communities of healthy and non-healthy eaters. A Ratatouille effect can be observed when food association learners become food preference learners, a potential sustainable behavioral change, which in turn, may impact the overall distribution of healthy eaters. In short, this work evaluates the effectiveness of population-level intervention strategies and the importance of institutionalizing nutrition programs that factor in economical, social, cultural, and environmental elements that mesh well with the norms and values in the community.

Identifying associations between pig pathologies using a multi-dimensional machine learning methodology

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Sanchez-Vazquez Manuel J

2012-08-01

Full Text Available Abstract Background Abattoir detected pathologies are of crucial importance to both pig production and food safety. Usually, more than one pathology coexist in a pig herd although it often remains unknown how these different pathologies interrelate to each other. Identification of the associations between different pathologies may facilitate an improved understanding of their underlying biological linkage, and support the veterinarians in encouraging control strategies aimed at reducing the prevalence of not just one, but two or more conditions simultaneously. Results Multi-dimensional machine learning methodology was used to identify associations between ten typical pathologies in 6485 batches of slaughtered finishing pigs, assisting the comprehension of their biological association. Pathologies potentially associated with septicaemia (e.g. pericarditis, peritonitis appear interrelated, suggesting on-going bacterial challenges by pathogens such as Haemophilus parasuis and Streptococcus suis. Furthermore, hepatic scarring appears interrelated with both milk spot livers (Ascaris suum and bacteria-related pathologies, suggesting a potential multi-pathogen nature for this pathology. Conclusions The application of novel multi-dimensional machine learning methodology provided new insights into how typical pig pathologies are potentially interrelated at batch level. The methodology presented is a powerful exploratory tool to generate hypotheses, applicable to a wide range of studies in veterinary research.

Differential-associative processing or example elaboration: Which strategy is best for learning the definitions of related and unrelated concepts?

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Hannon, Brenda

2012-10-01

Definitions of related concepts (e.g., genotype - phenotype ) are prevalent in introductory classes. Consequently, it is important that educators and students know which strategy(s) work best for learning them. This study showed that a new comparative elaboration strategy, called differential-associative processing, was better for learning definitions of related concepts than was an integrative elaborative strategy, called example elaboration. This outcome occurred even though example elaboration was administered in a naturalistic way (Experiment 1) and students spent more time in the example elaboration condition learning (Experiments 1, 2, 3), and generating pieces of information about the concepts (Experiments 2 and 3). Further, with unrelated concepts ( morpheme-fluid intelligence ), performance was similar regardless if students used differential-associative processing or example elaboration (Experiment 3). Taken as a whole, these results suggest that differential-associative processing is better than example elaboration for learning definitions of related concepts and is as good as example elaboration for learning definitions of unrelated concepts.

Two Ways of Learning Brand Associations

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S.M.J. van Osselaer (Stijn); C. Janiszewski (Chris)

2001-01-01

textabstractFour studies show that consumers have not one but two distinct learning processes that allow them to use brand names and other product features to predict consumption benefits. The first learning process is a relatively unfocused process in which all stimulus elements get

Confidence-Based Data Association and Discriminative Deep Appearance Learning for Robust Online Multi-Object Tracking.

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Bae, Seung-Hwan; Yoon, Kuk-Jin

2018-03-01

Online multi-object tracking aims at estimating the tracks of multiple objects instantly with each incoming frame and the information provided up to the moment. It still remains a difficult problem in complex scenes, because of the large ambiguity in associating multiple objects in consecutive frames and the low discriminability between objects appearances. In this paper, we propose a robust online multi-object tracking method that can handle these difficulties effectively. We first define the tracklet confidence using the detectability and continuity of a tracklet, and decompose a multi-object tracking problem into small subproblems based on the tracklet confidence. We then solve the online multi-object tracking problem by associating tracklets and detections in different ways according to their confidence values. Based on this strategy, tracklets sequentially grow with online-provided detections, and fragmented tracklets are linked up with others without any iterative and expensive association steps. For more reliable association between tracklets and detections, we also propose a deep appearance learning method to learn a discriminative appearance model from large training datasets, since the conventional appearance learning methods do not provide rich representation that can distinguish multiple objects with large appearance variations. In addition, we combine online transfer learning for improving appearance discriminability by adapting the pre-trained deep model during online tracking. Experiments with challenging public datasets show distinct performance improvement over other state-of-the-arts batch and online tracking methods, and prove the effect and usefulness of the proposed methods for online multi-object tracking.

Fusing Data Mining, Machine Learning and Traditional Statistics to Detect Biomarkers Associated with Depression

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Dipnall, Joanna F.; Pasco, Julie A.; Berk, Michael; Williams, Lana J.; Dodd, Seetal; Jacka, Felice N.; Meyer, Denny

2016-01-01

Background Atheoretical large-scale data mining techniques using machine learning algorithms have promise in the analysis of large epidemiological datasets. This study illustrates the use of a hybrid methodology for variable selection that took account of missing data and complex survey design to identify key biomarkers associated with depression from a large epidemiological study. Methods The study used a three-step methodology amalgamating multiple imputation, a machine learning boosted reg...

Children Learn Spurious Associations in Their Math Textbooks: Examples from Fraction Arithmetic

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Braithwaite, David W.; Siegler, Robert S.

2018-01-01

Fraction arithmetic is among the most important and difficult topics children encounter in elementary and middle school mathematics. Braithwaite, Pyke, and Siegler (2017) hypothesized that difficulties learning fraction arithmetic often reflect reliance on associative knowledge--rather than understanding of mathematical concepts and procedures--to…

Functional model of biological neural networks.

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Lo, James Ting-Ho

2010-12-01

A functional model of biological neural networks, called temporal hierarchical probabilistic associative memory (THPAM), is proposed in this paper. THPAM comprises functional models of dendritic trees for encoding inputs to neurons, a first type of neuron for generating spike trains, a second type of neuron for generating graded signals to modulate neurons of the first type, supervised and unsupervised Hebbian learning mechanisms for easy learning and retrieving, an arrangement of dendritic trees for maximizing generalization, hardwiring for rotation-translation-scaling invariance, and feedback connections with different delay durations for neurons to make full use of present and past informations generated by neurons in the same and higher layers. These functional models and their processing operations have many functions of biological neural networks that have not been achieved by other models in the open literature and provide logically coherent answers to many long-standing neuroscientific questions. However, biological justifications of these functional models and their processing operations are required for THPAM to qualify as a macroscopic model (or low-order approximate) of biological neural networks.

Fusing Data Mining, Machine Learning and Traditional Statistics to Detect Biomarkers Associated with Depression.

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Dipnall, Joanna F; Pasco, Julie A; Berk, Michael; Williams, Lana J; Dodd, Seetal; Jacka, Felice N; Meyer, Denny

2016-01-01

Atheoretical large-scale data mining techniques using machine learning algorithms have promise in the analysis of large epidemiological datasets. This study illustrates the use of a hybrid methodology for variable selection that took account of missing data and complex survey design to identify key biomarkers associated with depression from a large epidemiological study. The study used a three-step methodology amalgamating multiple imputation, a machine learning boosted regression algorithm and logistic regression, to identify key biomarkers associated with depression in the National Health and Nutrition Examination Study (2009-2010). Depression was measured using the Patient Health Questionnaire-9 and 67 biomarkers were analysed. Covariates in this study included gender, age, race, smoking, food security, Poverty Income Ratio, Body Mass Index, physical activity, alcohol use, medical conditions and medications. The final imputed weighted multiple logistic regression model included possible confounders and moderators. After the creation of 20 imputation data sets from multiple chained regression sequences, machine learning boosted regression initially identified 21 biomarkers associated with depression. Using traditional logistic regression methods, including controlling for possible confounders and moderators, a final set of three biomarkers were selected. The final three biomarkers from the novel hybrid variable selection methodology were red cell distribution width (OR 1.15; 95% CI 1.01, 1.30), serum glucose (OR 1.01; 95% CI 1.00, 1.01) and total bilirubin (OR 0.12; 95% CI 0.05, 0.28). Significant interactions were found between total bilirubin with Mexican American/Hispanic group (p = 0.016), and current smokers (pmachine learning algorithm with traditional statistical modelling, accounted for missing data and complex survey sampling methodology and was demonstrated to be a useful tool for detecting three biomarkers associated with depression for future

Initial investigation of the effects of an experimentally learned schema on spatial associative memory in humans.

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van Buuren, Mariët; Kroes, Marijn C W; Wagner, Isabella C; Genzel, Lisa; Morris, Richard G M; Fernández, Guillén

2014-12-10

Networks of interconnected neocortical representations of prior knowledge, "schemas," facilitate memory for congruent information. This facilitation is thought to be mediated by augmented encoding and accelerated consolidation. However, it is less clear how schema affects retrieval. Rodent and human studies to date suggest that schema-related memories are differently retrieved. However, these studies differ substantially as most human studies implement pre-experimental world-knowledge as schemas and tested item or nonspatial associative memory, whereas animal studies have used intraexperimental schemas based on item-location associations within a complex spatial layout that, in humans, could engage more strategic retrieval processes. Here, we developed a paradigm conceptually linked to rodent studies to examine the effects of an experimentally learned spatial associative schema on learning and retrieval of new object-location associations and to investigate the neural mechanisms underlying schema-related retrieval. Extending previous findings, we show that retrieval of schema-defining associations is related to activity along anterior and posterior midline structures and angular gyrus. The existence of such spatial associative schema resulted in more accurate learning and retrieval of new, related associations, and increased time allocated to retrieve these associations. This retrieval was associated with right dorsolateral prefrontal and lateral parietal activity, as well as interactions between the right dorsolateral prefrontal cortex and medial and lateral parietal regions, and between the medial prefrontal cortex and posterior midline regions, supporting the hypothesis that retrieval of new, schema-related object-location associations in humans also involves augmented monitoring and systematic search processes. Copyright © 2014 the authors 0270-6474/14/3416662-09$15.00/0.

The Value of E-Learning for the Prevention of Healthcare-Associated Infections.

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Labeau, Sonia O; Rello, Jordi; Dimopoulos, George; Lipman, Jeffrey; Sarikaya, Aklime; Oztürk, Candan; Vandijck, Dominique M; Vogelaers, Dirk; Vandewoude, Koenraad; Blot, Stijn I

2016-09-01

BACKGROUND Healthcare workers (HCWs) lack familiarity with evidence-based guidelines for the prevention of healthcare-associated infections (HAIs). There is good evidence that effective educational interventions help to facilitate guideline implementation, so we investigated whether e-learning could enhance HCW knowledge of HAI prevention guidelines. METHODS We developed an electronic course (e-course) and tested its usability and content validity. An international sample of voluntary learners submitted to a pretest (T0) that determined their baseline knowledge of guidelines, and they subsequently studied the e-course. Immediately after studying the course, posttest 1 (T1) assessed the immediate learning effect. After 3 months, during which participants had no access to the course, a second posttest (T2) evaluated the residual learning effect. RESULTS A total of 3,587 HCWs representing 79 nationalities enrolled: 2,590 HCWs (72%) completed T0; 1,410 HCWs (39%) completed T1; and 1,011 HCWs (28%) completed T2. The median study time was 193 minutes (interquartile range [IQR], 96-306 minutes) The median scores were 52% (IQR, 44%-62%) for T0, 80% (IQR, 68%-88%) for T1, and 74% (IQR, 64%-84%) for T2. The immediate learning effect (T0 vs T1) was +24% (IQR, 12%-34%; P300 minutes yielded the greatest residual effect (24%). CONCLUSIONS Moderate time invested in e-learning yielded significant immediate and residual learning effects. Decision makers could consider promoting e-learning as a supporting tool in HAI prevention. Infect Control Hosp Epidemiol 2016;37:1052-1059.

Sleep parameters, functional status and time post-stroke are associated with off-line motor skill learning in people with chronic stroke

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Catherine eSiengsukon

2015-10-01

Full Text Available Background: Mounting evidence demonstrates that individuals with stroke benefit from sleep to enhance learning of a motor task. While stage NREM2 sleep and REM sleep have been associated with off-line motor skill learning in neurologically-intact individuals, it remains unknown which sleep parameters or specific sleep stages are associated with off-line motor skill learning in individuals with stroke. Methods: Twenty individuals with chronic stroke (> 6 months following stroke and 10 neurologically slept for three consecutive nights in a sleep laboratory with polysomnography. Participants practiced a tracking task the morning before the third night and underwent a retention test the morning following the third night. Off-line learning on the tracking task was assessed. Pearson’s correlations assessed for associations between the magnitude of off-line learning and sleep variables, age, upper extremity motor function, stroke severity, depression and time since stroke occurrence.Results: Individuals with stroke performed with significantly less error on the tracking task following a night of sleep (p=.006 while the control participants did not (p=.816. Increased sleep efficiency (r= -.285, less time spent in stage NREM3 sleep (r=.260, and more time spent in stage REM sleep (r= -.266 was weakly-to-moderately associated with increased magnitude of off-line motor learning. Furthermore, higher upper-extremity motor function (r = -.400, lower stroke severity (r = .360, and less time since stroke occurrence (r=.311 were moderately associated with increased magnitude of off-line motor learning. Conclusion: This study is the first study to provide insight into which sleep stages and individual characteristics may be associated with off-line learning in people with stroke. Future work should continue to understand which factors or combination of factors promote off-line motor learning in people with neurologic injury to best promote motor recovery in

Fast But Fleeting: Adaptive Motor Learning Processes Associated with Aging and Cognitive Decline

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Trewartha, Kevin M.; Garcia, Angeles; Wolpert, Daniel M.

2014-01-01

Motor learning has been shown to depend on multiple interacting learning processes. For example, learning to adapt when moving grasped objects with novel dynamics involves a fast process that adapts and decays quickly—and that has been linked to explicit memory—and a slower process that adapts and decays more gradually. Each process is characterized by a learning rate that controls how strongly motor memory is updated based on experienced errors and a retention factor determining the movement-to-movement decay in motor memory. Here we examined whether fast and slow motor learning processes involved in learning novel dynamics differ between younger and older adults. In addition, we investigated how age-related decline in explicit memory performance influences learning and retention parameters. Although the groups adapted equally well, they did so with markedly different underlying processes. Whereas the groups had similar fast processes, they had different slow processes. Specifically, the older adults exhibited decreased retention in their slow process compared with younger adults. Within the older group, who exhibited considerable variation in explicit memory performance, we found that poor explicit memory was associated with reduced retention in the fast process, as well as the slow process. These findings suggest that explicit memory resources are a determining factor in impairments in the both the fast and slow processes for motor learning but that aging effects on the slow process are independent of explicit memory declines. PMID:25274819

Fast but fleeting: adaptive motor learning processes associated with aging and cognitive decline.

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Trewartha, Kevin M; Garcia, Angeles; Wolpert, Daniel M; Flanagan, J Randall

2014-10-01

Motor learning has been shown to depend on multiple interacting learning processes. For example, learning to adapt when moving grasped objects with novel dynamics involves a fast process that adapts and decays quickly-and that has been linked to explicit memory-and a slower process that adapts and decays more gradually. Each process is characterized by a learning rate that controls how strongly motor memory is updated based on experienced errors and a retention factor determining the movement-to-movement decay in motor memory. Here we examined whether fast and slow motor learning processes involved in learning novel dynamics differ between younger and older adults. In addition, we investigated how age-related decline in explicit memory performance influences learning and retention parameters. Although the groups adapted equally well, they did so with markedly different underlying processes. Whereas the groups had similar fast processes, they had different slow processes. Specifically, the older adults exhibited decreased retention in their slow process compared with younger adults. Within the older group, who exhibited considerable variation in explicit memory performance, we found that poor explicit memory was associated with reduced retention in the fast process, as well as the slow process. These findings suggest that explicit memory resources are a determining factor in impairments in the both the fast and slow processes for motor learning but that aging effects on the slow process are independent of explicit memory declines. Copyright © 2014 the authors 0270-6474/14/3413411-11$15.00/0.

Does academic performance or personal growth share a stronger association with learning environment perception?

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Tackett, Sean; Wright, Scott M.; Shochet, Robert S.

2016-01-01

Objectives This study was conducted to characterize the relative strength of associations of learning environment perception with academic performance and with personal growth. Methods In 2012-2014 second and third year students at Johns Hopkins University School of Medicine completed a learning environment survey and personal growth scale. Hierarchical linear regression analysis was employed to determine if the proportion of variance in learning environment scores accounted for by personal growth was significantly larger than the proportion accounted for by academic performance (course/clerkship grades). Results The proportion of variance in learning environment scores accounted for by personal growth was larger than the proportion accounted for by academic performance in year 2 [R2Δ of 0.09, F(1,175) = 14.99,  p environment scores shared a small amount of variance with academic performance in years 2 and 3.  The amount of variance between learning environment scores and personal growth was small in year 2 and large in year 3. Conclusions Since supportive learning environments are essential for medical education, future work must determine if enhancing personal growth prior to and during the clerkship year will increase learning environment perception. PMID:27570912

Importance of associative learning processes for one-trial behavioral sensitization of preweanling rats.

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McDougall, Sanders A; Pothier, Alexandria G; Der-Ghazarian, Taleen; Herbert, Matthew S; Kozanian, Olga O; Castellanos, Kevin A; Flores, Ana T

2011-10-01

During adulthood, associative learning is necessary for the expression of one-trial behavioral sensitization; however, it is uncertain whether the same associative processes are operative during the preweanling period. Two strategies were used to assess the importance of associative learning for one-trial behavioral sensitization of preweanling rats. In the initial experiments, we varied both the sequence and time interval between presentation of the conditioned stimulus (CS, novel environment) and unconditioned stimulus (US, cocaine). In the final experiment, we determined whether electroconvulsive shock-induced retrograde amnesia would disrupt one-trial behavioral sensitization. Results showed that robust-sensitized responding was apparent regardless of the sequence in which cocaine and the novel environment (the presumptive CS) were presented. Varying the time between CS and US presentation (0, 3, or 6 h) was also without effect. Results from experiment 3 showed that single or multiple electroconvulsive shock treatments did not alter the expression of the sensitized response. Therefore, these data indicated that one-trial behavioral sensitization of preweanling rats was exclusively mediated by nonassociative mechanisms and that associative processes did not modulate sensitized responding. These findings are in contrast to what is observed during adulthood, as adult rats exhibit one-trial behavioral sensitization only when associative processes are operative.

Phoneme Awareness, Visual-Verbal Paired-Associate Learning, and Rapid Automatized Naming as Predictors of Individual Differences in Reading Ability

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Warmington, Meesha; Hulme, Charles

2012-01-01

This study examines the concurrent relationships between phoneme awareness, visual-verbal paired-associate learning, rapid automatized naming (RAN), and reading skills in 7- to 11-year-old children. Path analyses showed that visual-verbal paired-associate learning and RAN, but not phoneme awareness, were unique predictors of word recognition,…

Performance monitoring during associative learning and its relation to obsessive-compulsive characteristics.

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Doñamayor, Nuria; Dinani, Jakob; Römisch, Manuel; Ye, Zheng; Münte, Thomas F

2014-10-01

Neural responses to performance errors and external feedback have been suggested to be altered in obsessive-compulsive disorder. In the current study, an associative learning task was used in healthy participants assessed for obsessive-compulsive symptoms by the OCI-R questionnaire. The task included a condition with equivocal feedback that did not inform about the participants' performance. Following incorrect responses, an error-related negativity and an error positivity were observed. In the feedback phase, the largest feedback-related negativity was observed following equivocal feedback. Theta and beta oscillatory components were found following incorrect and correct responses, respectively, and an increase in theta power was associated with negative and equivocal feedback. Changes over time were also explored as an indicator for possible learning effects. Finally, event-related potentials and oscillatory components were found to be uncorrelated with OCI-R scores in the current non-clinical sample. Copyright © 2014 Elsevier B.V. All rights reserved.

Learning Curve Characteristics for Caesarean Section Among Associate Clinicians : A Prospective Study from Sierra Leone

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Waalewijn, B.P.; van Duinen, A.; Koroma, A. P.; Rijken, M. J.; Elhassein, M.; Bolkan, H. A.

2017-01-01

Background: In response to the high maternal mortality ratio, Sierra Leone has adopted an associate clinician postgraduate surgical task-sharing training programme. Little is known about learning curve characteristics for caesarean sections among associate clinicians. The aim of this study is to

Towards a general theory of neural computation based on prediction by single neurons.

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Christopher D Fiorillo

Full Text Available Although there has been tremendous progress in understanding the mechanics of the nervous system, there has not been a general theory of its computational function. Here I present a theory that relates the established biophysical properties of single generic neurons to principles of Bayesian probability theory, reinforcement learning and efficient coding. I suggest that this theory addresses the general computational problem facing the nervous system. Each neuron is proposed to mirror the function of the whole system in learning to predict aspects of the world related to future reward. According to the model, a typical neuron receives current information about the state of the world from a subset of its excitatory synaptic inputs, and prior information from its other inputs. Prior information would be contributed by synaptic inputs representing distinct regions of space, and by different types of non-synaptic, voltage-regulated channels representing distinct periods of the past. The neuron's membrane voltage is proposed to signal the difference between current and prior information ("prediction error" or "surprise". A neuron would apply a Hebbian plasticity rule to select those excitatory inputs that are the most closely correlated with reward but are the least predictable, since unpredictable inputs provide the neuron with the most "new" information about future reward. To minimize the error in its predictions and to respond only when excitation is "new and surprising," the neuron selects amongst its prior information sources through an anti-Hebbian rule. The unique inputs of a mature neuron would therefore result from learning about spatial and temporal patterns in its local environment, and by extension, the external world. Thus the theory describes how the structure of the mature nervous system could reflect the structure of the external world, and how the complexity and intelligence of the system might develop from a population of

The Association between Students' Style of Learning Preferences, Social Presence, Collaborative Learning and Learning Outcomes

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Chen, Clement; Jones, Keith T.; Xu, Shawn

2018-01-01

Differences in styles of learning have become important considerations at all levels of education over the last several years. Examining college students' preferred style of learning is useful for course design and effective instructional methods. Using the Felder-Silverman Index of Learning Styles (ILS), we investigate how students' styles of…

University of Central Florida and the American Association of State Colleges and Universities: Blended Learning Toolkit

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EDUCAUSE, 2014

2014-01-01

The Blended Learning Toolkit supports the course redesign approach, and interest in its openly available clearinghouse of online tools, strategies, curricula, and other materials to support the adoption of blended learning continues to grow. When the resource originally launched in July 2011, 20 AASCU [American Association of State Colleges and…

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

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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.

A process-based approach to characterizing the effect of acute alprazolam challenge on visual paired associate learning and memory in healthy older adults.

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Pietrzak, Robert H; Scott, James Cobb; Harel, Brian T; Lim, Yen Ying; Snyder, Peter J; Maruff, Paul

2012-11-01

Alprazolam is a benzodiazepine that, when administered acutely, results in impairments in several aspects of cognition, including attention, learning, and memory. However, the profile (i.e., component processes) that underlie alprazolam-related decrements in visual paired associate learning has not been fully explored. In this double-blind, placebo-controlled, randomized cross-over study of healthy older adults, we used a novel, "process-based" computerized measure of visual paired associate learning to examine the effect of a single, acute 1-mg dose of alprazolam on component processes of visual paired associate learning and memory. Acute alprazolam challenge was associated with a large magnitude reduction in visual paired associate learning and memory performance (d = 1.05). Process-based analyses revealed significant increases in distractor, exploratory, between-search, and within-search error types. Analyses of percentages of each error type suggested that, relative to placebo, alprazolam challenge resulted in a decrease in the percentage of exploratory errors and an increase in the percentage of distractor errors, both of which reflect memory processes. Results of this study suggest that acute alprazolam challenge decreases visual paired associate learning and memory performance by reducing the strength of the association between pattern and location, which may reflect a general breakdown in memory consolidation, with less evidence of reductions in executive processes (e.g., working memory) that facilitate visual paired associate learning and memory. Copyright © 2012 John Wiley & Sons, Ltd.

Impaired associative fear learning in mice with complete loss or haploinsufficiency of AMPA GluR1 receptors

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Michael Feyder

2007-12-01

Full Text Available There is compelling evidence that L-alpha-amino-3-hydroxy-5-methylisoxazole-4-propionate (AMPA glutamate receptors containing the GluR1 subunit contribute to the molecular mechanisms associated with learning. AMPA GluR1 glutamate receptor knockout mice (KO exhibit abnormal hippocampal and amygdala plasticity, and deficits on various assays for cognition including Pavlovian fear conditioning. Here we examined associative fear learning in mice with complete absence (KO or partial loss (heterozygous mutant, HET of GluR1 on multiple fear conditioning paradigms. After multi-trial delay or trace conditioning, KO displayed impaired tone and context fear recall relative to WT, whereas HET were normal. After one-trial delay conditioning, both KO and HET showed impaired tone and context recall. HET and KO showed normal nociceptive sensitivity in the hot plate and tail flick tests. These data demonstrate that the complete absence of GluR1 subunit-containing receptors prevents the formation of associative fear memories, while GluR1 haploinsufficiency is sufficient to impair one-trial fear learning. These findings support growing evidence of a major role for GluR1-containing AMPA receptors in amygdalamediated forms of learning and memory.

A confrontation with reality - Proceedings of the 19th Association for Learning Technology Conference

NARCIS (Netherlands)

Hawkridge, David; Verjans, Steven; Wilson, Gail

2012-01-01

Hawkridge, D., Verjans, S., & Wilson, G. (Eds.) (2012). A confrontation with reality - Proceedings of the 19th Association for Learning Technology Conference (ALT-C 2012). September, 11-14, 2012, Manchester, UK.

Precise-spike-driven synaptic plasticity: learning hetero-association of spatiotemporal spike patterns.

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Qiang Yu

Full Text Available A new learning rule (Precise-Spike-Driven (PSD Synaptic Plasticity is proposed for processing and memorizing spatiotemporal patterns. PSD is a supervised learning rule that is analytically derived from the traditional Widrow-Hoff rule and can be used to train neurons to associate an input spatiotemporal spike pattern with a desired spike train. Synaptic adaptation is driven by the error between the desired and the actual output spikes, with positive errors causing long-term potentiation and negative errors causing long-term depression. The amount of modification is proportional to an eligibility trace that is triggered by afferent spikes. The PSD rule is both computationally efficient and biologically plausible. The properties of this learning rule are investigated extensively through experimental simulations, including its learning performance, its generality to different neuron models, its robustness against noisy conditions, its memory capacity, and the effects of its learning parameters. Experimental results show that the PSD rule is capable of spatiotemporal pattern classification, and can even outperform a well studied benchmark algorithm with the proposed relative confidence criterion. The PSD rule is further validated on a practical example of an optical character recognition problem. The results again show that it can achieve a good recognition performance with a proper encoding. Finally, a detailed discussion is provided about the PSD rule and several related algorithms including tempotron, SPAN, Chronotron and ReSuMe.

Precise-spike-driven synaptic plasticity: learning hetero-association of spatiotemporal spike patterns.

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Yu, Qiang; Tang, Huajin; Tan, Kay Chen; Li, Haizhou

2013-01-01

A new learning rule (Precise-Spike-Driven (PSD) Synaptic Plasticity) is proposed for processing and memorizing spatiotemporal patterns. PSD is a supervised learning rule that is analytically derived from the traditional Widrow-Hoff rule and can be used to train neurons to associate an input spatiotemporal spike pattern with a desired spike train. Synaptic adaptation is driven by the error between the desired and the actual output spikes, with positive errors causing long-term potentiation and negative errors causing long-term depression. The amount of modification is proportional to an eligibility trace that is triggered by afferent spikes. The PSD rule is both computationally efficient and biologically plausible. The properties of this learning rule are investigated extensively through experimental simulations, including its learning performance, its generality to different neuron models, its robustness against noisy conditions, its memory capacity, and the effects of its learning parameters. Experimental results show that the PSD rule is capable of spatiotemporal pattern classification, and can even outperform a well studied benchmark algorithm with the proposed relative confidence criterion. The PSD rule is further validated on a practical example of an optical character recognition problem. The results again show that it can achieve a good recognition performance with a proper encoding. Finally, a detailed discussion is provided about the PSD rule and several related algorithms including tempotron, SPAN, Chronotron and ReSuMe.

The Smart Gut: Tracking Affective Associative Learning with Measures of "Liking", Facial Electromyography, and Preferential Looking

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Armel, K. Carrie; Pulido, Carmen; Wixted, John T.; Chiba, Andrea A.

2009-01-01

We demonstrate here that initially neutral items can acquire "specific" value based on their associated outcomes, and that responses of physiological systems to such previously meaningless stimuli can rapidly reflect this associative history. Each participant participated in an associative learning task in which four neutral abstract pictures were…

Assessing patient risk of central line-associated bacteremia via machine learning.

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Beeler, Cole; Dbeibo, Lana; Kelley, Kristen; Thatcher, Levi; Webb, Douglas; Bah, Amadou; Monahan, Patrick; Fowler, Nicole R; Nicol, Spencer; Judy-Malcolm, Alisa; Azar, Jose

2018-04-13

Central line-associated bloodstream infections (CLABSIs) contribute to increased morbidity, length of hospital stay, and cost. Despite progress in understanding the risk factors, there remains a need to accurately predict the risk of CLABSIs and, in real time, prevent them from occurring. A predictive model was developed using retrospective data from a large academic healthcare system. Models were developed with machine learning via construction of random forests using validated input variables. Fifteen variables accounted for the most significant effect on CLABSI prediction based on a retrospective study of 70,218 unique patient encounters between January 1, 2013, and May 31, 2016. The area under the receiver operating characteristic curve for the best-performing model was 0.82 in production. This model has multiple applications for resource allocation for CLABSI prevention, including serving as a tool to target patients at highest risk for potentially cost-effective but otherwise time-limited interventions. Machine learning can be used to develop accurate models to predict the risk of CLABSI in real time prior to the development of infection. Copyright © 2018 Association for Professionals in Infection Control and Epidemiology, Inc. Published by Elsevier Inc. All rights reserved.

Fusing Data Mining, Machine Learning and Traditional Statistics to Detect Biomarkers Associated with Depression

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Dipnall, Joanna F.

2016-01-01

Background Atheoretical large-scale data mining techniques using machine learning algorithms have promise in the analysis of large epidemiological datasets. This study illustrates the use of a hybrid methodology for variable selection that took account of missing data and complex survey design to identify key biomarkers associated with depression from a large epidemiological study. Methods The study used a three-step methodology amalgamating multiple imputation, a machine learning boosted regression algorithm and logistic regression, to identify key biomarkers associated with depression in the National Health and Nutrition Examination Study (2009–2010). Depression was measured using the Patient Health Questionnaire-9 and 67 biomarkers were analysed. Covariates in this study included gender, age, race, smoking, food security, Poverty Income Ratio, Body Mass Index, physical activity, alcohol use, medical conditions and medications. The final imputed weighted multiple logistic regression model included possible confounders and moderators. Results After the creation of 20 imputation data sets from multiple chained regression sequences, machine learning boosted regression initially identified 21 biomarkers associated with depression. Using traditional logistic regression methods, including controlling for possible confounders and moderators, a final set of three biomarkers were selected. The final three biomarkers from the novel hybrid variable selection methodology were red cell distribution width (OR 1.15; 95% CI 1.01, 1.30), serum glucose (OR 1.01; 95% CI 1.00, 1.01) and total bilirubin (OR 0.12; 95% CI 0.05, 0.28). Significant interactions were found between total bilirubin with Mexican American/Hispanic group (p = 0.016), and current smokers (p<0.001). Conclusion The systematic use of a hybrid methodology for variable selection, fusing data mining techniques using a machine learning algorithm with traditional statistical modelling, accounted for missing data and

Fusing Data Mining, Machine Learning and Traditional Statistics to Detect Biomarkers Associated with Depression.

Directory of Open Access Journals (Sweden)

Joanna F Dipnall

Full Text Available Atheoretical large-scale data mining techniques using machine learning algorithms have promise in the analysis of large epidemiological datasets. This study illustrates the use of a hybrid methodology for variable selection that took account of missing data and complex survey design to identify key biomarkers associated with depression from a large epidemiological study.The study used a three-step methodology amalgamating multiple imputation, a machine learning boosted regression algorithm and logistic regression, to identify key biomarkers associated with depression in the National Health and Nutrition Examination Study (2009-2010. Depression was measured using the Patient Health Questionnaire-9 and 67 biomarkers were analysed. Covariates in this study included gender, age, race, smoking, food security, Poverty Income Ratio, Body Mass Index, physical activity, alcohol use, medical conditions and medications. The final imputed weighted multiple logistic regression model included possible confounders and moderators.After the creation of 20 imputation data sets from multiple chained regression sequences, machine learning boosted regression initially identified 21 biomarkers associated with depression. Using traditional logistic regression methods, including controlling for possible confounders and moderators, a final set of three biomarkers were selected. The final three biomarkers from the novel hybrid variable selection methodology were red cell distribution width (OR 1.15; 95% CI 1.01, 1.30, serum glucose (OR 1.01; 95% CI 1.00, 1.01 and total bilirubin (OR 0.12; 95% CI 0.05, 0.28. Significant interactions were found between total bilirubin with Mexican American/Hispanic group (p = 0.016, and current smokers (p<0.001.The systematic use of a hybrid methodology for variable selection, fusing data mining techniques using a machine learning algorithm with traditional statistical modelling, accounted for missing data and complex survey sampling

Sleep spindle-related reactivation of category-specific cortical regions after learning face-scene associations

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Bergmann, Til O; Mölle, Matthias; Diedrichs, Jens

2012-01-01

Newly acquired declarative memory traces are believed to be reactivated during NonREM sleep to promote their hippocampo-neocortical transfer for long-term storage. Yet it remains a major challenge to unravel the underlying neuronal mechanisms. Using simultaneous electroencephalography (EEG......-coupled reactivation of brain regions representing the specific task stimuli was traced during subsequent NonREM sleep with EEG-informed fMRI. Relative to the control task, learning face-scene associations triggered a stronger combined activation of neocortical and hippocampal regions during subsequent sleep. Notably......) and functional magnetic resonance imaging (fMRI) recordings in humans, we show that sleep spindles play a key role in the reactivation of memory-related neocortical representations. On separate days, participants either learned face-scene associations or performed a visuomotor control task. Spindle...

Use of the Learning together technique associated to the theory of significative learning

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Ester López Donoso

2008-09-01

Full Text Available This article deals with an experimental research, regarding a qualitative and quantitative design, applied to a group of students of General Physics course during the first semester of the university career of Engineering. Historically, students of this course present learning difficulties that directly affect their performance, conceptualization and permanence in the university. The present methodology integrates the collaborative learning, denominated Learning Together", with the theory of significant learning to avoid the above-written difficulties. Results of this research show that the proposed methodology works properly, especially to improve the conceptualization.

Information processing in illness representation: Implications from an associative-learning framework.

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Lowe, Rob; Norman, Paul

2017-03-01

The common-sense model (Leventhal, Meyer, & Nerenz, 1980) outlines how illness representations are important for understanding adjustment to health threats. However, psychological processes giving rise to these representations are little understood. To address this, an associative-learning framework was used to model low-level process mechanics of illness representation and coping-related decision making. Associative learning was modeled within a connectionist network simulation. Two types of information were paired: Illness identities (indigestion, heart attack, cancer) were paired with illness-belief profiles (cause, timeline, consequences, control/cure), and specific illness beliefs were paired with coping procedures (family doctor, emergency services, self-treatment). To emulate past experience, the network was trained with these pairings. As an analogue of a current illness event, the trained network was exposed to partial information (illness identity or select representation beliefs) and its response recorded. The network (a) produced the appropriate representation profile (beliefs) for a given illness identity, (b) prioritized expected coping procedures, and (c) highlighted circumstances in which activated representation profiles could include self-generated or counterfactual beliefs. Encoding and activation of illness beliefs can occur spontaneously and automatically; conventional questionnaire measurement may be insensitive to these automatic representations. Furthermore, illness representations may comprise a coherent set of nonindependent beliefs (a schema) rather than a collective of independent beliefs. Incoming information may generate a "tipping point," dramatically changing the active schema as a new illness-knowledge set is invoked. Finally, automatic activation of well-learned information can lead to the erroneous interpretation of illness events, with implications for [inappropriate] coping efforts. (PsycINFO Database Record (c) 2017 APA, all

Stachys sieboldii (Labiatae, Chorogi) Protects against Learning and Memory Dysfunction Associated with Ischemic Brain Injury.

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Harada, Shinichi; Tsujita, Tsukasa; Ono, Akiko; Miyagi, Kei; Mori, Takaharu; Tokuyama, Shogo

2015-01-01

Stachys sieboldii (Labiatae; Chinese artichoke, a tuber), "chorogi" in Japanese, has been extensively used in folk medicine, and has a number of pharmacological properties, including antioxidative activity. However, few studies have examined the neuroprotective effects of S. sieboldii tuber extract (chorogi extract), and it remains unknown whether the extract can alleviate learning and memory dysfunction associated with vascular dementia or Alzheimer's disease. Therefore, in this study, we investigated the neuroprotective effects of chorogi extract, and examined its protection against learning and memory dysfunction using Ginkgo biloba leaf extract (ginkgo extract) as a positive control. Mice were subjected to bilateral carotid artery occlusion (BCAO) for 30 min. Oral administration of chorogi extract or ginkgo extract significantly reduced post-ischemic glucose intolerance on day 1 and neuronal damage including memory impairment on day 3 after BCAO, compared with the vehicle-treated group. Neither herbal medicine affected locomotor activity. Furthermore, neither significantly alleviated scopolamine-induced learning and memory impairment. In primary neurons, neuronal survival rate was significantly reduced by hydrogen peroxide treatment. This hydrogen peroxide-induced neurotoxicity was significantly suppressed by chorogi extract and ginkgo extract. Taken together, our findings suggest that chorogi extract as well as ginkgo extract can protect against learning and memory dysfunction associated with ischemic brain injury through an antioxidative mechanism.

Learning Styles.

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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…

Beliefs Associated with Support for Child-Centred Learning Environment among Hong Kong Pre-Service Early Childhood Teachers

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Cheung, Sum Kwing; Ling, Elsa Ka-wei; Leung, Suzannie Kit Ying

2017-01-01

The physical, social and temporal dimensions of the classroom environment have an important role in children's learning. This study examines the level of support for child-centred learning, and its associated beliefs, that is provided by Hong Kong's pre-service early childhood teachers. Two hundred and seventy-five students from a pre-service…

Social influence on associative learning: double dissociation in high-functioning autism, early-stage behavioural variant frontotemporal dementia and Alzheimer's disease.

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Kéri, Szabolcs

2014-05-01

Most of our learning activity takes place in a social context. I examined how social interactions influence associative learning in neurodegenerative diseases and atypical neurodevelopmental conditions primarily characterised by social cognitive and memory dysfunctions. Participants were individuals with high-functioning autism (HFA, n = 18), early-stage behavioural variant frontotemporal dementia (bvFTD, n = 16) and Alzheimer's disease (AD, n = 20). The leading symptoms in HFA and bvFTD were social and behavioural dysfunctions, whereas AD was characterised by memory deficits. Participants received three versions of a paired associates learning task. In the game with boxes test, objects were hidden in six candy boxes placed in different locations on the computer screen. In the game with faces, each box was labelled by a photo of a person. In the real-life version of the game, participants played with real persons. Individuals with HFA and bvFTD performed well in the computer games, but failed on the task including real persons. In contrast, in patients with early-stage AD, social interactions boosted paired associates learning up to the level of healthy control volunteers. Worse performance in the real life game was associated with less successful recognition of complex emotions and mental states in the Reading the Mind in the Eyes Test. Spatial span did not affect the results. When social cognition is impaired, but memory systems are less compromised (HFA and bvFTD), real-life interactions disrupt associative learning; when disease process impairs memory systems but social cognition is relatively intact (early-stage AD), social interactions have a beneficial effect on learning and memory. Copyright © 2014 Elsevier Ltd. All rights reserved.

Examining the direct and indirect effects of visual-verbal paired associate learning on Chinese word reading.

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Georgiou, George; Liu, Cuina; Xu, Shiyang

2017-08-01

Associative learning, traditionally measured with paired associate learning (PAL) tasks, has been found to predict reading ability in several languages. However, it remains unclear whether it also predicts word reading in Chinese, which is known for its ambiguous print-sound correspondences, and whether its effects are direct or indirect through the effects of other reading-related skills such as phonological awareness and rapid naming. Thus, the purpose of this study was to examine the direct and indirect effects of visual-verbal PAL on word reading in an unselected sample of Chinese children followed from the second to the third kindergarten year. A sample of 141 second-year kindergarten children (71 girls and 70 boys; mean age=58.99months, SD=3.17) were followed for a year and were assessed at both times on measures of visual-verbal PAL, rapid naming, and phonological awareness. In the third kindergarten year, they were also assessed on word reading. The results of path analysis showed that visual-verbal PAL exerted a significant direct effect on word reading that was independent of the effects of phonological awareness and rapid naming. However, it also exerted significant indirect effects through phonological awareness. Taken together, these findings suggest that variations in cross-modal associative learning (as measured by visual-verbal PAL) place constraints on the development of word recognition skills irrespective of the characteristics of the orthography children are learning to read. Copyright © 2017 Elsevier Inc. All rights reserved.

Are Approaches to Learning in Kindergarten Associated with Academic and Social Competence Similarly?

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Razza, Rachel A.; Martin, Anne; Brooks-Gunn, Jeanne

2015-01-01

Background: Approaches to learning (ATL) is a key domain of school readiness with important implications for children's academic trajectories. Interestingly, however, the impact of early ATL on children's social competence has not been examined. Objective: This study examines associations between children's ATL at age 5 and academic achievement…

Early exposure to volatile anesthetics impairs long-term associative learning and recognition memory.

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Bradley H Lee

Full Text Available Anesthetic exposure early in life affects neural development and long-term cognitive function, but our understanding of the types of memory that are altered is incomplete. Specific cognitive tests in rodents that isolate different memory processes provide a useful approach for gaining insight into this issue.Postnatal day 7 (P7 rats were exposed to either desflurane or isoflurane at 1 Minimum Alveolar Concentration for 4 h. Acute neuronal death was assessed 12 h later in the thalamus, CA1-3 regions of hippocampus, and dentate gyrus. In separate behavioral experiments, beginning at P48, subjects were evaluated in a series of object recognition tests relying on associative learning, as well as social recognition.Exposure to either anesthetic led to a significant increase in neuroapoptosis in each brain region. The extent of neuronal death did not differ between groups. Subjects were unaffected in simple tasks of novel object and object-location recognition. However, anesthetized animals from both groups were impaired in allocentric object-location memory and a more complex task requiring subjects to associate an object with its location and contextual setting. Isoflurane exposure led to additional impairment in object-context association and social memory.Isoflurane and desflurane exposure during development result in deficits in tasks relying on associative learning and recognition memory. Isoflurane may potentially cause worse impairment than desflurane.

Associative Mechanisms Allow for Social Learning and Cultural Transmission of String Pulling in an Insect

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Zhu, Xingfu; Ingraham, Thomas; Søvik, Eirik

2016-01-01

Social insects make elaborate use of simple mechanisms to achieve seemingly complex behavior and may thus provide a unique resource to discover the basic cognitive elements required for culture, i.e., group-specific behaviors that spread from “innovators” to others in the group via social learning. We first explored whether bumblebees can learn a nonnatural object manipulation task by using string pulling to access a reward that was presented out of reach. Only a small minority “innovated” and solved the task spontaneously, but most bees were able to learn to pull a string when trained in a stepwise manner. In addition, naïve bees learnt the task by observing a trained demonstrator from a distance. Learning the behavior relied on a combination of simple associative mechanisms and trial-and-error learning and did not require “insight”: naïve bees failed a “coiled-string experiment,” in which they did not receive instant visual feedback of the target moving closer when tugging on the string. In cultural diffusion experiments, the skill spread rapidly from a single knowledgeable individual to the majority of a colony’s foragers. We observed that there were several sequential sets (“generations”) of learners, so that previously naïve observers could first acquire the technique by interacting with skilled individuals and, subsequently, themselves become demonstrators for the next “generation” of learners, so that the longevity of the skill in the population could outlast the lives of informed foragers. This suggests that, so long as animals have a basic toolkit of associative and motor learning processes, the key ingredients for the cultural spread of unusual skills are already in place and do not require sophisticated cognition. PMID:27701411

Association of learning styles with research self-efficacy: study of short-term research training program for medical students.

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Dumbauld, Jill; Black, Michelle; Depp, Colin A; Daly, Rebecca; Curran, Maureen A; Winegarden, Babbi; Jeste, Dilip V

2014-12-01

With a growing need for developing future physician scientists, identifying characteristics of medical students who are likely to benefit from research training programs is important. This study assessed if specific learning styles of medical students, participating in federally funded short-term research training programs, were associated with research self-efficacy, a potential predictor of research career success. Seventy-five first-year medical students from 28 medical schools, selected to participate in two competitive NIH-supported summer programs for research training in aging, completed rating scales to evaluate learning styles at baseline, and research self-efficacy before and after training. We examined associations of individual learning styles (visual-verbal, sequential-global, sensing-intuitive, and active-reflective) with students' gender, ranking of medical school, and research self-efficacy. Research self-efficacy improved significantly following the training programs. Students with a verbal learning style reported significantly greater research self-efficacy at baseline, while visual, sequential, and intuitive learners demonstrated significantly greater increases in research self-efficacy from baseline to posttraining. No significant relationships were found between learning styles and students' gender or ranking of their medical school. Assessments of learning styles may provide useful information to guide future training endeavors aimed at developing the next generation of physician-scientists. © 2014 Wiley Periodicals, Inc.

Do Psychology Department Mission Statements Reflect the American Psychological Association Undergraduate Learning Goals?

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Warchal, Judith R.; Ruiz, Ana I.; You, Di

2017-01-01

This study focuses on the inclusion of the American Psychological Association's learning goals in the mission statements of undergraduate psychology programs across the US. We reviewed the mission statements available on websites for 1336 psychology programs listed in the Carnegie classification. Results of a content analysis revealed that of the…

Neural Pattern Similarity in the Left IFG and Fusiform Is Associated with Novel Word Learning

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Jing Qu

2017-08-01

Full Text Available Previous studies have revealed that greater neural pattern similarity across repetitions is associated with better subsequent memory. In this study, we used an artificial language training paradigm and representational similarity analysis to examine whether neural pattern similarity across repetitions before training was associated with post-training behavioral performance. Twenty-four native Chinese speakers were trained to learn a logographic artificial language for 12 days and behavioral performance was recorded using the word naming and picture naming tasks. Participants were scanned while performing a passive viewing task before training, after 4-day training and after 12-day training. Results showed that pattern similarity in the left pars opercularis (PO and fusiform gyrus (FG before training was negatively associated with reaction time (RT in both word naming and picture naming tasks after training. These results suggest that neural pattern similarity is an effective neurofunctional predictor of novel word learning in addition to word memory.

Neural Pattern Similarity in the Left IFG and Fusiform Is Associated with Novel Word Learning

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Qu, Jing; Qian, Liu; Chen, Chuansheng; Xue, Gui; Li, Huiling; Xie, Peng; Mei, Leilei

2017-01-01

Previous studies have revealed that greater neural pattern similarity across repetitions is associated with better subsequent memory. In this study, we used an artificial language training paradigm and representational similarity analysis to examine whether neural pattern similarity across repetitions before training was associated with post-training behavioral performance. Twenty-four native Chinese speakers were trained to learn a logographic artificial language for 12 days and behavioral performance was recorded using the word naming and picture naming tasks. Participants were scanned while performing a passive viewing task before training, after 4-day training and after 12-day training. Results showed that pattern similarity in the left pars opercularis (PO) and fusiform gyrus (FG) before training was negatively associated with reaction time (RT) in both word naming and picture naming tasks after training. These results suggest that neural pattern similarity is an effective neurofunctional predictor of novel word learning in addition to word memory. PMID:28878640

Equine Assisted Psychotherapy: The Equine Assisted Growth and Learning Association's Model Overview of Equine-Based Modalities

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Notgrass, Clayton G.; Pettinelli, J. Douglas

2015-01-01

This article describes the Equine Assisted Growth and Learning Association's (EAGALA) experiential model called "Equine Assisted Psychotherapy" (EAP). EAGALA's model is based on the Association for Experiential Education's (AEE) tenets and is focused on the learner's experience with horses. Drawing on the historical use of equines in the…

Neural network regulation driven by autonomous neural firings

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Cho, Myoung Won

2016-07-01

Biological neurons naturally fire spontaneously due to the existence of a noisy current. Such autonomous firings may provide a driving force for network formation because synaptic connections can be modified due to neural firings. Here, we study the effect of autonomous firings on network formation. For the temporally asymmetric Hebbian learning, bidirectional connections lose their balance easily and become unidirectional ones. Defining the difference between reciprocal connections as new variables, we could express the learning dynamics as if Ising model spins interact with each other in magnetism. We present a theoretical method to estimate the interaction between the new variables in a neural system. We apply the method to some network systems and find some tendencies of autonomous neural network regulation.

Function of insulin in snail brain in associative learning.

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Kojima, S; Sunada, H; Mita, K; Sakakibara, M; Lukowiak, K; Ito, E

2015-10-01

Insulin is well known as a hormone regulating glucose homeostasis across phyla. Although there are insulin-independent mechanisms for glucose uptake in the mammalian brain, which had contributed to a perception of the brain as an insulin-insensitive organ for decades, the finding of insulin and its receptors in the brain revolutionized the concept of insulin signaling in the brain. However, insulin's role in brain functions, such as cognition, attention, and memory, remains unknown. Studies using invertebrates with their open blood-vascular system have the promise of promoting a better understanding of the role played by insulin in mediating/modulating cognitive functions. In this review, the relationship between insulin and its impact on long-term memory (LTM) is discussed particularly in snails. The pond snail Lymnaea stagnalis has the ability to undergo conditioned taste aversion (CTA), that is, it associatively learns and forms LTM not to respond with a feeding response to a food that normally elicits a robust feeding response. We show that molluscan insulin-related peptides are up-regulated in snails exhibiting CTA-LTM and play a key role in the causal neural basis of CTA-LTM. We also survey the relevant literature of the roles played by insulin in learning and memory in other phyla.

Educational Experiences Associated with International Students' Learning, Development, and Positive Perceptions of Campus Climate

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Glass, Chris R.

2012-01-01

This research project uses the constructive-developmental tradition, in the self-authorship framework of intercultural maturity (King & Baxter Magolda, 2005), to examine the extent to which 12 specific educational experiences may be associated with international undergraduates' learning, development, and perception of campus climate. The study…

Arc mRNA induction in striatal efferent neurons associated with response learning.

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Daberkow, D P; Riedy, M D; Kesner, R P; Keefe, K A

2007-07-01

The dorsal striatum is involved in motor-response learning, but the extent to which distinct populations of striatal efferent neurons are differentially involved in such learning is unknown. Activity-regulated, cytoskeleton-associated (Arc) protein is an effector immediate-early gene implicated in synaptic plasticity. We examined arc mRNA expression in striatopallidal vs. striatonigral efferent neurons in dorsomedial and dorsolateral striatum of rats engaged in reversal learning on a T-maze motor-response task. Male Sprague-Dawley rats learned to turn right or left for 3 days. Half of the rats then underwent reversal training. The remaining rats were yoked to rats undergoing reversal training, such that they ran the same number of trials but ran them as continued-acquisition trials. Brains were removed and processed using double-label fluorescent in situ hybridization for arc and preproenkephalin (PPE) mRNA. In the reversal, but not the continued-acquisition, group there was a significant relation between the overall arc mRNA signal in dorsomedial striatum and the number of trials run, with rats reaching criterion in fewer trials having higher levels of arc mRNA expression. A similar relation was seen between the numbers of PPE(+) and PPE(-) neurons in dorsomedial striatum with cytoplasmic arc mRNA expression. Interestingly, in behaviourally activated animals significantly more PPE(-) neurons had cytoplasmic arc mRNA expression. These data suggest that Arc in both striatonigral and striatopallidal efferent neurons is involved in striatal synaptic plasticity mediating motor-response learning in the T-maze and that there is differential processing of arc mRNA in distinct subpopulations of striatal efferent neurons.

Challenge and Hindrance Stress: Relationships with Exhaustion, Motivation to Learn, and Learning Performance

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LePine, Jeffrey A.; LePine, Marcie A.; Jackson, Christine L.

2004-01-01

In a study of 696 learners, the authors found that stress associated with challenges in the learning environment had a positive relationship with learning performance and that stress associated with hindrances in the learning environment had a negative relationship with learning performance. They also found evidence suggesting that these…

Functional requirements for reward-modulated spike-timing-dependent plasticity.

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Frémaux, Nicolas; Sprekeler, Henning; Gerstner, Wulfram

2010-10-06

Recent experiments have shown that spike-timing-dependent plasticity is influenced by neuromodulation. We derive theoretical conditions for successful learning of reward-related behavior for a large class of learning rules where Hebbian synaptic plasticity is conditioned on a global modulatory factor signaling reward. We show that all learning rules in this class can be separated into a term that captures the covariance of neuronal firing and reward and a second term that presents the influence of unsupervised learning. The unsupervised term, which is, in general, detrimental for reward-based learning, can be suppressed if the neuromodulatory signal encodes the difference between the reward and the expected reward-but only if the expected reward is calculated for each task and stimulus separately. If several tasks are to be learned simultaneously, the nervous system needs an internal critic that is able to predict the expected reward for arbitrary stimuli. We show that, with a critic, reward-modulated spike-timing-dependent plasticity is capable of learning motor trajectories with a temporal resolution of tens of milliseconds. The relation to temporal difference learning, the relevance of block-based learning paradigms, and the limitations of learning with a critic are discussed.

Motor learning in a complex balance task and associated neuroplasticity: a comparison between endurance athletes and nonathletes.

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Seidel, Oliver; Carius, Daniel; Kenville, Rouven; Ragert, Patrick

2017-09-01

Studies suggested that motor expertise is associated with functional and structural brain alterations, which positively affect sensorimotor performance and learning capabilities. The purpose of the present study was to unravel differences in motor skill learning and associated functional neuroplasticity between endurance athletes (EA) and nonathletes (NA). For this purpose, participants had to perform a multimodal balance task (MBT) training on 2 sessions, which were separated by 1 wk. Before and after MBT training, a static balance task (SBT) had to be performed. MBT-induced functional neuroplasticity and neuromuscular alterations were assessed by means of functional near-infrared spectroscopy (fNIRS) and electromyography (EMG) during SBT performance. We hypothesized that EA would showed superior initial SBT performance and stronger MBT-induced improvements in SBT learning rates compared with NA. On a cortical level, we hypothesized that MBT training would lead to differential learning-dependent functional changes in motor-related brain regions [such as primary motor cortex (M1)] during SBT performance. In fact, EA showed superior initial SBT performance, whereas learning rates did not differ between groups. On a cortical level, fNIRS recordings (time × group interaction) revealed a stronger MBT-induced decrease in left M1 and inferior parietal lobe (IPL) for deoxygenated hemoglobin in EA. Even more interesting, learning rates were correlated with fNIRS changes in right M1/IPL. On the basis of these findings, we provide novel evidence for superior MBT training-induced functional neuroplasticity in highly trained athletes. Future studies should investigate these effects in different sports disciplines to strengthen previous work on experience-dependent neuroplasticity. NEW & NOTEWORTHY Motor expertise is associated with functional/structural brain plasticity. How such neuroplastic reorganization translates into altered motor learning processes remains elusive. We

Selectivity in associative learning: A cognitive stage framework for blocking and cue competition phenomena

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Yannick eBoddez

2014-11-01

Full Text Available Blocking is the most important phenomenon in the history of associative learning theory: For over 40 years, blocking has inspired a whole generation of learning models. Blocking is part of a family of effects that are typically termed cue competition effects. Common amongst all cue competition effects is that a cue-outcome relation is poorly learned or poorly expressed because the cue is trained in the presence of an alternative predictor or cause of the outcome. We provide an overview of the cognitive processes involved in cue competition effects in humans and propose a stage framework that brings these processes together. The framework contends that the behavioral display of cue competition is cognitively construed following three stages that include (1 an encoding stage, (2 a retention stage, and (3 a performance stage. We argue that the stage framework supports a comprehensive understanding of cue competition effects.

Fetal and infantile alcohol-mediated associative learning in the rat.

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Abate, P; Spear And, N E; Molina, J C

2001-07-01

Infant rats express conditioned responses to an odor experienced prenatally as a chemosensory cue associated with moderate alcohol intoxication. This study examined postnatal intake of a chemosensory cue (cineole) that had been paired with alcohol's unconditioned effects. It also tested the interaction between prenatal association and postnatal conditioning with cineole and alcohol. Pregnant female rats intubated with cineole were given ethanol (EtOH).25 or 4.0 hr later. Other groups received only water or water paired with ethanol. During postnatal day 15 (PD15), infant consumption of cineole solution was assessed. After the cineole drinking test, pups were intubated with EtOH or water to assess infant conditioning. On PD16, all pups were tested for mouthing to milk alone or to a milk-cineole solution. Statistical analysis confirmed fetal associative conditioning attributable to the unconditioned effects of prenatal alcohol. Fetuses given explicit pairings of cineole and alcohol ingested less cineole on PD15 than control fetuses given a 4-hr interval between cineole and alcohol. On PD16, consumption of cineole was significantly increased by prenatal exposure to cineole. Teratogenic effects of this dose of prenatal alcohol did not affect postnatal associative or nonassociative behavior. Prenatal associative learning can be established through temporal contiguity between fetal chemosensory stimulation and alcohol's unconditioned properties. This associative memory survives to infancy and modulates intake patterns and behavioral reactivity to substances that were prenatally paired with alcohol intoxication.

[Optogenetic activation of dorsal hippocampal astrocytic Rac1 blocks the learning of associative memory].

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Guo, Xiao-Mu; Liao, Zhao-Hui; Tao, Ye-Zheng; Wang, Fei-Fei; Ma, Lan

2017-06-25

Rac1 belongs to the family of Rho GTPases, and plays important roles in the brain function. It affects the cell migration and axon guidance via regulating the cytoskeleton and cellular morphology. However, the effect of its dynamic activation in regulating physiological function remains unclear. Recently, a photoactivatable analogue of Rac1 (PA-Rac1) has been developed, allowing the activation of Rac1 by the specific wavelength of light in living cells. Thus, we constructed recombinant adeno-associated virus (AAV) of PA-Rac1 and its light-insensitive mutant PA-Rac1-C450A under the control of the mouse glial fibrillary acidic protein (mGFAP) promoter to manipulate Rac1 activity in astrocytes by optical stimulation. Primary culture of hippocampal astrocytes was infected with the recombinant AAV-PA-Rac1 or AAV-PA-Rac1-C450A. Real-time fluorescence imaging showed that the cell membrane of the astrocyte expressing PA-Rac1 protruded near the light spot, while the astrocyte expressing PA-Rac1-C450A did not. We injected AAV-PA-Rac1 and AAV-PA-Rac1-C450A into dorsal hippocampus to investigate the role of the activation of Rac1 in regulating the associative learning. With optical stimulation, the PA-Rac1 group, rather than the PA-Rac1-C450A group, showed slower learning curve during the fear conditioning compared with the control group, indicating that activating astrocytic Rac1 blocks the formation of contextual memory. Our data suggest that the activation of Rac1 in dorsal hippocampal astrocyte plays an important role in the associative learning.

Learning by Helping? Undergraduate Communication Outcomes Associated with Training or Service-Learning Experiences

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Katz, Jennifer; DuBois, Melinda; Wigderson, Sara

2014-01-01

This study investigated communication outcomes after training or applied service-learning experiences. Pre-practicum trainees learned active listening skills over 10 weeks. Practicum students were successful trainees who staffed a helpline. Community interns were trained and supervised at community agencies. Undergraduate students in psychology…

Functional Specialization within the Striatum along Both the Dorsal/Ventral and Anterior/Posterior Axes during Associative Learning via Reward and Punishment

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Mattfeld, Aaron T.; Gluck, Mark A.; Stark, Craig E. L.

2011-01-01

The goal of the present study was to elucidate the role of the human striatum in learning via reward and punishment during an associative learning task. Previous studies have identified the striatum as a critical component in the neural circuitry of reward-related learning. It remains unclear, however, under what task conditions, and to what…

Mapping of olfactory memory circuits: region-specific c-fos activation after odor-reward associative learning or after its retrieval.

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Tronel, Sophie; Sara, Susan J

2002-01-01

Although there is growing knowledge about intracellular mechanisms underlying neuronal plasticity and memory consolidation and reconsolidation after retrieval, information concerning the interaction among brain areas during formation and retrieval of memory is relatively sparse and fragmented. Addressing this question requires simultaneous monitoring of activity in multiple brain regions during learning, the post-acquisition consolidation period, and retrieval and subsequent reconsolidation. Immunoreaction to the immediate early gene c-fos is a powerful tool to mark neuronal activation of specific populations of neurons. Using this method, we are able to report, for the first time, post-training activation of a network of closely related brain regions, particularly in the frontal cortex and the basolateral amygdala (BLA), that is specific to the learning of an odor-reward association. On the other hand, retrieval of a well-established associative memory trace does not seem to differentially activate the same regions. The amygdala, in particular, is not engaged after retrieval, whereas the lateral habenula (LHab) shows strong activation that is restricted to animals having previously learned the association. Although intracellular mechanisms may be similar during consolidation and reconsolidation, this study indicates that different brain circuits are involved in the two processes, at least with respect to a rapidly learned olfactory task.

Associative learning in humans--conditioning of sensory-evoked brain activity.

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Skrandies, W; Jedynak, A

2000-01-01

A classical conditioning paradigm was employed in two experiments performed on 35 human volunteers. In nine subjects, the presentation of Landolt rings (conditioned stimuli, CS + ) was paired with an electric stimulus (unconditioned stimuli, UCS) applied to the left median nerve. Neutral visual control stimuli were full circles (CS -) that were not paired with the UCS. The skin conductance response (SCR) was determined in a time interval of 5 s after onset of the visual stimuli, and it was measured in the acquisition and test phase. Associative learning was reflected by a SCR occurring selectively with CS +. The same experiment was repeated with another group of 26 adults while electroencephalogram (EEG) was recorded from 30 electrodes. For each subject, mean evoked potentials were computed. In 13 of the subjects, a conditioning paradigm was followed while the other subjects served as the control group (non-contingent stimulation). There were somatosensory and visual brain activity evoked by the stimuli. Conditioned components were identified by computing cross-correlation between evoked somatosensory components and the averaged EEG. In the visual evoked brain activity, three components with mean latencies of 105.4, 183.2, and 360.3 ms were analyzed. Somatosensory stimuli were followed by major components that occurred at mean latencies of 48.8, 132.5, 219.7, 294.8, and 374.2 ms latency after the shock. All components were analyzed in terms of latency, field strength, and topographic characteristics, and were compared between groups and experimental conditions. Both visual and somatosensory brain activity was significantly affected by classical conditioning. Our data illustrate how associative learning affects the topography of brain electrical activity elicited by presentation of conditioned visual stimuli.

Group social rank is associated with performance on a spatial learning task.

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Langley, Ellis J G; van Horik, Jayden O; Whiteside, Mark A; Madden, Joah R

2018-02-01

Dominant individuals differ from subordinates in their performances on cognitive tasks across a suite of taxa. Previous studies often only consider dyadic relationships, rather than the more ecologically relevant social hierarchies or networks, hence failing to account for how dyadic relationships may be adjusted within larger social groups. We used a novel statistical method: randomized Elo-ratings, to infer the social hierarchy of 18 male pheasants, Phasianus colchicus , while in a captive, mixed-sex group with a linear hierarchy. We assayed individual learning performance of these males on a binary spatial discrimination task to investigate whether inter-individual variation in performance is associated with group social rank. Task performance improved with increasing trial number and was positively related to social rank, with higher ranking males showing greater levels of success. Motivation to participate in the task was not related to social rank or task performance, thus indicating that these rank-related differences are not a consequence of differences in motivation to complete the task. Our results provide important information about how variation in cognitive performance relates to an individual's social rank within a group. Whether the social environment causes differences in learning performance or instead, inherent differences in learning ability predetermine rank remains to be tested.

Educational strategies associated with development of problem-solving, critical thinking, and self-directed learning.

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Hendricson, William D; Andrieu, Sandra C; Chadwick, D Gregory; Chmar, Jacqueline E; Cole, James R; George, Mary C; Glickman, Gerald N; Glover, Joel F; Goldberg, Jerold S; Haden, N Karl; Meyerowitz, Cyril; Neumann, Laura; Pyle, Marsha; Tedesco, Lisa A; Valachovic, Richard W; Weaver, Richard G; Winder, Ronald L; Young, Stephen K; Kalkwarf, Kenneth L

2006-09-01

This article was developed for the Commission on Change and Innovation in Dental Education (CCI), established by the American Dental Education Association. CCI was created because numerous organizations within organized dentistry and the educational community have initiated studies or proposed modifications to the process of dental education, often working to achieve positive and desirable goals but without coordination or communication. The fundamental mission of CCI is to serve as a focal meeting place where dental educators and administrators, representatives from organized dentistry, the dental licensure community, the Commission on Dental Accreditation, the ADA Council on Dental Education and Licensure, and the Joint Commission on National Dental Examinations can meet and coordinate efforts to improve dental education and the nation's oral health. One of the objectives of the CCI is to provide guidance to dental schools related to curriculum design. In pursuit of that objective, this article summarizes the evidence related to this question: What are educational best practices for helping dental students acquire the capacity to function as an entry-level general dentist or to be a better candidate to begin advanced studies? Three issues are addressed, with special emphasis on the third: 1) What constitutes expertise, and when does an individual become an expert? 2) What are the differences between novice and expert thinking? and 3) What educational best practices can help our students acquire mental capacities associated with expert function, including critical thinking and self-directed learning? The purpose of this review is to provide a benchmark that faculty and academic planners can use to assess the degree to which their curricula include learning experiences associated with development of problem-solving, critical thinking, self-directed learning, and other cognitive skills necessary for dental school graduates to ultimately become expert performers as

Large developing receptive fields using a distributed and locally reprogrammable address-event receiver.

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Bamford, Simeon A; Murray, Alan F; Willshaw, David J

2010-02-01

A distributed and locally reprogrammable address-event receiver has been designed, in which incoming address-events are monitored simultaneously by all synapses, allowing for arbitrarily large axonal fan-out without reducing channel capacity. Synapses can change the address of their presynaptic neuron, allowing the distributed implementation of a biologically realistic learning rule, with both synapse formation and elimination (synaptic rewiring). Probabilistic synapse formation leads to topographic map development, made possible by a cross-chip current-mode calculation of Euclidean distance. As well as synaptic plasticity in rewiring, synapses change weights using a competitive Hebbian learning rule (spike-timing-dependent plasticity). The weight plasticity allows receptive fields to be modified based on spatio-temporal correlations in the inputs, and the rewiring plasticity allows these modifications to become embedded in the network topology.

Timing is not everything: neuromodulation opens the STDP gate

Directory of Open Access Journals (Sweden)

Verena Pawlak

2010-10-01

Full Text Available Spike timing dependent plasticity (STDP is a temporally specific extension of Hebbian associative plasticity that has tied together the timing of presynaptic inputs relative to the postsynaptic single spike. However, it is difficult to translate this mechanism to in vivo conditions where there is an abundance of presynaptic activity constantly impinging upon the dendritic tree as well as ongoing postsynaptic spiking activity that backpropagates along the dendrite. Theoretical studies have proposed that, in addition to this pre- and postsynaptic activity, a ‘third factor’ would enable the association of specific inputs to specific outputs. Experimentally, the picture that is beginning to emerge, is that in addition to the precise timing of pre- and postsynaptic spikes, this third factor involves neuromodulators that have a distinctive influence on STDP rules. Specifically, neuromodulatory systems can influence STDP rules by acting via dopaminergic, noradrenergic, muscarinic and nicotinic receptors. Neuromodulator actions can enable STDP induction or - by increasing or decreasing the threshold - can change the conditions for plasticity induction. Because some of the neuromodulators are also involved in reward, a link between STDP and reward-mediated learning is emerging. However, many outstanding questions concerning the relationship between neuromodulatory systems and STDP rules remain, that once solved, will help make the crucial link from timing-based synaptic plasticity rules to behaviorally-based learning.

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

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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

Mechanism and treatment for the learning and memory deficits associated with mouse models of Noonan syndrome

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Lee, Yong-Seok; Ehninger, Dan; Zhou, Miou; Oh, Jun-Young; Kang, Minkyung; Kwak, Chuljung; Ryu, Hyun-Hee; Butz, Delana; Araki, Toshiyuki; Cai, Ying; Balaji, J.; Sano, Yoshitake; Nam, Christine I.; Kim, Hyong Kyu; Kaang, Bong-Kiun; Burger, Corinna; Neel, Benjamin G.; Silva, Alcino J.

2015-01-01

In Noonan Syndrome (NS) 30% to 50% of subjects show cognitive deficits of unknown etiology and with no known treatment. Here, we report that knock-in mice expressing either of two NS-associated Ptpn11 mutations show hippocampal-dependent spatial learning impairments and deficits in hippocampal long-term potentiation (LTP). In addition, viral overexpression of the PTPN11D61G in adult hippocampus results in increased baseline excitatory synaptic function, deficits in LTP and spatial learning, which can all be reversed by a MEK inhibitor. Furthermore, brief treatment with lovastatin reduces Ras-Erk activation in the brain, and normalizes the LTP and learning deficits in adult Ptpn11D61G/+ mice. Our results demonstrate that increased basal Erk activity and corresponding baseline increases in excitatory synaptic function are responsible for the LTP impairments and, consequently, the learning deficits in mouse models of NS. These data also suggest that lovastatin or MEK inhibitors may be useful for treating the cognitive deficits in NS. PMID:25383899

Neural changes associated to procedural learning and automatization process in Developmental Coordination Disorder and/or Developmental Dyslexia.

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Biotteau, Maëlle; Péran, Patrice; Vayssière, Nathalie; Tallet, Jessica; Albaret, Jean-Michel; Chaix, Yves

2017-03-01

Recent theories hypothesize that procedural learning may support the frequent overlap between neurodevelopmental disorders. The neural circuitry supporting procedural learning includes, among others, cortico-cerebellar and cortico-striatal loops. Alteration of these loops may account for the frequent comorbidity between Developmental Coordination Disorder (DCD) and Developmental Dyslexia (DD). The aim of our study was to investigate cerebral changes due to the learning and automatization of a sequence learning task in children with DD, or DCD, or both disorders. fMRI on 48 children (aged 8-12) with DD, DCD or DD + DCD was used to explore their brain activity during procedural tasks, performed either after two weeks of training or in the early stage of learning. Firstly, our results indicate that all children were able to perform the task with the same level of automaticity, but recruit different brain processes to achieve the same performance. Secondly, our fMRI results do not appear to confirm Nicolson and Fawcett's model. The neural correlates recruited for procedural learning by the DD and the comorbid groups are very close, while the DCD group presents distinct characteristics. This provide a promising direction on the neural mechanisms associated with procedural learning in neurodevelopmental disorders and for understanding comorbidity. Published by Elsevier Ltd.

Student learning outcomes associated with video vs. paper cases in a public health dentistry course.

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Chi, Donald L; Pickrell, Jacqueline E; Riedy, Christine A

2014-01-01

Educational technologies such as video cases can improve health professions student learning outcomes, but few studies in dentistry have evaluated video-based technologies. The goal of this study was to compare outcomes associated with video and paper cases used in an introductory public health dentistry course. This was a retrospective cohort study with a historical control group. Based on dual coding theory, the authors tested the hypotheses that dental students who received a video case (n=37) would report better affective, cognitive, and overall learning outcomes than students who received a paper case (n=75). One-way ANOVA was used to test the hypotheses across ten cognitive, two affective, and one general assessment measures (α=0.05). Students in the video group reported a significantly higher overall mean effectiveness score than students in the paper group (4.2 and 3.3, respectively; p<0.001). Video cases were also associated with significantly higher mean scores across the remaining twelve measures and were effective in helping students achieve cognitive (e.g., facilitating good discussions, identifying public health problems, realizing how health disparities might impact their future role as dentists) and affective (e.g., empathizing with vulnerable individuals, appreciating how health disparities impact real people) goals. Compared to paper cases, video cases significantly improved cognitive, affective, and overall learning outcomes for dental students.

Do medical students watch video clips in eLearning and do these facilitate learning?

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Romanov, Kalle; Nevgi, Anne

2007-06-01

There is controversial evidence of the impact of individual learning style on students' performance in computer-aided learning. We assessed the association between the use of multimedia materials, such as video clips, and collaborative communication tools with learning outcome among medical students. One hundred and twenty-one third-year medical students attended a course in medical informatics (0.7 credits) consisting of lectures, small group sessions and eLearning material. The eLearning material contained six learning modules with integrated video clips and collaborative learning tools in WebCT. Learning outcome was measured with a course exam. Approximately two-thirds of students (68.6%) viewed two or more videos. Female students were significantly more active video-watchers. No significant associations were found between video-watching and self-test scores or the time used in eLearning. Video-watchers were more active in WebCT; they loaded more pages and more actively participated in discussion forums. Video-watching was associated with a better course grade. Students who watched video clips were more active in using collaborative eLearning tools and achieved higher course grades.

The demise of the synapse as the locus of memory: A looming paradigm shift?

Directory of Open Access Journals (Sweden)

Patrick C. Trettenbrein

2016-11-01

Full Text Available Synaptic plasticity is widely considered to be the neurobiological basis of learning and memory by neuroscientists and researchers in adjacent fields, though diverging opinions are increasingly being recognised. From the perspective of what we might call classical cognitive science it has always been understood that the mind/brain is to be considered a computational-representational system. Proponents of the information-processing approach to cognitive science have long been critical of connectionist or network approaches to (neuro-cognitive architecture, pointing to the shortcomings of the associative psychology that underlies Hebbian learning as well as to the fact that synapses are practically unfit to implement symbols. Recent work on memory has been adding fuel to the fire and current findings in neuroscience now provide first tentative neurobiological evidence for the cognitive scientists’ doubts about the synapse as the (sole locus of memory in the brain. This paper briefly considers the history and appeal of synaptic plasticity as a memory mechanism, followed by a summary of the cognitive scientists’ objections regarding these assertions. Next, a variety of tentative neuroscientific evidence that appears to substantiate questioning the idea of the synapse as the locus of memory is presented. On this basis, a novel way of thinking about the role of synaptic plasticity in learning and memory is proposed.

APOE epsilon4 is associated with impaired verbal learning in patients with MS.

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Koutsis, G; Panas, M; Giogkaraki, E; Potagas, C; Karadima, G; Sfagos, C; Vassilopoulos, D

2007-02-20

To investigate the effect of APOE epsilon4 on different cognitive domains in a population of Greek patients with multiple sclerosis (MS). A total of 125 patients with MS and 43 controls were included in this study and underwent neuropsychological assessment with Rao's Brief Repeatable Battery. All patients with MS were genotyped for APOE. The effect of APOE epsilon4 on different cognitive domains was investigated. Fifty-one percent of patients with MS were cognitively impaired. E4 carriers had a sixfold increase in the relative risk of impairment in verbal learning vs noncarriers (OR 6.28, 95% CI 1.74 to 22.69). This effect was domain-specific and was not observed in other cognitive domains assessed by the battery. We found an association of APOE epsilon4 with impaired verbal learning in patients with multiple sclerosis.

DCS-Neural-Network Program for Aircraft Control and Testing

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Jorgensen, Charles C.

2006-01-01

A computer program implements a dynamic-cell-structure (DCS) artificial neural network that can perform such tasks as learning selected aerodynamic characteristics of an airplane from wind-tunnel test data and computing real-time stability and control derivatives of the airplane for use in feedback linearized control. A DCS neural network is one of several types of neural networks that can incorporate additional nodes in order to rapidly learn increasingly complex relationships between inputs and outputs. In the DCS neural network implemented by the present program, the insertion of nodes is based on accumulated error. A competitive Hebbian learning rule (a supervised-learning rule in which connection weights are adjusted to minimize differences between actual and desired outputs for training examples) is used. A Kohonen-style learning rule (derived from a relatively simple training algorithm, implements a Delaunay triangulation layout of neurons) is used to adjust node positions during training. Neighborhood topology determines which nodes are used to estimate new values. The network learns, starting with two nodes, and adds new nodes sequentially in locations chosen to maximize reductions in global error. At any given time during learning, the error becomes homogeneously distributed over all nodes.

Dental Students' Study Habits in Flipped/Blended Classrooms and Their Association with Active Learning Practices.