Hebbian based learning with winner-take-all for spiking neural networks

NASA Astrophysics Data System (ADS)

Gupta, Ankur; Long, Lyle

2009-03-01

Learning methods for spiking neural networks are not as well developed as the traditional neural networks that widely use back-propagation training. We propose and implement a Hebbian based learning method with winner-take-all competition for spiking neural networks. This approach is spike time dependent which makes it naturally well suited for a network of spiking neurons. Homeostasis with Hebbian learning is implemented which ensures stability and quicker learning. Homeostasis implies that the net sum of incoming weights associated with a neuron remains the same. Winner-take-all is also implemented for competitive learning between output neurons. We implemented this learning rule on a biologically based vision processing system that we are developing, and use layers of leaky integrate and fire neurons. The network when presented with 4 bars (or Gabor filters) of different orientation learns to recognize the bar orientations (or Gabor filters). After training, each output neuron learns to recognize a bar at specific orientation and responds by firing more vigorously to that bar and less vigorously to others. These neurons are found to have bell shaped tuning curves and are similar to the simple cells experimentally observed by Hubel and Wiesel in the striate cortex of cat and monkey.

Learning with incomplete information in the committee machine.

PubMed

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.

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

PubMed

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

2014-01-01

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

Integrating Hebbian and homeostatic plasticity: introduction.

PubMed

Fox, Kevin; Stryker, Michael

2017-03-05

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

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

PubMed

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 in VisNet.

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

PubMed Central

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 in VisNet. PMID:28562618

NDRAM: nonlinear dynamic recurrent associative memory for learning bipolar and nonbipolar correlated patterns.

PubMed

Chartier, Sylvain; Proulx, Robert

2005-11-01

This paper presents a new unsupervised attractor neural network, which, contrary to optimal linear associative memory models, is able to develop nonbipolar attractors as well as bipolar attractors. Moreover, the model is able to develop less spurious attractors and has a better recall performance under random noise than any other Hopfield type neural network. Those performances are obtained by a simple Hebbian/anti-Hebbian online learning rule that directly incorporates feedback from a specific nonlinear transmission rule. Several computer simulations show the model's distinguishing properties.

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

PubMed Central

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

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

PubMed

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.

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

PubMed

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 principles familiar in connectionist models of organismic learning.

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

PubMed

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.

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

PubMed

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.

AHaH Computing–From Metastable Switches to Attractors to Machine Learning

PubMed Central

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

An Approximation of the Error Backpropagation Algorithm in a Predictive Coding Network with Local Hebbian Synaptic Plasticity

PubMed Central

Whittington, James C. R.; Bogacz, Rafal

2017-01-01

To efficiently learn from feedback, cortical networks need to update synaptic weights on multiple levels of cortical hierarchy. An effective and well-known algorithm for computing such changes in synaptic weights is the error backpropagation algorithm. However, in this algorithm, the change in synaptic weights is a complex function of weights and activities of neurons not directly connected with the synapse being modified, whereas the changes in biological synapses are determined only by the activity of presynaptic and postsynaptic neurons. Several models have been proposed that approximate the backpropagation algorithm with local synaptic plasticity, but these models require complex external control over the network or relatively complex plasticity rules. Here we show that a network developed in the predictive coding framework can efficiently perform supervised learning fully autonomously, employing only simple local Hebbian plasticity. Furthermore, for certain parameters, the weight change in the predictive coding model converges to that of the backpropagation algorithm. This suggests that it is possible for cortical networks with simple Hebbian synaptic plasticity to implement efficient learning algorithms in which synapses in areas on multiple levels of hierarchy are modified to minimize the error on the output. PMID:28333583

An Approximation of the Error Backpropagation Algorithm in a Predictive Coding Network with Local Hebbian Synaptic Plasticity.

PubMed

Whittington, James C R; Bogacz, Rafal

2017-05-01

To efficiently learn from feedback, cortical networks need to update synaptic weights on multiple levels of cortical hierarchy. An effective and well-known algorithm for computing such changes in synaptic weights is the error backpropagation algorithm. However, in this algorithm, the change in synaptic weights is a complex function of weights and activities of neurons not directly connected with the synapse being modified, whereas the changes in biological synapses are determined only by the activity of presynaptic and postsynaptic neurons. Several models have been proposed that approximate the backpropagation algorithm with local synaptic plasticity, but these models require complex external control over the network or relatively complex plasticity rules. Here we show that a network developed in the predictive coding framework can efficiently perform supervised learning fully autonomously, employing only simple local Hebbian plasticity. Furthermore, for certain parameters, the weight change in the predictive coding model converges to that of the backpropagation algorithm. This suggests that it is possible for cortical networks with simple Hebbian synaptic plasticity to implement efficient learning algorithms in which synapses in areas on multiple levels of hierarchy are modified to minimize the error on the output.

Learning pattern recognition and decision making in the insect brain

NASA Astrophysics Data System (ADS)

Huerta, R.

2013-01-01

We revise the current model of learning pattern recognition in the Mushroom Bodies of the insects using current experimental knowledge about the location of learning, olfactory coding and connectivity. We show that it is possible to have an efficient pattern recognition device based on the architecture of the Mushroom Bodies, sparse code, mutual inhibition and Hebbian leaning only in the connections from the Kenyon cells to the output neurons. We also show that despite the conventional wisdom that believes that artificial neural networks are the bioinspired model of the brain, the Mushroom Bodies actually resemble very closely Support Vector Machines (SVMs). The derived SVM learning rules are situated in the Mushroom Bodies, are nearly identical to standard Hebbian rules, and require inhibition in the output. A very particular prediction of the model is that random elimination of the Kenyon cells in the Mushroom Bodies do not impair the ability to recognize odorants previously learned.

Muscarinic acetylcholine receptors control baseline activity and Hebbian stimulus timing-dependent plasticity in fusiform cells of the dorsal cochlear nucleus.

PubMed

Stefanescu, Roxana A; Shore, Susan E

2017-03-01

Cholinergic modulation contributes to adaptive sensory processing by controlling spontaneous and stimulus-evoked neural activity and long-term synaptic plasticity. In the dorsal cochlear nucleus (DCN), in vitro activation of muscarinic acetylcholine receptors (mAChRs) alters the spontaneous activity of DCN neurons and interacts with N -methyl-d-aspartate (NMDA) and endocannabinoid receptors to modulate the plasticity of parallel fiber synapses onto fusiform cells by converting Hebbian long-term potentiation to anti-Hebbian long-term depression. Because noise exposure and tinnitus are known to increase spontaneous activity in fusiform cells as well as alter stimulus timing-dependent plasticity (StTDP), it is important to understand the contribution of mAChRs to in vivo spontaneous activity and plasticity in fusiform cells. In the present study, we blocked mAChRs actions by infusing atropine, a mAChR antagonist, into the DCN fusiform cell layer in normal hearing guinea pigs. Atropine delivery leads to decreased spontaneous firing rates and increased synchronization of fusiform cell spiking activity. Consistent with StTDP alterations observed in tinnitus animals, atropine infusion induced a dominant pattern of inversion of StTDP mean population learning rule from a Hebbian to an anti-Hebbian profile. Units preserving their initial Hebbian learning rules shifted toward more excitatory changes in StTDP, whereas units with initial suppressive learning rules transitioned toward a Hebbian profile. Together, these results implicate muscarinic cholinergic modulation as a factor in controlling in vivo fusiform cell baseline activity and plasticity, suggesting a central role in the maladaptive plasticity associated with tinnitus pathology. NEW & NOTEWORTHY This study is the first to use a novel method of atropine infusion directly into the fusiform cell layer of the dorsal cochlear nucleus coupled with simultaneous recordings of neural activity to clarify the contribution of muscarinic acetylcholine receptors (mAChRs) to in vivo fusiform cell baseline activity and auditory-somatosensory plasticity. We have determined that blocking the mAChRs increases the synchronization of spiking activity across the fusiform cell population and induces a dominant pattern of inversion in their stimulus timing-dependent plasticity. These modifications are consistent with similar changes established in previous tinnitus studies, suggesting that mAChRs might have a critical contribution in mediating the maladaptive alterations associated with tinnitus pathology. Blocking mAChRs also resulted in decreased fusiform cell spontaneous firing rates, which is in contrast with their tinnitus hyperactivity, suggesting that changes in the interactions between the cholinergic and GABAergic systems might also be an underlying factor in tinnitus pathology. Copyright © 2017 the American Physiological Society.

Control of a simulated arm using a novel combination of Cerebellar learning mechanisms

NASA Technical Reports Server (NTRS)

Assad, C.; Hartmann, M.; Paulin, M. G.

2001-01-01

We present a model of cerebellar cortex that combines two types of learning: feedforward predicitve association based on local Hebbian-type learning between granule cell ascending branch and parallel fiber inputs, and reinforcement learning with feedback error correction based on climbing fiber activity.

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

PubMed

Frémaux, Nicolas; Gerstner, Wulfram

2015-01-01

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 neuromodulators on 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 discuss some 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.

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

PubMed

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.

Synaptic and nonsynaptic plasticity approximating probabilistic inference

PubMed Central

Tully, Philip J.; Hennig, Matthias H.; Lansner, Anders

2014-01-01

Learning and memory operations in neural circuits are believed to involve molecular cascades of synaptic and nonsynaptic changes that lead to a diverse repertoire of dynamical phenomena at higher levels of processing. Hebbian and homeostatic plasticity, neuromodulation, and intrinsic excitability all conspire to form and maintain memories. But it is still unclear how these seemingly redundant mechanisms could jointly orchestrate learning in a more unified system. To this end, a Hebbian learning rule for spiking neurons inspired by Bayesian statistics is proposed. In this model, synaptic weights and intrinsic currents are adapted on-line upon arrival of single spikes, which initiate a cascade of temporally interacting memory traces that locally estimate probabilities associated with relative neuronal activation levels. Trace dynamics enable synaptic learning to readily demonstrate a spike-timing dependence, stably return to a set-point over long time scales, and remain competitive despite this stability. Beyond unsupervised learning, linking the traces with an external plasticity-modulating signal enables spike-based reinforcement learning. At the postsynaptic neuron, the traces are represented by an activity-dependent ion channel that is shown to regulate the input received by a postsynaptic cell and generate intrinsic graded persistent firing levels. We show how spike-based Hebbian-Bayesian learning can be performed in a simulated inference task using integrate-and-fire (IAF) neurons that are Poisson-firing and background-driven, similar to the preferred regime of cortical neurons. Our results support the view that neurons can represent information in the form of probability distributions, and that probabilistic inference could be a functional by-product of coupled synaptic and nonsynaptic mechanisms operating over several timescales. The model provides a biophysical realization of Bayesian computation by reconciling several observed neural phenomena whose functional effects are only partially understood in concert. PMID:24782758

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

PubMed

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.

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

PubMed

Lindsay, Grace W; Rigotti, Mattia; Warden, Melissa R; Miller, Earl K; Fusi, Stefano

2017-11-08

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 ("mixed selectivity")-is a topic of interest. Even though models with random feedforward connectivity are capable of creating computationally relevant mixed selectivity, such a model does not match the levels of mixed selectivity seen in the data analyzed in this study. Adding simple Hebbian learning to the model increases mixed selectivity to the correct level and makes the model match the data on several other relevant measures. This study thus offers predictions on how mixed selectivity and other properties evolve with training. Copyright © 2017 the authors 0270-6474/17/3711021-16$15.00/0.

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

PubMed Central

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 (“mixed selectivity”)—is a topic of interest. Even though models with random feedforward connectivity are capable of creating computationally relevant mixed selectivity, such a model does not match the levels of mixed selectivity seen in the data analyzed in this study. Adding simple Hebbian learning to the model increases mixed selectivity to the correct level and makes the model match the data on several other relevant measures. This study thus offers predictions on how mixed selectivity and other properties evolve with training. PMID:28986463

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

PubMed

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 cortical tissue. These findings are directly relevant to the ongoing paradigm shift in the WM field. Copyright © 2017 Fiebig and Lansner.

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

PubMed Central

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 neurophysiology of the underlying cortical tissue. These findings are directly relevant to the ongoing paradigm shift in the WM field. PMID:28053032

Associative Memory in Three Aplysiids: Correlation with Heterosynaptic Modulation

ERIC Educational Resources Information Center

Thompson, Laura; Wright, William G.; Hoover, Brian A.; Nguyen, Hoang

2006-01-01

Much recent research on mechanisms of learning and memory focuses on the role of heterosynaptic neuromodulatory signaling. Such neuromodulation appears to stabilize Hebbian synaptic changes underlying associative learning, thereby extending memory. Previous comparisons of three related sea-hares (Mollusca, Opisthobranchia) uncovered interspecific…

Auto-programmable impulse neural circuits

NASA Technical Reports Server (NTRS)

Watula, D.; Meador, J.

1990-01-01

Impulse neural networks use pulse trains to communicate neuron activation levels. Impulse neural circuits emulate natural neurons at a more detailed level than that typically employed by contemporary neural network implementation methods. An impulse neural circuit which realizes short term memory dynamics is presented. The operation of that circuit is then characterized in terms of pulse frequency modulated signals. Both fixed and programmable synapse circuits for realizing long term memory are also described. The implementation of a simple and useful unsupervised learning law is then presented. The implementation of a differential Hebbian learning rule for a specific mean-frequency signal interpretation is shown to have a straightforward implementation using digital combinational logic with a variation of a previously developed programmable synapse circuit. This circuit is expected to be exploited for simple and straightforward implementation of future auto-adaptive neural circuits.

Genetic attack on neural cryptography.

PubMed

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

DOE Office of Scientific and Technical Information (OSTI.GOV)

Ruttor, Andreas; Kinzel, Wolfgang; Naeh, Rivka

2006-03-15

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 formore » 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.« less

Genetic attack on neural cryptography

NASA Astrophysics Data System (ADS)

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.

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

PubMed Central

Frémaux, Nicolas; Gerstner, Wulfram

2016-01-01

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 neuromodulators on 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 discuss some 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. PMID:26834568

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

PubMed

van Hartevelt, Tim J; Cabral, Joana; Møller, Arne; FitzGerald, James J; Green, Alexander L; Aziz, Tipu Z; Deco, Gustavo; Kringelbach, Morten L

2015-01-01

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 (SC) 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 (PD) 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 (STN) to the putamen and the thalamus in a Hebbian-like manner. Moreover, long-term DBS also significantly changed the SC 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 SC 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.

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

PubMed

Olde Scheper, Tjeerd V; Meredith, Rhiannon M; Mansvelder, Huibert D; van Pelt, Jaap; van Ooyen, Arjen

2017-01-01

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 computational entities, each contributing to the global activity, not in a simply linear fashion, but in a manner that is appropriate to achieve local and global stability of the neuron and the entire dendritic structure.

Synthetic Modeling of Autonomous Learning with a Chaotic Neural Network

NASA Astrophysics Data System (ADS)

Funabashi, Masatoshi

We investigate the possible role of intermittent chaotic dynamics called chaotic itinerancy, in interaction with nonsupervised learnings that reinforce and weaken the neural connection depending on the dynamics itself. We first performed hierarchical stability analysis of the Chaotic Neural Network model (CNN) according to the structure of invariant subspaces. Irregular transition between two attractor ruins with positive maximum Lyapunov exponent was triggered by the blowout bifurcation of the attractor spaces, and was associated with riddled basins structure. We secondly modeled two autonomous learnings, Hebbian learning and spike-timing-dependent plasticity (STDP) rule, and simulated the effect on the chaotic itinerancy state of CNN. Hebbian learning increased the residence time on attractor ruins, and produced novel attractors in the minimum higher-dimensional subspace. It also augmented the neuronal synchrony and established the uniform modularity in chaotic itinerancy. STDP rule reduced the residence time on attractor ruins, and brought a wide range of periodicity in emerged attractors, possibly including strange attractors. Both learning rules selectively destroyed and preserved the specific invariant subspaces, depending on the neuron synchrony of the subspace where the orbits are situated. Computational rationale of the autonomous learning is discussed in connectionist perspective.

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

PubMed

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.

Adaptive WTA with an analog VLSI neuromorphic learning chip.

PubMed

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.

Towards autonomous neuroprosthetic control using Hebbian reinforcement learning.

PubMed

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.

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

PubMed Central

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 messenger in neurons with synapses as low and band-pass filters to obtain high encoding efficiency in different environmental and physiological conditions. PMID:25972786

Learning with incomplete information and the mathematical structure behind it.

PubMed

Kühn, Reimer; Stamatescu, Ion-Olimpiu

2007-07-01

We investigate the problem of learning with incomplete information as exemplified by learning with delayed reinforcement. We study a two phase learning scenario in which a phase of Hebbian associative learning based on momentary internal representations is supplemented by an 'unlearning' phase depending on a graded reinforcement signal. The reinforcement signal quantifies the success-rate globally for a number of learning steps in phase one, and 'unlearning' is indiscriminate with respect to associations learnt in that phase. Learning according to this model is studied via simulations and analytically within a student-teacher scenario for both single layer networks and, for a committee machine. Success and speed of learning depend on the ratio lambda of the learning rates used for the associative Hebbian learning phase and for the unlearning-correction in response to the reinforcement signal, respectively. Asymptotically perfect generalization is possible only, if this ratio exceeds a critical value lambda( c ), in which case the generalization error exhibits a power law decay with the number of examples seen by the student, with an exponent that depends in a non-universal manner on the parameter lambda. We find these features to be robust against a wide spectrum of modifications of microscopic modelling details. Two illustrative applications-one of a robot learning to navigate a field containing obstacles, and the problem of identifying a specific component in a collection of stimuli-are also provided.

From modulated Hebbian plasticity to simple behavior learning through noise and weight saturation.

PubMed

Soltoggio, Andrea; Stanley, Kenneth O

2012-10-01

Synaptic plasticity is a major mechanism for adaptation, learning, and memory. Yet current models struggle to link local synaptic changes to the acquisition of behaviors. The aim of this paper is to demonstrate a computational relationship between local Hebbian plasticity and behavior learning by exploiting two traditionally unwanted features: neural noise and synaptic weight saturation. A modulation signal is employed to arbitrate the sign of plasticity: when the modulation is positive, the synaptic weights saturate to express exploitative behavior; when it is negative, the weights converge to average values, and neural noise reconfigures the network's functionality. This process is demonstrated through simulating neural dynamics in the autonomous emergence of fearful and aggressive navigating behaviors and in the solution to reward-based problems. The neural model learns, memorizes, and modifies different behaviors that lead to positive modulation in a variety of settings. The algorithm establishes a simple relationship between local plasticity and behavior learning by demonstrating the utility of noise and weight saturation. Moreover, it provides a new tool to simulate adaptive behavior, and contributes to bridging the gap between synaptic changes and behavior in neural computation. Copyright © 2012 Elsevier Ltd. All rights reserved.

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

PubMed Central

Garagnani, Max; Wennekers, Thomas; Pulvermüller, Friedemann

2009-01-01

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. PMID:20396612

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

PubMed

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.

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

PubMed

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.

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

PubMed Central

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. PMID:23801941

A neoHebbian framework for episodic memory; role of dopamine-dependent late LTP

PubMed Central

Grace, Anthony A.; Duzel, Emrah

2011-01-01

According to the Hebb rule, the change in the strength of a synapse depends only on the local interaction of presynaptic and postsynaptic events. Studies at many types of synapses indicate that the early phase of long-term potentiation (LTP) has Hebbian properties. However, it is now clear that the Hebb rule does not account for late LTP; this requires an additional signal that is non-local. For novel information and motivational events such as rewards, this signal at hippocampal CA1 synapses is mediated by the neuromodulator, dopamine. In this Review, we discuss recent experimental findings that support the view that this “neoHebbian” framework can account for memory behavior in a variety of learning situations. PMID:21851992

ERP evidence for conflict in contingency learning.

PubMed

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.

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

PubMed

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.

Hebbian self-organizing integrate-and-fire networks for data clustering.

PubMed

Landis, Florian; Ott, Thomas; Stoop, Ruedi

2010-01-01

We propose a Hebbian learning-based data clustering algorithm using spiking neurons. The algorithm is capable of distinguishing between clusters and noisy background data and finds an arbitrary number of clusters of arbitrary shape. These properties render the approach particularly useful for visual scene segmentation into arbitrarily shaped homogeneous regions. We present several application examples, and in order to highlight the advantages and the weaknesses of our method, we systematically compare the results with those from standard methods such as the k-means and Ward's linkage clustering. The analysis demonstrates that not only the clustering ability of the proposed algorithm is more powerful than those of the two concurrent methods, the time complexity of the method is also more modest than that of its generally used strongest competitor.

Spike-timing dependent inhibitory plasticity to learn a selective gating of backpropagating action potentials.

PubMed

Wilmes, Katharina Anna; Schleimer, Jan-Hendrik; Schreiber, Susanne

2017-04-01

Inhibition is known to influence the forward-directed flow of information within neurons. However, also regulation of backward-directed signals, such as backpropagating action potentials (bAPs), can enrich the functional repertoire of local circuits. Inhibitory control of bAP spread, for example, can provide a switch for the plasticity of excitatory synapses. Although such a mechanism is possible, it requires a precise timing of inhibition to annihilate bAPs without impairment of forward-directed excitatory information flow. Here, we propose a specific learning rule for inhibitory synapses to automatically generate the correct timing to gate bAPs in pyramidal cells when embedded in a local circuit of feedforward inhibition. Based on computational modeling of multi-compartmental neurons with physiological properties, we demonstrate that a learning rule with anti-Hebbian shape can establish the required temporal precision. In contrast to classical spike-timing dependent plasticity of excitatory synapses, the proposed inhibitory learning mechanism does not necessarily require the definition of an upper bound of synaptic weights because of its tendency to self-terminate once annihilation of bAPs has been reached. Our study provides a functional context in which one of the many time-dependent learning rules that have been observed experimentally - specifically, a learning rule with anti-Hebbian shape - is assigned a relevant role for inhibitory synapses. Moreover, the described mechanism is compatible with an upregulation of excitatory plasticity by disinhibition. © 2016 Federation of European Neuroscience Societies and John Wiley & Sons Ltd.

Theta Coordinated Error Driven Learning in the Hippocampus (Open Access, Publisher’s Version)

DTIC Science & Technology

2013-06-06

assumed to be Hebbian in nature, where individual neurons in an engram join together with synaptic weight increases to support facilitated recall of...together as part of a memory or engram representation, e.g., in the central area CA3 of the hippocampus. With these connections strengthened, the ability

A comparison of two neural network schemes for navigation

NASA Technical Reports Server (NTRS)

Munro, Paul W.

1989-01-01

Neural networks have been applied to tasks in several areas of artificial intelligence, including vision, speech, and language. Relatively little work has been done in the area of problem solving. Two approaches to path-finding are presented, both using neural network techniques. Both techniques require a training period. Training under the back propagation (BPL) method was accomplished by presenting representations of (current position, goal position) pairs as input and appropriate actions as output. The Hebbian/interactive activation (HIA) method uses the Hebbian rule to associate points that are nearby. A path to a goal is found by activating a representation of the goal in the network and processing until the current position is activated above some threshold level. BPL, using back-propagation learning, failed to learn, except in a very trivial fashion, that is equivalent to table lookup techniques. HIA, performed much better, and required storage of fewer weights. In drawing a comparison, it is important to note that back propagation techniques depend critically upon the forms of representation used, and can be sensitive to parameters in the simulations; hence the BPL technique may yet yield strong results.

A comparison of two neural network schemes for navigation

NASA Technical Reports Server (NTRS)

Munro, Paul

1990-01-01

Neural networks have been applied to tasks in several areas of artificial intelligence, including vision, speech, and language. Relatively little work has been done in the area of problem solving. Two approaches to path-finding are presented, both using neural network techniques. Both techniques require a training period. Training under the back propagation (BPL) method was accomplished by presenting representations of current position, goal position pairs as input and appropriate actions as output. The Hebbian/interactive activation (HIA) method uses the Hebbian rule to associate points that are nearby. A path to a goal is found by activating a representation of the goal in the network and processing until the current position is activated above some threshold level. BPL, using back-propagation learning, failed to learn, except in a very trivial fashion, that is equivalent to table lookup techniques. HIA, performed much better, and required storage of fewer weights. In drawing a comparison, it is important to note that back propagation techniques depend critically upon the forms of representation used, and can be sensitive to parameters in the simulations; hence the BPL technique may yet yield strong results.

Addressing the Movement of a Freescale Robotic Car Using Neural Network

NASA Astrophysics Data System (ADS)

Horváth, Dušan; Cuninka, Peter

2016-12-01

This article deals with the management of a Freescale small robotic car along the predefined guide line. Controlling of the direction of movement of the robot is performed by neural networks, and scales (memory) of neurons are calculated by Hebbian learning from the truth tables as learning with a teacher. Reflexive infrared sensors serves as inputs. The results are experiments, which are used to compare two methods of mobile robot control - tracking lines.

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

PubMed

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 relapse. We found that repeated cocaine exposure alters a Hebbian associative synaptic learning rule that governs activity-dependent synaptic plasticity in the mouse prefrontal cortex, characterized by a broader temporal window and a lower threshold for spike-timing-dependent LTP (t-LTP), a cellular form of learning and memory. This rule change is caused by cocaine-exacerbated D1-cAMP/protein kinase A dopamine signaling in pyramidal neurons that in turn pathologically recruits l-type Ca 2+ channels to facilitate coincidence detection during t-LTP induction. Our study provides novel insights on how cocaine, even with only brief exposure, may prime neural circuits for subsequent experience-dependent remodeling that may underlie certain addictive behavior. Copyright © 2017 the authors 0270-6474/17/370986-12$15.00/0.

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

PubMed

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.

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

DTIC Science & Technology

2012-03-16

topological complexity a way to compare the efficiency of a scale-free network to the random network of Erdos and Renyi . All this is extensively dis- cussed in...an excellent review paper byArenas et al. (2008) showing very interesting comparisons of Erdos– Renyi networks and scale- free networks as a function

Circuit mechanisms of sensorimotor learning

PubMed Central

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

Unsupervised learning in general connectionist systems.

PubMed

Dente, J A; Mendes, R Vilela

1996-01-01

There is a common framework in which different connectionist systems may be treated in a unified way. The general system in which they may all be mapped is a network which, in addition to the connection strengths, has an adaptive node parameter controlling the output intensity. In this paper we generalize two neural network learning schemes to networks with node parameters. In generalized Hebbian learning we find improvements to the convergence rate for small eigenvalues in principal component analysis. For competitive learning the use of node parameters also seems useful in that, by emphasizing or de-emphasizing the dominance of winning neurons, either improved robustness or discrimination is obtained.

How synapses can enhance sensibility of a neural network

NASA Astrophysics Data System (ADS)

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.

Hebbian Learning of Cognitive Control: Dealing with Specific and Nonspecific Adaptation

ERIC Educational Resources Information Center

Verguts, Tom; Notebaert, Wim

2008-01-01

The conflict monitoring model of M. M. Botvinick, T. S. Braver, D. M. Barch, C. S. Carter, and J. D. Cohen (2001) triggered several research programs investigating various aspects of cognitive control. One problematic aspect of the Botvinick et al. model is that there is no clear account of how the cognitive system knows where to intervene when…

Dual coding with STDP in a spiking recurrent neural network model of the hippocampus.

PubMed

Bush, Daniel; Philippides, Andrew; Husbands, Phil; O'Shea, Michael

2010-07-01

The firing rate of single neurons in the mammalian hippocampus has been demonstrated to encode for a range of spatial and non-spatial stimuli. It has also been demonstrated that phase of firing, with respect to the theta oscillation that dominates the hippocampal EEG during stereotype learning behaviour, correlates with an animal's spatial location. These findings have led to the hypothesis that the hippocampus operates using a dual (rate and temporal) coding system. To investigate the phenomenon of dual coding in the hippocampus, we examine a spiking recurrent network model with theta coded neural dynamics and an STDP rule that mediates rate-coded Hebbian learning when pre- and post-synaptic firing is stochastic. We demonstrate that this plasticity rule can generate both symmetric and asymmetric connections between neurons that fire at concurrent or successive theta phase, respectively, and subsequently produce both pattern completion and sequence prediction from partial cues. This unifies previously disparate auto- and hetero-associative network models of hippocampal function and provides them with a firmer basis in modern neurobiology. Furthermore, the encoding and reactivation of activity in mutually exciting Hebbian cell assemblies demonstrated here is believed to represent a fundamental mechanism of cognitive processing in the brain.

Neural coordination can be enhanced by occasional interruption of normal firing patterns: a self-optimizing spiking neural network model.

PubMed

Woodward, Alexander; Froese, Tom; Ikegami, Takashi

2015-02-01

The state space of a conventional Hopfield network typically exhibits many different attractors of which only a small subset satisfies constraints between neurons in a globally optimal fashion. It has recently been demonstrated that combining Hebbian learning with occasional alterations of normal neural states avoids this problem by means of self-organized enlargement of the best basins of attraction. However, so far it is not clear to what extent this process of self-optimization is also operative in real brains. Here we demonstrate that it can be transferred to more biologically plausible neural networks by implementing a self-optimizing spiking neural network model. In addition, by using this spiking neural network to emulate a Hopfield network with Hebbian learning, we attempt to make a connection between rate-based and temporal coding based neural systems. Although further work is required to make this model more realistic, it already suggests that the efficacy of the self-optimizing process is independent from the simplifying assumptions of a conventional Hopfield network. We also discuss natural and cultural processes that could be responsible for occasional alteration of neural firing patterns in actual brains. Copyright © 2014 Elsevier Ltd. All rights reserved.

Hebbian Plasticity in CPG Controllers Facilitates Self-Synchronization for Human-Robot Handshaking.

PubMed

Jouaiti, Melanie; Caron, Lancelot; Hénaff, Patrick

2018-01-01

It is well-known that human social interactions generate synchrony phenomena which are often unconscious. If the interaction between individuals is based on rhythmic movements, synchronized and coordinated movements will emerge from the social synchrony. This paper proposes a plausible model of plastic neural controllers that allows the emergence of synchronized movements in physical and rhythmical interactions. The controller is designed with central pattern generators (CPG) based on rhythmic Rowat-Selverston neurons endowed with neuronal and synaptic Hebbian plasticity. To demonstrate the interest of the proposed model, the case of handshaking is considered because it is a very common, both physically and socially, but also, a very complex act in the point of view of robotics, neuroscience and psychology. Plastic CPGs controllers are implemented in the joints of a simulated robotic arm that has to learn the frequency and amplitude of an external force applied to its effector, thus reproducing the act of handshaking with a human. Results show that the neural and synaptic Hebbian plasticity are working together leading to a natural and autonomous synchronization between the arm and the external force even if the frequency is changing during the movement. Moreover, a power consumption analysis shows that, by offering emergence of synchronized and coordinated movements, the plasticity mechanisms lead to a significant decrease in the energy spend by the robot actuators thus generating a more adaptive and natural human/robot handshake.

The dialectic of Hebb and homeostasis.

PubMed

Turrigiano, Gina G

2017-03-05

It has become widely accepted that homeostatic and Hebbian plasticity mechanisms work hand in glove to refine neural circuit function. Nonetheless, our understanding of how these fundamentally distinct forms of plasticity compliment (and under some circumstances interfere with) each other remains rudimentary. Here, I describe some of the recent progress of the field, as well as some of the deep puzzles that remain. These include unravelling the spatial and temporal scales of different homeostatic and Hebbian mechanisms, determining which aspects of network function are under homeostatic control, and understanding when and how homeostatic and Hebbian mechanisms must be segregated within neural circuits to prevent interference.This article is part of the themed issue 'Integrating Hebbian and homeostatic plasticity'. © 2017 The Author(s).

Activity-dependent synaptic plasticity of a chalcogenide electronic synapse for neuromorphic systems.

PubMed

Li, Yi; Zhong, Yingpeng; Zhang, Jinjian; Xu, Lei; Wang, Qing; Sun, Huajun; Tong, Hao; Cheng, Xiaoming; Miao, Xiangshui

2014-05-09

Nanoscale inorganic electronic synapses or synaptic devices, which are capable of emulating the functions of biological synapses of brain neuronal systems, are regarded as the basic building blocks for beyond-Von Neumann computing architecture, combining information storage and processing. Here, we demonstrate a Ag/AgInSbTe/Ag structure for chalcogenide memristor-based electronic synapses. The memristive characteristics with reproducible gradual resistance tuning are utilised to mimic the activity-dependent synaptic plasticity that serves as the basis of memory and learning. Bidirectional long-term Hebbian plasticity modulation is implemented by the coactivity of pre- and postsynaptic spikes, and the sign and degree are affected by assorted factors including the temporal difference, spike rate and voltage. Moreover, synaptic saturation is observed to be an adjustment of Hebbian rules to stabilise the growth of synaptic weights. Our results may contribute to the development of highly functional plastic electronic synapses and the further construction of next-generation parallel neuromorphic computing architecture.

A Learning Model for L/M Specificity in Ganglion Cells

NASA Technical Reports Server (NTRS)

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.

Competitive STDP Learning of Overlapping Spatial Patterns.

PubMed

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.

The neuroscience of learning: beyond the Hebbian synapse.

PubMed

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.

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

PubMed

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.

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

NASA Astrophysics Data System (ADS)

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.

Hybrid Topological Lie-Hamiltonian Learning in Evolving Energy Landscapes

NASA Astrophysics Data System (ADS)

Ivancevic, Vladimir G.; Reid, Darryn J.

2015-11-01

In this Chapter, a novel bidirectional algorithm for hybrid (discrete + continuous-time) Lie-Hamiltonian evolution in adaptive energy landscape-manifold is designed and its topological representation is proposed. The algorithm is developed within a geometrically and topologically extended framework of Hopfield's neural nets and Haken's synergetics (it is currently designed in Mathematica, although with small changes it could be implemented in Symbolic C++ or any other computer algebra system). The adaptive energy manifold is determined by the Hamiltonian multivariate cost function H, based on the user-defined vehicle-fleet configuration matrix W, which represents the pseudo-Riemannian metric tensor of the energy manifold. Search for the global minimum of H is performed using random signal differential Hebbian adaptation. This stochastic gradient evolution is driven (or, pulled-down) by `gravitational forces' defined by the 2nd Lie derivatives of H. Topological changes of the fleet matrix W are observed during the evolution and its topological invariant is established. The evolution stops when the W-topology breaks down into several connectivity-components, followed by topology-breaking instability sequence (i.e., a series of phase transitions).

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

PubMed

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.

Thermodynamic efficiency of learning a rule in neural networks

NASA Astrophysics Data System (ADS)

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.

Higher-order neural networks, Polyà polynomials, and Fermi cluster diagrams

NASA Astrophysics Data System (ADS)

Kürten, K. E.; Clark, J. W.

2003-09-01

The problem of controlling higher-order interactions in neural networks is addressed with techniques commonly applied in the cluster analysis of quantum many-particle systems. For multineuron synaptic weights chosen according to a straightforward extension of the standard Hebbian learning rule, we show that higher-order contributions to the stimulus felt by a given neuron can be readily evaluated via Polyà’s combinatoric group-theoretical approach or equivalently by exploiting a precise formal analogy with fermion diagrammatics.

A spiking neural network model of self-organized pattern recognition in the early mammalian olfactory system.

PubMed

Kaplan, Bernhard A; Lansner, Anders

2014-01-01

Olfactory sensory information passes through several processing stages before an odor percept emerges. The question how the olfactory system learns to create odor representations linking those different levels and how it learns to connect and discriminate between them is largely unresolved. We present a large-scale network model with single and multi-compartmental Hodgkin-Huxley type model neurons representing olfactory receptor neurons (ORNs) in the epithelium, periglomerular cells, mitral/tufted cells and granule cells in the olfactory bulb (OB), and three types of cortical cells in the piriform cortex (PC). Odor patterns are calculated based on affinities between ORNs and odor stimuli derived from physico-chemical descriptors of behaviorally relevant real-world odorants. The properties of ORNs were tuned to show saturated response curves with increasing concentration as seen in experiments. On the level of the OB we explored the possibility of using a fuzzy concentration interval code, which was implemented through dendro-dendritic inhibition leading to winner-take-all like dynamics between mitral/tufted cells belonging to the same glomerulus. The connectivity from mitral/tufted cells to PC neurons was self-organized from a mutual information measure and by using a competitive Hebbian-Bayesian learning algorithm based on the response patterns of mitral/tufted cells to different odors yielding a distributed feed-forward projection to the PC. The PC was implemented as a modular attractor network with a recurrent connectivity that was likewise organized through Hebbian-Bayesian learning. We demonstrate the functionality of the model in a one-sniff-learning and recognition task on a set of 50 odorants. Furthermore, we study its robustness against noise on the receptor level and its ability to perform concentration invariant odor recognition. Moreover, we investigate the pattern completion capabilities of the system and rivalry dynamics for odor mixtures.

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

DOE Office of Scientific and Technical Information (OSTI.GOV)

Moody, Daniela Irina

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 detectmore » geologic, vegetative, hydrologic, and man-made features, as well as changes in these features over time.« less

Improvement and decline in tactile discrimination behavior after cortical plasticity induced by passive tactile coactivation.

PubMed

Hodzic, Amra; Veit, Ralf; Karim, Ahmed A; Erb, Michael; Godde, Ben

2004-01-14

Perceptual learning can be induced by passive tactile coactivation without attention or reinforcement. We used functional MRI (fMRI) and psychophysics to investigate in detail the specificity of this type of learning for different tactile discrimination tasks and the underlying cortical reorganization. We found that a few hours of Hebbian coactivation evoked a significant increase of primary (SI) and secondary (SII) somatosensory cortical areas representing the stimulated body parts. The amount of plastic changes was strongly correlated with improvement in spatial discrimination performance. However, in the same subjects, frequency discrimination was impaired after coactivation, indicating that even maladaptive processes can be induced by intense passive sensory stimulation.

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

NASA Astrophysics Data System (ADS)

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.

Neural learning circuits utilizing nano-crystalline silicon transistors and memristors.

PubMed

Cantley, Kurtis D; Subramaniam, Anand; Stiegler, Harvey J; Chapman, Richard A; Vogel, Eric M

2012-04-01

Properties of neural circuits are demonstrated via SPICE simulations and their applications are discussed. The neuron and synapse subcircuits include ambipolar nano-crystalline silicon transistor and memristor device models based on measured data. Neuron circuit characteristics and the Hebbian synaptic learning rule are shown to be similar to biology. Changes in the average firing rate learning rule depending on various circuit parameters are also presented. The subcircuits are then connected into larger neural networks that demonstrate fundamental properties including associative learning and pulse coincidence detection. Learned extraction of a fundamental frequency component from noisy inputs is demonstrated. It is then shown that if the fundamental sinusoid of one neuron input is out of phase with the rest, its synaptic connection changes differently than the others. Such behavior indicates that the system can learn to detect which signals are important in the general population, and that there is a spike-timing-dependent component of the learning mechanism. Finally, future circuit design and considerations are discussed, including requirements for the memristive device.

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

PubMed

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.

Visuocortical Changes During Delay and Trace Aversive Conditioning: Evidence From Steady-State Visual Evoked Potentials

PubMed Central

Miskovic, Vladimir; Keil, Andreas

2015-01-01

The visual system is biased towards sensory cues that have been associated with danger or harm through temporal co-occurrence. An outstanding question about conditioning-induced changes in visuocortical processing is the extent to which they are driven primarily by top-down factors such as expectancy or by low-level factors such as the temporal proximity between conditioned stimuli and aversive outcomes. Here, we examined this question using two different differential aversive conditioning experiments: participants learned to associate a particular grating stimulus with an aversive noise that was presented either in close temporal proximity (delay conditioning experiment) or after a prolonged stimulus-free interval (trace conditioning experiment). In both experiments we probed cue-related cortical responses by recording steady-state visual evoked potentials (ssVEPs). Although behavioral ratings indicated that all participants successfully learned to discriminate between the grating patterns that predicted the presence versus absence of the aversive noise, selective amplification of population-level responses in visual cortex for the conditioned danger signal was observed only when the grating and the noise were temporally contiguous. Our findings are in line with notions purporting that changes in the electrocortical response of visual neurons induced by aversive conditioning are a product of Hebbian associations among sensory cell assemblies rather than being driven entirely by expectancy-based, declarative processes. PMID:23398582

Dynamic DNA Methylation Controls Glutamate Receptor Trafficking and Synaptic Scaling

PubMed Central

Sweatt, J. David

2016-01-01

Hebbian plasticity, including LTP and LTD, has long been regarded as important for local circuit refinement in the context of memory formation and stabilization. However, circuit development and stabilization additionally relies on non-Hebbian, homoeostatic, forms of plasticity such as synaptic scaling. Synaptic scaling is induced by chronic increases or decreases in neuronal activity. Synaptic scaling is associated with cell-wide adjustments in postsynaptic receptor density, and can occur in a multiplicative manner resulting in preservation of relative synaptic strengths across the entire neuron's population of synapses. Both active DNA methylation and de-methylation have been validated as crucial regulators of gene transcription during learning, and synaptic scaling is known to be transcriptionally dependent. However, it has been unclear whether homeostatic forms of plasticity such as synaptic scaling are regulated via epigenetic mechanisms. This review describes exciting recent work that has demonstrated a role for active changes in neuronal DNA methylation and demethylation as a controller of synaptic scaling and glutamate receptor trafficking. These findings bring together three major categories of memory-associated mechanisms that were previously largely considered separately: DNA methylation, homeostatic plasticity, and glutamate receptor trafficking. PMID:26849493

Neuronal boost to evolutionary dynamics.

PubMed

de Vladar, Harold P; Szathmáry, Eörs

2015-12-06

Standard evolutionary dynamics is limited by the constraints of the genetic system. A central message of evolutionary neurodynamics is that evolutionary dynamics in the brain can happen in a neuronal niche in real time, despite the fact that neurons do not reproduce. We show that Hebbian learning and structural synaptic plasticity broaden the capacity for informational replication and guided variability provided a neuronally plausible mechanism of replication is in place. The synergy between learning and selection is more efficient than the equivalent search by mutation selection. We also consider asymmetric landscapes and show that the learning weights become correlated with the fitness gradient. That is, the neuronal complexes learn the local properties of the fitness landscape, resulting in the generation of variability directed towards the direction of fitness increase, as if mutations in a genetic pool were drawn such that they would increase reproductive success. Evolution might thus be more efficient within evolved brains than among organisms out in the wild.

Neural Mechanism for Stochastic Behavior During a Competitive Game

PubMed Central

Soltani, Alireza; Lee, Daeyeol; Wang, Xiao-Jing

2006-01-01

Previous studies have shown that non-human primates can generate highly stochastic choice behavior, especially when this is required during a competitive interaction with another agent. To understand the neural mechanism of such dynamic choice behavior, we propose a biologically plausible model of decision making endowed with synaptic plasticity that follows a reward-dependent stochastic Hebbian learning rule. This model constitutes a biophysical implementation of reinforcement learning, and it reproduces salient features of behavioral data from an experiment with monkeys playing a matching pennies game. Due to interaction with an opponent and learning dynamics, the model generates quasi-random behavior robustly in spite of intrinsic biases. Furthermore, non-random choice behavior can also emerge when the model plays against a non-interactive opponent, as observed in the monkey experiment. Finally, when combined with a meta-learning algorithm, our model accounts for the slow drift in the animal’s strategy based on a process of reward maximization. PMID:17015181

Learning the Gestalt rule of collinearity from object motion.

PubMed

Prodöhl, Carsten; Würtz, Rolf P; von der Malsburg, Christoph

2003-08-01

The Gestalt principle of collinearity (and curvilinearity) is widely regarded as being mediated by the long-range connection structure in primary visual cortex. We review the neurophysiological and psychophysical literature to argue that these connections are developed from visual experience after birth, relying on coherent object motion. We then present a neural network model that learns these connections in an unsupervised Hebbian fashion with input from real camera sequences. The model uses spatiotemporal retinal filtering, which is very sensitive to changes in the visual input. We show that it is crucial for successful learning to use the correlation of the transient responses instead of the sustained ones. As a consequence, learning works best with video sequences of moving objects. The model addresses a special case of the fundamental question of what represents the necessary a priori knowledge the brain is equipped with at birth so that the self-organized process of structuring by experience can be successful.

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

PubMed

Fiorillo, Christopher D

2008-10-01

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 undifferentiated neurons, each implementing similar learning algorithms.

Modeling trial by trial and block feedback in perceptual learning

PubMed Central

Liu, Jiajuan; Dosher, Barbara; Lu, Zhong-Lin

2014-01-01

Feedback has been shown to play a complex role in visual perceptual learning. It is necessary for performance improvement in some conditions while not others. Different forms of feedback, such as trial-by-trial feedback or block feedback, may both facilitate learning, but with different mechanisms. False feedback can abolish learning. We account for all these results with the Augmented Hebbian Reweight Model (AHRM). Specifically, three major factors in the model advance performance improvement: the external trial-by-trial feedback when available, the self-generated output as an internal feedback when no external feedback is available, and the adaptive criterion control based on the block feedback. Through simulating a comprehensive feedback study (Herzog & Fahle 1997, Vision Research, 37 (15), 2133–2141), we show that the model predictions account for the pattern of learning in seven major feedback conditions. The AHRM can fully explain the complex empirical results on the role of feedback in visual perceptual learning. PMID:24423783

Stereo, Shading, and Surfaces: Curvature Constraints Couple Neural Computations

DTIC Science & Technology

2014-04-23

Bullier, and J. S. Lund, ‘‘Circuits for local and global signal integration in primary visual cortex,’’ J. Neurosci ., vol. 22, no. 19, pp. 8633–8646...cortex,’’ J. Neurosci ., vol. 17, no. 6, pp. 2112–2127, Mar. 15, 1997. [16] Y. Boykov, O. Veksler, and R. Zabih, ‘‘Fast approximate energy minimization...plasticity: A Hebbian learning rule,’’ Annu. Rev. Neurosci ., vol. 31, pp. 25–46, 2008. [19] V. A. Casagrande and J. H. Kaas, ‘‘The afferent, intrinsic

Models of Acetylcholine and Dopamine Signals Differentially Improve Neural Representations

PubMed Central

Holca-Lamarre, Raphaël; Lücke, Jörg; Obermayer, Klaus

2017-01-01

Biological and artificial neural networks (ANNs) represent input signals as patterns of neural activity. In biology, neuromodulators can trigger important reorganizations of these neural representations. For instance, pairing a stimulus with the release of either acetylcholine (ACh) or dopamine (DA) evokes long lasting increases in the responses of neurons to the paired stimulus. The functional roles of ACh and DA in rearranging representations remain largely unknown. Here, we address this question using a Hebbian-learning neural network model. Our aim is both to gain a functional understanding of ACh and DA transmission in shaping biological representations and to explore neuromodulator-inspired learning rules for ANNs. We model the effects of ACh and DA on synaptic plasticity and confirm that stimuli coinciding with greater neuromodulator activation are over represented in the network. We then simulate the physiological release schedules of ACh and DA. We measure the impact of neuromodulator release on the network's representation and on its performance on a classification task. We find that ACh and DA trigger distinct changes in neural representations that both improve performance. The putative ACh signal redistributes neural preferences so that more neurons encode stimulus classes that are challenging for the network. The putative DA signal adapts synaptic weights so that they better match the classes of the task at hand. Our model thus offers a functional explanation for the effects of ACh and DA on cortical representations. Additionally, our learning algorithm yields performances comparable to those of state-of-the-art optimisation methods in multi-layer perceptrons while requiring weaker supervision signals and interacting with synaptically-local weight updates. PMID:28690509

Neural network regulation driven by autonomous neural firings

NASA Astrophysics Data System (ADS)

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.

Modelling the formation of working memory with networks of integrate-and-fire neurons connected by plastic synapses.

PubMed

Del Giudice, Paolo; Fusi, Stefano; Mattia, Maurizio

2003-01-01

In this paper we review a series of works concerning models of spiking neurons interacting via spike-driven, plastic, Hebbian synapses, meant to implement stimulus driven, unsupervised formation of working memory (WM) states. Starting from a summary of the experimental evidence emerging from delayed matching to sample (DMS) experiments, we briefly review the attractor picture proposed to underlie WM states. We then describe a general framework for a theoretical approach to learning with synapses subject to realistic constraints and outline some general requirements to be met by a mechanism of Hebbian synaptic structuring. We argue that a stochastic selection of the synapses to be updated allows for optimal memory storage, even if the number of stable synaptic states is reduced to the extreme (bistable synapses). A description follows of models of spike-driven synapses that implement the stochastic selection by exploiting the high irregularity in the pre- and post-synaptic activity. Reasons are listed why dynamic learning, that is the process by which the synaptic structure develops under the only guidance of neural activities, driven in turn by stimuli, is hard to accomplish. We provide a 'feasibility proof' of dynamic formation of WM states in this context the beneficial role of short-term depression (STD) is illustrated. by showing how an initially unstructured network autonomously develops a synaptic structure supporting simultaneously stable spontaneous and WM states in this context the beneficial role of short-term depression (STD) is illustrated. After summarizing heuristic indications emerging from the study performed, we conclude by briefly discussing open problems and critical issues still to be clarified.

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

PubMed

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.

MOLECULAR MECHANISMS OF FEAR LEARNING AND MEMORY

PubMed Central

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

Complementary roles for amygdala and periaqueductal gray in temporal-difference fear learning.

PubMed

Cole, Sindy; McNally, Gavan P

2009-01-01

Pavlovian fear conditioning is not a unitary process. At the neurobiological level multiple brain regions and neurotransmitters contribute to fear learning. At the behavioral level many variables contribute to fear learning including the physical salience of the events being learned about, the direction and magnitude of predictive error, and the rate at which these are learned about. These experiments used a serial compound conditioning design to determine the roles of basolateral amygdala (BLA) NMDA receptors and ventrolateral midbrain periaqueductal gray (vlPAG) mu-opioid receptors (MOR) in predictive fear learning. Rats received a three-stage design, which arranged for both positive and negative prediction errors producing bidirectional changes in fear learning within the same subjects during the test stage. Intra-BLA infusion of the NR2B receptor antagonist Ifenprodil prevented all learning. In contrast, intra-vlPAG infusion of the MOR antagonist CTAP enhanced learning in response to positive predictive error but impaired learning in response to negative predictive error--a pattern similar to Hebbian learning and an indication that fear learning had been divorced from predictive error. These findings identify complementary but dissociable roles for amygdala NMDA receptors and vlPAG MOR in temporal-difference predictive fear learning.

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

NASA Astrophysics Data System (ADS)

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 new modulated Hebbian learning rule--biologically plausible method for local computation of a principal subspace.

PubMed

Jankovic, Marko; Ogawa, Hidemitsu

2003-08-01

This paper presents one possible implementation of a transformation that performs linear mapping to a lower-dimensional subspace. Principal component subspace will be the one that will be analyzed. Idea implemented in this paper represents generalization of the recently proposed infinity OH neural method for principal component extraction. The calculations in the newly proposed method are performed locally--a feature which is usually considered as desirable from the biological point of view. Comparing to some other wellknown methods, proposed synaptic efficacy learning rule requires less information about the value of the other efficacies to make single efficacy modification. Synaptic efficacies are modified by implementation of Modulated Hebb-type (MH) learning rule. Slightly modified MH algorithm named Modulated Hebb Oja (MHO) algorithm, will be also introduced. Structural similarity of the proposed network with part of the retinal circuit will be presented, too.

Induction of Anti-Hebbian LTP in CA1 Stratum Oriens Interneurons: Interactions between Group I Metabotropic Glutamate Receptors and M1 Muscarinic Receptors

PubMed Central

Savary, Etienne; Kullmann, Dimitri M.; Miles, Richard

2015-01-01

An anti-Hebbian form of LTP is observed at excitatory synapses made with some hippocampal interneurons. LTP induction is facilitated when postsynaptic interneurons are hyperpolarized, presumably because Ca2+ entry through Ca2+-permeable glutamate receptors is enhanced. The contribution of modulatory transmitters to anti-Hebbian LTP induction remains to be established. Activation of group I metabotropic receptors (mGluRs) is required for anti-Hebbian LTP induction in interneurons with cell bodies in the CA1 stratum oriens. This region receives a strong cholinergic innervation from the septum, and muscarinic acetylcholine receptors (mAChRs) share some signaling pathways and cooperate with mGluRs in the control of neuronal excitability. We therefore examined possible interactions between group I mGluRs and mAChRs in anti-Hebbian LTP at synapses which excite oriens interneurons in rat brain slices. We found that blockade of either group I mGluRs or M1 mAChRs prevented the induction of anti-Hebbian LTP by pairing presynaptic activity with postsynaptic hyperpolarization. Blocking either receptor also suppressed long-term effects of activation of the other G-protein coupled receptor on interneuron membrane potential. However, no crossed blockade was detected for mGluR or mAchR effects on interneuron after-burst potentials or on the frequency of miniature EPSPs. Paired recordings between pyramidal neurons and oriens interneurons were obtained to determine whether LTP could be induced without concurrent stimulation of cholinergic axons. Exogenous activation of mAChRs led to LTP, with changes in EPSP amplitude distributions consistent with a presynaptic locus of expression. LTP, however, required noninvasive presynaptic and postsynaptic recordings. SIGNIFICANCE STATEMENT In the hippocampus, a form of NMDA receptor-independent long-term potentiation (LTP) occurs at excitatory synapses made on some inhibitory neurons. This is preferentially induced when postsynaptic interneurons are hyperpolarized, depends on Ca2+ entry through Ca2+-permeable AMPA receptors, and has been labeled anti-Hebbian LTP. Here we show that this form of LTP also depends on activation of both group I mGluR and M1 mAChRs. We demonstrate that these G-protein coupled receptors (GPCRs) interact, because the blockade of one receptor suppresses long-term effects of activation of the other GPCR on both LTP and interneuron membrane potential. This LTP was also detected in paired recordings, although only when both presynaptic and postsynaptic recordings did not perturb the intracellular medium. Changes in EPSP amplitude distributions in dual recordings were consistent with a presynaptic locus of expression. PMID:26446209

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

PubMed

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.

Neuronal boost to evolutionary dynamics

PubMed Central

de Vladar, Harold P.; Szathmáry, Eörs

2015-01-01

Standard evolutionary dynamics is limited by the constraints of the genetic system. A central message of evolutionary neurodynamics is that evolutionary dynamics in the brain can happen in a neuronal niche in real time, despite the fact that neurons do not reproduce. We show that Hebbian learning and structural synaptic plasticity broaden the capacity for informational replication and guided variability provided a neuronally plausible mechanism of replication is in place. The synergy between learning and selection is more efficient than the equivalent search by mutation selection. We also consider asymmetric landscapes and show that the learning weights become correlated with the fitness gradient. That is, the neuronal complexes learn the local properties of the fitness landscape, resulting in the generation of variability directed towards the direction of fitness increase, as if mutations in a genetic pool were drawn such that they would increase reproductive success. Evolution might thus be more efficient within evolved brains than among organisms out in the wild. PMID:26640653

Autonomous learning in gesture recognition by using lobe component analysis

NASA Astrophysics Data System (ADS)

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.

Associative Learning in Invertebrates

PubMed Central

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

A Spiking Neurocomputational Model of High-Frequency Oscillatory Brain Responses to Words and Pseudowords

PubMed Central

Garagnani, Max; Lucchese, Guglielmo; Tomasello, Rosario; Wennekers, Thomas; Pulvermüller, Friedemann

2017-01-01

Experimental evidence indicates that neurophysiological responses to well-known meaningful sensory items and symbols (such as familiar objects, faces, or words) differ from those to matched but novel and senseless materials (unknown objects, scrambled faces, and pseudowords). Spectral responses in the high beta- and gamma-band have been observed to be generally stronger to familiar stimuli than to unfamiliar ones. These differences have been hypothesized to be caused by the activation of distributed neuronal circuits or cell assemblies, which act as long-term memory traces for learned familiar items only. Here, we simulated word learning using a biologically constrained neurocomputational model of the left-hemispheric cortical areas known to be relevant for language and conceptual processing. The 12-area spiking neural-network architecture implemented replicates physiological and connectivity features of primary, secondary, and higher-association cortices in the frontal, temporal, and occipital lobes of the human brain. We simulated elementary aspects of word learning in it, focussing specifically on semantic grounding in action and perception. As a result of spike-driven Hebbian synaptic plasticity mechanisms, distributed, stimulus-specific cell-assembly (CA) circuits spontaneously emerged in the network. After training, presentation of one of the learned “word” forms to the model correlate of primary auditory cortex induced periodic bursts of activity within the corresponding CA, leading to oscillatory phenomena in the entire network and spontaneous across-area neural synchronization. Crucially, Morlet wavelet analysis of the network's responses recorded during presentation of learned meaningful “word” and novel, senseless “pseudoword” patterns revealed stronger induced spectral power in the gamma-band for the former than the latter, closely mirroring differences found in neurophysiological data. Furthermore, coherence analysis of the simulated responses uncovered dissociated category specific patterns of synchronous oscillations in distant cortical areas, including indirectly connected primary sensorimotor areas. Bridging the gap between cellular-level mechanisms, neuronal-population behavior, and cognitive function, the present model constitutes the first spiking, neurobiologically, and anatomically realistic model able to explain high-frequency oscillatory phenomena indexing language processing on the basis of dynamics and competitive interactions of distributed cell-assembly circuits which emerge in the brain as a result of Hebbian learning and sensorimotor experience. PMID:28149276

Social biases determine spatiotemporal sparseness of ciliate mating heuristics.

PubMed

Clark, Kevin B

2012-01-01

Ciliates become highly social, even displaying animal-like qualities, in the joint presence of aroused conspecifics and nonself mating pheromones. Pheromone detection putatively helps trigger instinctual and learned courtship and dominance displays from which social judgments are made about the availability, compatibility, and fitness representativeness or likelihood of prospective mates and rivals. In earlier studies, I demonstrated the heterotrich Spirostomum ambiguum improves mating competence by effecting preconjugal strategies and inferences in mock social trials via behavioral heuristics built from Hebbian-like associative learning. Heuristics embody serial patterns of socially relevant action that evolve into ordered, topologically invariant computational networks supporting intra- and intermate selection. S. ambiguum employs heuristics to acquire, store, plan, compare, modify, select, and execute sets of mating propaganda. One major adaptive constraint over formation and use of heuristics involves a ciliate's initial subjective bias, responsiveness, or preparedness, as defined by Stevens' Law of subjective stimulus intensity, for perceiving the meaningfulness of mechanical pressures accompanying cell-cell contacts and additional perimating events. This bias controls durations and valences of nonassociative learning, search rates for appropriate mating strategies, potential net reproductive payoffs, levels of social honesty and deception, successful error diagnosis and correction of mating signals, use of insight or analysis to solve mating dilemmas, bioenergetics expenditures, and governance of mating decisions by classical or quantum statistical mechanics. I now report this same social bias also differentially affects the spatiotemporal sparseness, as measured with metric entropy, of ciliate heuristics. Sparseness plays an important role in neural systems through optimizing the specificity, efficiency, and capacity of memory representations. The present findings indicate sparseness performs a similar function in single aneural cells by tuning the size and density of encoded computational architectures useful for decision making in social contexts.

Social biases determine spatiotemporal sparseness of ciliate mating heuristics

PubMed Central

2012-01-01

Ciliates become highly social, even displaying animal-like qualities, in the joint presence of aroused conspecifics and nonself mating pheromones. Pheromone detection putatively helps trigger instinctual and learned courtship and dominance displays from which social judgments are made about the availability, compatibility, and fitness representativeness or likelihood of prospective mates and rivals. In earlier studies, I demonstrated the heterotrich Spirostomum ambiguum improves mating competence by effecting preconjugal strategies and inferences in mock social trials via behavioral heuristics built from Hebbian-like associative learning. Heuristics embody serial patterns of socially relevant action that evolve into ordered, topologically invariant computational networks supporting intra- and intermate selection. S. ambiguum employs heuristics to acquire, store, plan, compare, modify, select, and execute sets of mating propaganda. One major adaptive constraint over formation and use of heuristics involves a ciliate’s initial subjective bias, responsiveness, or preparedness, as defined by Stevens’ Law of subjective stimulus intensity, for perceiving the meaningfulness of mechanical pressures accompanying cell-cell contacts and additional perimating events. This bias controls durations and valences of nonassociative learning, search rates for appropriate mating strategies, potential net reproductive payoffs, levels of social honesty and deception, successful error diagnosis and correction of mating signals, use of insight or analysis to solve mating dilemmas, bioenergetics expenditures, and governance of mating decisions by classical or quantum statistical mechanics. I now report this same social bias also differentially affects the spatiotemporal sparseness, as measured with metric entropy, of ciliate heuristics. Sparseness plays an important role in neural systems through optimizing the specificity, efficiency, and capacity of memory representations. The present findings indicate sparseness performs a similar function in single aneural cells by tuning the size and density of encoded computational architectures useful for decision making in social contexts. PMID:22482001

A neuroanatomically grounded Hebbian-learning model of attention–language interactions in the human brain

PubMed Central

Garagnani, Max; Wennekers, Thomas; Pulvermüller, Friedemann

2008-01-01

Meaningful familiar stimuli and senseless unknown materials lead to different patterns of brain activation. A late major neurophysiological response indexing ‘sense’ is the negative component of event-related potential peaking at around 400 ms (N400), an event-related potential that emerges in attention-demanding tasks and is larger for senseless materials (e.g. meaningless pseudowords) than for matched meaningful stimuli (words). However, the mismatch negativity (latency 100–250 ms), an early automatic brain response elicited under distraction, is larger to words than to pseudowords, thus exhibiting the opposite pattern to that seen for the N400. So far, no theoretical account has been able to reconcile and explain these findings by means of a single, mechanistic neural model. We implemented a neuroanatomically grounded neural network model of the left perisylvian language cortex and simulated: (i) brain processes of early language acquisition and (ii) cortical responses to familiar word and senseless pseudoword stimuli. We found that variation of the area-specific inhibition (the model correlate of attention) modulated the simulated brain response to words and pseudowords, producing either an N400- or a mismatch negativity-like response depending on the amount of inhibition (i.e. available attentional resources). Our model: (i) provides a unifying explanatory account, at cortical level, of experimental observations that, so far, had not been given a coherent interpretation within a single framework; (ii) demonstrates the viability of purely Hebbian, associative learning in a multilayered neural network architecture; and (iii) makes clear predictions on the effects of attention on latency and magnitude of event-related potentials to lexical items. Such predictions have been confirmed by recent experimental evidence. PMID:18215243

Molecular mechanisms of fear learning and memory.

PubMed

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.

DCS-Neural-Network Program for Aircraft Control and Testing

NASA Technical Reports Server (NTRS)

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.

Acquisition of automatic imitation is sensitive to sensorimotor contingency.

PubMed

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.

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

PubMed

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.

Synergistic Gating of Electro-Iono-Photoactive 2D Chalcogenide Neuristors: Coexistence of Hebbian and Homeostatic Synaptic Metaplasticity.

PubMed

John, Rohit Abraham; Liu, Fucai; Chien, Nguyen Anh; Kulkarni, Mohit R; Zhu, Chao; Fu, Qundong; Basu, Arindam; Liu, Zheng; Mathews, Nripan

2018-06-01

Emulation of brain-like signal processing with thin-film devices can lay the foundation for building artificially intelligent learning circuitry in future. Encompassing higher functionalities into single artificial neural elements will allow the development of robust neuromorphic circuitry emulating biological adaptation mechanisms with drastically lesser neural elements, mitigating strict process challenges and high circuit density requirements necessary to match the computational complexity of the human brain. Here, 2D transition metal di-chalcogenide (MoS 2 ) neuristors are designed to mimic intracellular ion endocytosis-exocytosis dynamics/neurotransmitter-release in chemical synapses using three approaches: (i) electronic-mode: a defect modulation approach where the traps at the semiconductor-dielectric interface are perturbed; (ii) ionotronic-mode: where electronic responses are modulated via ionic gating; and (iii) photoactive-mode: harnessing persistent photoconductivity or trap-assisted slow recombination mechanisms. Exploiting a novel multigated architecture incorporating electrical and optical biases, this incarnation not only addresses different charge-trapping probabilities to finely modulate the synaptic weights, but also amalgamates neuromodulation schemes to achieve "plasticity of plasticity-metaplasticity" via dynamic control of Hebbian spike-time dependent plasticity and homeostatic regulation. Coexistence of such multiple forms of synaptic plasticity increases the efficacy of memory storage and processing capacity of artificial neuristors, enabling design of highly efficient novel neural architectures. © 2018 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Is the Role of External Feedback in Auditory Skill Learning Age Dependent?

PubMed

Zaltz, Yael; Roth, Daphne Ari-Even; Kishon-Rabin, Liat

2017-12-20

The purpose of this study is to investigate the role of external feedback in auditory perceptual learning of school-age children as compared with that of adults. Forty-eight children (7-9 years of age) and 64 adults (20-35 years of age) conducted a training session using an auditory frequency discrimination (difference limen for frequency) task, with external feedback (EF) provided for half of them. Data supported the following findings: (a) Children learned the difference limen for frequency task only when EF was provided. (b) The ability of the children to benefit from EF was associated with better cognitive skills. (c) Adults showed significant learning whether EF was provided or not. (d) In children, within-session learning following training was dependent on the provision of feedback, whereas between-sessions learning occurred irrespective of feedback. EF was found beneficial for auditory skill learning of 7-9-year-old children but not for young adults. The data support the supervised Hebbian model for auditory skill learning, suggesting combined bottom-up internal neural feedback controlled by top-down monitoring. In the case of immature executive functions, EF enhanced auditory skill learning. This study has implications for the design of training protocols in the auditory modality for different age groups, as well as for special populations.

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

PubMed

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.

Change detection and classification of land cover in multispectral satellite imagery using clustering of sparse approximations (CoSA) over learned feature dictionaries

DOE Office of Scientific and Technical Information (OSTI.GOV)

Moody, Daniela I.; Brumby, Steven P.; Rowland, Joel C.

Neuromimetic machine vision and pattern recognition algorithms are of great interest for landscape characterization and change detection in satellite imagery in support of global climate change science and modeling. We present results from an ongoing effort to extend machine vision methods to the environmental sciences, using adaptive sparse signal processing combined with machine learning. A Hebbian learning rule is used to build multispectral, multiresolution dictionaries from regional satellite normalized band difference index data. Land cover labels are automatically generated via our CoSA algorithm: Clustering of Sparse Approximations, using a clustering distance metric that combines spectral and spatial textural characteristics tomore » help separate geologic, vegetative, and hydrologie features. We demonstrate our method on example Worldview-2 satellite images of an Arctic region, and use CoSA labels to detect seasonal surface changes. In conclusion, our results suggest that neuroscience-based models are a promising approach to practical pattern recognition and change detection problems in remote sensing.« less

Change detection and classification of land cover in multispectral satellite imagery using clustering of sparse approximations (CoSA) over learned feature dictionaries

DOE PAGES

Moody, Daniela I.; Brumby, Steven P.; Rowland, Joel C.; ...

2014-10-01

Neuromimetic machine vision and pattern recognition algorithms are of great interest for landscape characterization and change detection in satellite imagery in support of global climate change science and modeling. We present results from an ongoing effort to extend machine vision methods to the environmental sciences, using adaptive sparse signal processing combined with machine learning. A Hebbian learning rule is used to build multispectral, multiresolution dictionaries from regional satellite normalized band difference index data. Land cover labels are automatically generated via our CoSA algorithm: Clustering of Sparse Approximations, using a clustering distance metric that combines spectral and spatial textural characteristics tomore » help separate geologic, vegetative, and hydrologie features. We demonstrate our method on example Worldview-2 satellite images of an Arctic region, and use CoSA labels to detect seasonal surface changes. In conclusion, our results suggest that neuroscience-based models are a promising approach to practical pattern recognition and change detection problems in remote sensing.« less

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

PubMed

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.

The HTM Spatial Pooler-A Neocortical Algorithm for Online Sparse Distributed Coding.

PubMed

Cui, Yuwei; Ahmad, Subutai; Hawkins, Jeff

2017-01-01

Hierarchical temporal memory (HTM) provides a theoretical framework that models several key computational principles of the neocortex. In this paper, we analyze an important component of HTM, the HTM spatial pooler (SP). The SP models how neurons learn feedforward connections and form efficient representations of the input. It converts arbitrary binary input patterns into sparse distributed representations (SDRs) using a combination of competitive Hebbian learning rules and homeostatic excitability control. We describe a number of key properties of the SP, including fast adaptation to changing input statistics, improved noise robustness through learning, efficient use of cells, and robustness to cell death. In order to quantify these properties we develop a set of metrics that can be directly computed from the SP outputs. We show how the properties are met using these metrics and targeted artificial simulations. We then demonstrate the value of the SP in a complete end-to-end real-world HTM system. We discuss the relationship with neuroscience and previous studies of sparse coding. The HTM spatial pooler represents a neurally inspired algorithm for learning sparse representations from noisy data streams in an online fashion.

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

PubMed

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.

Bimodal stimulus timing-dependent plasticity in primary auditory cortex is altered after noise exposure with and without tinnitus

PubMed Central

Koehler, Seth D.; Shore, Susan E.

2015-01-01

Central auditory circuits are influenced by the somatosensory system, a relationship that may underlie tinnitus generation. In the guinea pig dorsal cochlear nucleus (DCN), pairing spinal trigeminal nucleus (Sp5) stimulation with tones at specific intervals and orders facilitated or suppressed subsequent tone-evoked neural responses, reflecting spike timing-dependent plasticity (STDP). Furthermore, after noise-induced tinnitus, bimodal responses in DCN were shifted from Hebbian to anti-Hebbian timing rules with less discrete temporal windows, suggesting a role for bimodal plasticity in tinnitus. Here, we aimed to determine if multisensory STDP principles like those in DCN also exist in primary auditory cortex (A1), and whether they change following noise-induced tinnitus. Tone-evoked and spontaneous neural responses were recorded before and 15 min after bimodal stimulation in which the intervals and orders of auditory-somatosensory stimuli were randomized. Tone-evoked and spontaneous firing rates were influenced by the interval and order of the bimodal stimuli, and in sham-controls Hebbian-like timing rules predominated as was seen in DCN. In noise-exposed animals with and without tinnitus, timing rules shifted away from those found in sham-controls to more anti-Hebbian rules. Only those animals with evidence of tinnitus showed increased spontaneous firing rates, a purported neurophysiological correlate of tinnitus in A1. Together, these findings suggest that bimodal plasticity is also evident in A1 following noise damage and may have implications for tinnitus generation and therapeutic intervention across the central auditory circuit. PMID:26289461

Imitation, empathy, and mirror neurons.

PubMed

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.

Unsupervised segmentation with dynamical units.

PubMed

Rao, A Ravishankar; Cecchi, Guillermo A; Peck, Charles C; Kozloski, James R

2008-01-01

In this paper, we present a novel network to separate mixtures of inputs that have been previously learned. A significant capability of the network is that it segments the components of each input object that most contribute to its classification. The network consists of amplitude-phase units that can synchronize their dynamics, so that separation is determined by the amplitude of units in an output layer, and segmentation by phase similarity between input and output layer units. Learning is unsupervised and based on a Hebbian update, and the architecture is very simple. Moreover, efficient segmentation can be achieved even when there is considerable superposition of the inputs. The network dynamics are derived from an objective function that rewards sparse coding in the generalized amplitude-phase variables. We argue that this objective function can provide a possible formal interpretation of the binding problem and that the implementation of the network architecture and dynamics is biologically plausible.

Acquired fears reflected in cortical sensory processing: A review of electrophysiological studies of human classical conditioning

PubMed Central

Miskovic, Vladimir; Keil, Andreas

2012-01-01

The capacity to associate neutral stimuli with affective value is an important survival strategy that can be accomplished by cell assemblies obeying Hebbian learning principles. In the neuroscience laboratory, classical fear conditioning has been extensively used as a model to study learning related changes in neural structure and function. Here, we review the effects of classical fear conditioning on electromagnetic brain activity in humans, focusing on how sensory systems adapt to changing fear-related contingencies. By considering spatio-temporal patterns of mass neuronal activity we illustrate a range of cortical changes related to a retuning of neuronal sensitivity to amplify signals consistent with fear-associated stimuli at the cost of other sensory information. Putative mechanisms that may underlie fear-associated plasticity at the level of the sensory cortices are briefly considered and several avenues for future work are outlined. PMID:22891639

Decomposition of Rotor Hopfield Neural Networks Using Complex Numbers.

PubMed

Kobayashi, Masaki

2018-04-01

A complex-valued Hopfield neural network (CHNN) is a multistate model of a Hopfield neural network. It has the disadvantage of low noise tolerance. Meanwhile, a symmetric CHNN (SCHNN) is a modification of a CHNN that improves noise tolerance. Furthermore, a rotor Hopfield neural network (RHNN) is an extension of a CHNN. It has twice the storage capacity of CHNNs and SCHNNs, and much better noise tolerance than CHNNs, although it requires twice many connection parameters. In this brief, we investigate the relations between CHNN, SCHNN, and RHNN; an RHNN is uniquely decomposed into a CHNN and SCHNN. In addition, the Hebbian learning rule for RHNNs is decomposed into those for CHNNs and SCHNNs.

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

PubMed Central

Sinapayen, Lana; 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. PMID:28158309

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

PubMed

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.

Land cover classification in multispectral imagery using clustering of sparse approximations over learned feature dictionaries

DOE PAGES

Moody, Daniela I.; Brumby, Steven P.; Rowland, Joel C.; ...

2014-12-09

We present results from an ongoing effort to extend neuromimetic machine vision algorithms to multispectral data using adaptive signal processing combined with compressive sensing and machine learning techniques. Our goal is to develop a robust classification methodology that will allow for automated discretization of the landscape into distinct units based on attributes such as vegetation, surface hydrological properties, and topographic/geomorphic characteristics. We use a Hebbian learning rule to build spectral-textural dictionaries that are tailored for classification. We learn our dictionaries from millions of overlapping multispectral image patches and then use a pursuit search to generate classification features. Land cover labelsmore » are automatically generated using unsupervised clustering of sparse approximations (CoSA). We demonstrate our method on multispectral WorldView-2 data from a coastal plain ecosystem in Barrow, Alaska. We explore learning from both raw multispectral imagery and normalized band difference indices. We explore a quantitative metric to evaluate the spectral properties of the clusters in order to potentially aid in assigning land cover categories to the cluster labels. In this study, our results suggest CoSA is a promising approach to unsupervised land cover classification in high-resolution satellite imagery.« less

Learning to attend: modeling the shaping of selectivity in infero-temporal cortex in a categorization task.

PubMed

Szabo, Miruna; Deco, Gustavo; Fusi, Stefano; Del Giudice, Paolo; Mattia, Maurizio; Stetter, Martin

2006-05-01

Recent experiments on behaving monkeys have shown that learning a visual categorization task makes the neurons in infero-temporal cortex (ITC) more selective to the task-relevant features of the stimuli (Sigala and Logothetis in Nature 415 318-320, 2002). We hypothesize that such a selectivity modulation emerges from the interaction between ITC and other cortical area, presumably the prefrontal cortex (PFC), where the previously learned stimulus categories are encoded. We propose a biologically inspired model of excitatory and inhibitory spiking neurons with plastic synapses, modified according to a reward based Hebbian learning rule, to explain the experimental results and test the validity of our hypothesis. We assume that the ITC neurons, receiving feature selective inputs, form stronger connections with the category specific neurons to which they are consistently associated in rewarded trials. After learning, the top-down influence of PFC neurons enhances the selectivity of the ITC neurons encoding the behaviorally relevant features of the stimuli, as observed in the experiments. We conclude that the perceptual representation in visual areas like ITC can be strongly affected by the interaction with other areas which are devoted to higher cognitive functions.

Bayesian Inference and Online Learning in Poisson Neuronal Networks.

PubMed

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.

Optimizing one-shot learning with binary synapses.

PubMed

Romani, Sandro; Amit, Daniel J; Amit, Yali

2008-08-01

A network of excitatory synapses trained with a conservative version of Hebbian learning is used as a model for recognizing the familiarity of thousands of once-seen stimuli from those never seen before. Such networks were initially proposed for modeling memory retrieval (selective delay activity). We show that the same framework allows the incorporation of both familiarity recognition and memory retrieval, and estimate the network's capacity. In the case of binary neurons, we extend the analysis of Amit and Fusi (1994) to obtain capacity limits based on computations of signal-to-noise ratio of the field difference between selective and non-selective neurons of learned signals. We show that with fast learning (potentiation probability approximately 1), the most recently learned patterns can be retrieved in working memory (selective delay activity). A much higher number of once-seen learned patterns elicit a realistic familiarity signal in the presence of an external field. With potentiation probability much less than 1 (slow learning), memory retrieval disappears, whereas familiarity recognition capacity is maintained at a similarly high level. This analysis is corroborated in simulations. For analog neurons, where such analysis is more difficult, we simplify the capacity analysis by studying the excess number of potentiated synapses above the steady-state distribution. In this framework, we derive the optimal constraint between potentiation and depression probabilities that maximizes the capacity.

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

PubMed

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.

Geometry of the perceptual space

NASA Astrophysics Data System (ADS)

Assadi, Amir H.; Palmer, Stephen; Eghbalnia, Hamid; Carew, John

1999-09-01

The concept of space and geometry varies across the subjects. Following Poincare, we consider the construction of the perceptual space as a continuum equipped with a notion of magnitude. The study of the relationships of objects in the perceptual space gives rise to what we may call perceptual geometry. Computational modeling of objects and investigation of their deeper perceptual geometrical properties (beyond qualitative arguments) require a mathematical representation of the perceptual space. Within the realm of such a mathematical/computational representation, visual perception can be studied as in the well-understood logic-based geometry. This, however, does not mean that one could reduce all problems of visual perception to their geometric counterparts. Rather, visual perception as reported by a human observer, has a subjective factor that could be analytically quantified only through statistical reasoning and in the course of repetitive experiments. Thus, the desire to experimentally verify the statements in perceptual geometry leads to an additional probabilistic structure imposed on the perceptual space, whose amplitudes are measured through intervention by human observers. We propose a model for the perceptual space and the case of perception of textured surfaces as a starting point for object recognition. To rigorously present these ideas and propose computational simulations for testing the theory, we present the model of the perceptual geometry of surfaces through an amplification of theory of Riemannian foliation in differential topology, augmented by statistical learning theory. When we refer to the perceptual geometry of a human observer, the theory takes into account the Bayesian formulation of the prior state of the knowledge of the observer and Hebbian learning. We use a Parallel Distributed Connectionist paradigm for computational modeling and experimental verification of our theory.

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

PubMed

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.

Pattern Learning, Damage and Repair within Biological Neural Networks

NASA Astrophysics Data System (ADS)

Siu, Theodore; Fitzgerald O'Neill, Kate; Shinbrot, Troy

2015-03-01

Traumatic brain injury (TBI) causes damage to neural networks, potentially leading to disability or even death. Nearly one in ten of these patients die, and most of the remainder suffer from symptoms ranging from headaches and nausea to convulsions and paralysis. In vitro studies to develop treatments for TBI have limited in vivo applicability, and in vitro therapies have even proven to worsen the outcome of TBI patients. We propose that this disconnect between in vitro and in vivo outcomes may be associated with the fact that in vitro tests assess indirect measures of neuronal health, but do not investigate the actual function of neuronal networks. Therefore in this talk, we examine both in vitro and in silico neuronal networks that actually perform a function: pattern identification. We allow the networks to execute genetic, Hebbian, learning, and additionally, we examine the effects of damage and subsequent repair within our networks. We show that the length of repaired connections affects the overall pattern learning performance of the network and we propose therapies that may improve function following TBI in clinical settings.

Cultured Cortical Neurons Can Perform Blind Source Separation According to the Free-Energy Principle

PubMed Central

Isomura, Takuya; Kotani, Kiyoshi; Jimbo, Yasuhiko

2015-01-01

Blind source separation is the computation underlying the cocktail party effect––a partygoer can distinguish a particular talker’s voice from the ambient noise. Early studies indicated that the brain might use blind source separation as a signal processing strategy for sensory perception and numerous mathematical models have been proposed; however, it remains unclear how the neural networks extract particular sources from a complex mixture of inputs. We discovered that neurons in cultures of dissociated rat cortical cells could learn to represent particular sources while filtering out other signals. Specifically, the distinct classes of neurons in the culture learned to respond to the distinct sources after repeating training stimulation. Moreover, the neural network structures changed to reduce free energy, as predicted by the free-energy principle, a candidate unified theory of learning and memory, and by Jaynes’ principle of maximum entropy. This implicit learning can only be explained by some form of Hebbian plasticity. These results are the first in vitro (as opposed to in silico) demonstration of neural networks performing blind source separation, and the first formal demonstration of neuronal self-organization under the free energy principle. PMID:26690814

Nothing can be coincidence: synaptic inhibition and plasticity in the cerebellar nuclei

PubMed Central

Pugh, Jason R.; Raman, Indira M.

2009-01-01

Many cerebellar neurons fire spontaneously, generating 10–100 action potentials per second even without synaptic input. This high basal activity correlates with information-coding mechanisms that differ from those of cells that are quiescent until excited synaptically. For example, in the deep cerebellar nuclei, Hebbian patterns of coincident synaptic excitation and postsynaptic firing fail to induce long-term increases in the strength of excitatory inputs. Instead, excitatory synaptic currents are potentiated by combinations of inhibition and excitation that resemble the activity of Purkinje and mossy fiber afferents that is predicted to occur during cerebellar associative learning tasks. Such results indicate that circuits with intrinsically active neurons have rules for information transfer and storage that distinguish them from other brain regions. PMID:19178955

Learning of Chunking Sequences in Cognition and Behavior

PubMed Central

Rabinovich, Mikhail

2015-01-01

We often learn and recall long sequences in smaller segments, such as a phone number 858 534 22 30 memorized as four segments. Behavioral experiments suggest that humans and some animals employ this strategy of breaking down cognitive or behavioral sequences into chunks in a wide variety of tasks, but the dynamical principles of how this is achieved remains unknown. Here, we study the temporal dynamics of chunking for learning cognitive sequences in a chunking representation using a dynamical model of competing modes arranged to evoke hierarchical Winnerless Competition (WLC) dynamics. Sequential memory is represented as trajectories along a chain of metastable fixed points at each level of the hierarchy, and bistable Hebbian dynamics enables the learning of such trajectories in an unsupervised fashion. Using computer simulations, we demonstrate the learning of a chunking representation of sequences and their robust recall. During learning, the dynamics associates a set of modes to each information-carrying item in the sequence and encodes their relative order. During recall, hierarchical WLC guarantees the robustness of the sequence order when the sequence is not too long. The resulting patterns of activities share several features observed in behavioral experiments, such as the pauses between boundaries of chunks, their size and their duration. Failures in learning chunking sequences provide new insights into the dynamical causes of neurological disorders such as Parkinson’s disease and Schizophrenia. PMID:26584306

Improving KPCA Online Extraction by Orthonormalization in the Feature Space.

PubMed

Souza Filho, Joao B O; Diniz, Paulo S R

2018-04-01

Recently, some online kernel principal component analysis (KPCA) techniques based on the generalized Hebbian algorithm (GHA) were proposed for use in large data sets, defining kernel components using concise dictionaries automatically extracted from data. This brief proposes two new online KPCA extraction algorithms, exploiting orthogonalized versions of the GHA rule. In both the cases, the orthogonalization of kernel components is achieved by the inclusion of some low complexity additional steps to the kernel Hebbian algorithm, thus not substantially affecting the computational cost of the algorithm. Results show improved convergence speed and accuracy of components extracted by the proposed methods, as compared with the state-of-the-art online KPCA extraction algorithms.

Linear analysis of auto-organization in Hebbian neural networks.

PubMed

Carlos Letelier, J; Mpodozis, J

1995-01-01

The self-organization of neurotopies where neural connections follow Hebbian dynamics is framed in terms of linear operator theory. A general and exact equation describing the time evolution of the overall synaptic strength connecting two neural laminae is derived. This linear matricial equation, which is similar to the equations used to describe oscillating systems in physics, is modified by the introduction of non-linear terms, in order to capture self-organizing (or auto-organizing) processes. The behavior of a simple and small system, that contains a non-linearity that mimics a metabolic constraint, is analyzed by computer simulations. The emergence of a simple "order" (or degree of organization) in this low-dimensionality model system is discussed.

Spike-Based Bayesian-Hebbian Learning of Temporal Sequences

PubMed Central

Lindén, Henrik; Lansner, Anders

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 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. PMID:27213810

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

PubMed Central

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

Critical neural networks with short- and long-term plasticity.

PubMed

Michiels van Kessenich, L; Luković, M; de Arcangelis, L; Herrmann, H J

2018-03-01

In recent years self organized critical neuronal models have provided insights regarding the origin of the experimentally observed avalanching behavior of neuronal systems. It has been shown that dynamical synapses, as a form of short-term plasticity, can cause critical neuronal dynamics. Whereas long-term plasticity, such as Hebbian or activity dependent plasticity, have a crucial role in shaping the network structure and endowing neural systems with learning abilities. In this work we provide a model which combines both plasticity mechanisms, acting on two different time scales. The measured avalanche statistics are compatible with experimental results for both the avalanche size and duration distribution with biologically observed percentages of inhibitory neurons. The time series of neuronal activity exhibits temporal bursts leading to 1/f decay in the power spectrum. The presence of long-term plasticity gives the system the ability to learn binary rules such as xor, providing the foundation of future research on more complicated tasks such as pattern recognition.

Sequential neuromodulation of Hebbian plasticity offers mechanism for effective reward-based navigation

PubMed Central

Brzosko, Zuzanna; Zannone, Sara; Schultz, Wolfram

2017-01-01

Spike timing-dependent plasticity (STDP) is under neuromodulatory control, which is correlated with distinct behavioral states. Previously, we reported that dopamine, a reward signal, broadens the time window for synaptic potentiation and modulates the outcome of hippocampal STDP even when applied after the plasticity induction protocol (Brzosko et al., 2015). Here, we demonstrate that sequential neuromodulation of STDP by acetylcholine and dopamine offers an efficacious model of reward-based navigation. Specifically, our experimental data in mouse hippocampal slices show that acetylcholine biases STDP toward synaptic depression, whilst subsequent application of dopamine converts this depression into potentiation. Incorporating this bidirectional neuromodulation-enabled correlational synaptic learning rule into a computational model yields effective navigation toward changing reward locations, as in natural foraging behavior. Thus, temporally sequenced neuromodulation of STDP enables associations to be made between actions and outcomes and also provides a possible mechanism for aligning the time scales of cellular and behavioral learning. DOI: http://dx.doi.org/10.7554/eLife.27756.001 PMID:28691903

Critical neural networks with short- and long-term plasticity

NASA Astrophysics Data System (ADS)

Michiels van Kessenich, L.; Luković, M.; de Arcangelis, L.; Herrmann, H. J.

2018-03-01

In recent years self organized critical neuronal models have provided insights regarding the origin of the experimentally observed avalanching behavior of neuronal systems. It has been shown that dynamical synapses, as a form of short-term plasticity, can cause critical neuronal dynamics. Whereas long-term plasticity, such as Hebbian or activity dependent plasticity, have a crucial role in shaping the network structure and endowing neural systems with learning abilities. In this work we provide a model which combines both plasticity mechanisms, acting on two different time scales. The measured avalanche statistics are compatible with experimental results for both the avalanche size and duration distribution with biologically observed percentages of inhibitory neurons. The time series of neuronal activity exhibits temporal bursts leading to 1 /f decay in the power spectrum. The presence of long-term plasticity gives the system the ability to learn binary rules such as xor, providing the foundation of future research on more complicated tasks such as pattern recognition.

Functional model of biological neural networks.

PubMed

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.

Stream-based Hebbian eigenfilter for real-time neuronal spike discrimination

PubMed Central

2012-01-01

Background Principal component analysis (PCA) has been widely employed for automatic neuronal spike sorting. Calculating principal components (PCs) is computationally expensive, and requires complex numerical operations and large memory resources. Substantial hardware resources are therefore needed for hardware implementations of PCA. General Hebbian algorithm (GHA) has been proposed for calculating PCs of neuronal spikes in our previous work, which eliminates the needs of computationally expensive covariance analysis and eigenvalue decomposition in conventional PCA algorithms. However, large memory resources are still inherently required for storing a large volume of aligned spikes for training PCs. The large size memory will consume large hardware resources and contribute significant power dissipation, which make GHA difficult to be implemented in portable or implantable multi-channel recording micro-systems. Method In this paper, we present a new algorithm for PCA-based spike sorting based on GHA, namely stream-based Hebbian eigenfilter, which eliminates the inherent memory requirements of GHA while keeping the accuracy of spike sorting by utilizing the pseudo-stationarity of neuronal spikes. Because of the reduction of large hardware storage requirements, the proposed algorithm can lead to ultra-low hardware resources and power consumption of hardware implementations, which is critical for the future multi-channel micro-systems. Both clinical and synthetic neural recording data sets were employed for evaluating the accuracy of the stream-based Hebbian eigenfilter. The performance of spike sorting using stream-based eigenfilter and the computational complexity of the eigenfilter were rigorously evaluated and compared with conventional PCA algorithms. Field programmable logic arrays (FPGAs) were employed to implement the proposed algorithm, evaluate the hardware implementations and demonstrate the reduction in both power consumption and hardware memories achieved by the streaming computing Results and discussion Results demonstrate that the stream-based eigenfilter can achieve the same accuracy and is 10 times more computationally efficient when compared with conventional PCA algorithms. Hardware evaluations show that 90.3% logic resources, 95.1% power consumption and 86.8% computing latency can be reduced by the stream-based eigenfilter when compared with PCA hardware. By utilizing the streaming method, 92% memory resources and 67% power consumption can be saved when compared with the direct implementation of GHA. Conclusion Stream-based Hebbian eigenfilter presents a novel approach to enable real-time spike sorting with reduced computational complexity and hardware costs. This new design can be further utilized for multi-channel neuro-physiological experiments or chronic implants. PMID:22490725

A single-cell spiking model for the origin of grid-cell patterns

PubMed Central

Kempter, Richard

2017-01-01

Spatial cognition in mammals is thought to rely on the activity of grid cells in the entorhinal cortex, yet the fundamental principles underlying the origin of grid-cell firing are still debated. Grid-like patterns could emerge via Hebbian learning and neuronal adaptation, but current computational models remained too abstract to allow direct confrontation with experimental data. Here, we propose a single-cell spiking model that generates grid firing fields via spike-rate adaptation and spike-timing dependent plasticity. Through rigorous mathematical analysis applicable in the linear limit, we quantitatively predict the requirements for grid-pattern formation, and we establish a direct link to classical pattern-forming systems of the Turing type. Our study lays the groundwork for biophysically-realistic models of grid-cell activity. PMID:28968386

Criterion learning in rule-based categorization: Simulation of neural mechanism and new data

PubMed Central

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

2015-01-01

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. PMID:25682349

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

PubMed

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.

Feedforward Inhibition and Synaptic Scaling – Two Sides of the Same Coin?

PubMed Central

Lücke, Jörg

2012-01-01

Feedforward inhibition and synaptic scaling are important adaptive processes that control the total input a neuron can receive from its afferents. While often studied in isolation, the two have been reported to co-occur in various brain regions. The functional implications of their interactions remain unclear, however. Based on a probabilistic modeling approach, we show here that fast feedforward inhibition and synaptic scaling interact synergistically during unsupervised learning. In technical terms, we model the input to a neural circuit using a normalized mixture model with Poisson noise. We demonstrate analytically and numerically that, in the presence of lateral inhibition introducing competition between different neurons, Hebbian plasticity and synaptic scaling approximate the optimal maximum likelihood solutions for this model. Our results suggest that, beyond its conventional use as a mechanism to remove undesired pattern variations, input normalization can make typical neural interaction and learning rules optimal on the stimulus subspace defined through feedforward inhibition. Furthermore, learning within this subspace is more efficient in practice, as it helps avoid locally optimal solutions. Our results suggest a close connection between feedforward inhibition and synaptic scaling which may have important functional implications for general cortical processing. PMID:22457610

Change detection in Arctic satellite imagery using clustering of sparse approximations (CoSA) over learned feature dictionaries

NASA Astrophysics Data System (ADS)

Moody, Daniela I.; Wilson, Cathy J.; Rowland, Joel C.; Altmann, Garrett L.

2015-06-01

Advanced pattern recognition and computer vision algorithms are of great interest for landscape characterization, change detection, and change monitoring in satellite imagery, in support of global climate change science and modeling. We present results from an ongoing effort to extend neuroscience-inspired models for feature extraction to the environmental sciences, and we demonstrate our work using Worldview-2 multispectral satellite imagery. We use a Hebbian learning rule to derive multispectral, multiresolution dictionaries directly from regional satellite normalized band difference index data. These feature dictionaries are used to build sparse scene representations, from which we automatically generate land cover labels via our CoSA algorithm: Clustering of Sparse Approximations. These data adaptive feature dictionaries use joint spectral and spatial textural characteristics to help separate geologic, vegetative, and hydrologic features. Land cover labels are estimated in example Worldview-2 satellite images of Barrow, Alaska, taken at two different times, and are used to detect and discuss seasonal surface changes. Our results suggest that an approach that learns from both spectral and spatial features is promising for practical pattern recognition problems in high resolution satellite imagery.

Acute effects of aerobic exercise promote learning

PubMed Central

Perini, Renza; Bortoletto, Marta; Capogrosso, Michela; Fertonani, Anna; Miniussi, Carlo

2016-01-01

The benefits that physical exercise confers on cardiovascular health are well known, whereas the notion that physical exercise can also improve cognitive performance has only recently begun to be explored and has thus far yielded only controversial results. In the present study, we used a sample of young male subjects to test the effects that a single bout of aerobic exercise has on learning. Two tasks were run: the first was an orientation discrimination task involving the primary visual cortex, and the second was a simple thumb abduction motor task that relies on the primary motor cortex. Forty-four and forty volunteers participated in the first and second experiments, respectively. We found that a single bout of aerobic exercise can significantly facilitate learning mechanisms within visual and motor domains and that these positive effects can persist for at least 30 minutes following exercise. This finding suggests that physical activity, at least of moderate intensity, might promote brain plasticity. By combining physical activity–induced plasticity with specific cognitive training–induced plasticity, we favour a gradual up-regulation of a functional network due to a steady increase in synaptic strength, promoting associative Hebbian-like plasticity. PMID:27146330

Implementation study of an analog spiking neural network for assisting cardiac delay prediction in a cardiac resynchronization therapy device.

PubMed

Sun, Qing; Schwartz, François; Michel, Jacques; Herve, Yannick; Dalmolin, Renzo

2011-06-01

In this paper, we aim at developing an analog spiking neural network (SNN) for reinforcing the performance of conventional cardiac resynchronization therapy (CRT) devices (also called biventricular pacemakers). Targeting an alternative analog solution in 0.13- μm CMOS technology, this paper proposes an approach to improve cardiac delay predictions in every cardiac period in order to assist the CRT device to provide real-time optimal heartbeats. The primary analog SNN architecture is proposed and its implementation is studied to fulfill the requirement of very low energy consumption. By using the Hebbian learning and reinforcement learning algorithms, the intended adaptive CRT device works with different functional modes. The simulations of both learning algorithms have been carried out, and they were shown to demonstrate the global functionalities. To improve the realism of the system, we introduce various heart behavior models (with constant/variable heart rates) that allow pathologic simulations with/without noise on the signals of the input sensors. The simulations of the global system (pacemaker models coupled with heart models) have been investigated and used to validate the analog spiking neural network implementation.

Feedforward inhibition and synaptic scaling--two sides of the same coin?

PubMed

Keck, Christian; Savin, Cristina; Lücke, Jörg

2012-01-01

Feedforward inhibition and synaptic scaling are important adaptive processes that control the total input a neuron can receive from its afferents. While often studied in isolation, the two have been reported to co-occur in various brain regions. The functional implications of their interactions remain unclear, however. Based on a probabilistic modeling approach, we show here that fast feedforward inhibition and synaptic scaling interact synergistically during unsupervised learning. In technical terms, we model the input to a neural circuit using a normalized mixture model with Poisson noise. We demonstrate analytically and numerically that, in the presence of lateral inhibition introducing competition between different neurons, Hebbian plasticity and synaptic scaling approximate the optimal maximum likelihood solutions for this model. Our results suggest that, beyond its conventional use as a mechanism to remove undesired pattern variations, input normalization can make typical neural interaction and learning rules optimal on the stimulus subspace defined through feedforward inhibition. Furthermore, learning within this subspace is more efficient in practice, as it helps avoid locally optimal solutions. Our results suggest a close connection between feedforward inhibition and synaptic scaling which may have important functional implications for general cortical processing.

View-Tolerant Face Recognition and Hebbian Learning Imply Mirror-Symmetric Neural Tuning to Head Orientation.

PubMed

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

2017-01-09

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 [3-6]. 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. Copyright © 2017 Elsevier Ltd. All rights reserved.

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

PubMed

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

2012-10-01

Classic approaches to word learning emphasize referential ambiguity: In naming situations, a novel word could refer to many possible objects, properties, actions, and so forth. To solve this, researchers have posited constraints, and inference strategies, but assume that determining the referent of a novel word is isomorphic to learning. We present an alternative in which referent selection is an online process and independent of long-term learning. We illustrate this theoretical approach with a dynamic associative model in which referent selection emerges from real-time competition between referents and learning is associative (Hebbian). This model accounts for a range of findings including the differences in expressive and receptive vocabulary, cross-situational learning under high degrees of ambiguity, accelerating (vocabulary explosion) and decelerating (power law) learning, fast mapping by mutual exclusivity (and differences in bilinguals), improvements in familiar word recognition with development, and correlations between speed of processing and learning. Together it suggests that (a) association learning buttressed by dynamic competition can account for much of the literature; (b) familiar word recognition is subserved by the same processes that identify the referents of novel words (fast mapping); (c) online competition may allow the children to leverage information available in the task to augment performance despite slow learning; (d) in complex systems, associative learning is highly multifaceted; and (e) learning and referent selection, though logically distinct, can be subtly related. It suggests more sophisticated ways of describing the interaction between situation- and developmental-time processes and points to the need for considering such interactions as a primary determinant of development. PsycINFO Database Record (c) 2012 APA, all rights reserved.

Rapid motor learning in the translational vestibulo-ocular reflex

NASA Technical Reports Server (NTRS)

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 <1 min of accumulated stimulation. These learned changes were consolidated over >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.

Hebbian Wiring Plasticity Generates Efficient Network Structures for Robust Inference with Synaptic Weight Plasticity

PubMed Central

Hiratani, Naoki; Fukai, Tomoki

2016-01-01

In the adult mammalian cortex, a small fraction of spines are created and eliminated every day, and the resultant synaptic connection structure is highly nonrandom, even in local circuits. However, it remains unknown whether a particular synaptic connection structure is functionally advantageous in local circuits, and why creation and elimination of synaptic connections is necessary in addition to rich synaptic weight plasticity. To answer these questions, we studied an inference task model through theoretical and numerical analyses. We demonstrate that a robustly beneficial network structure naturally emerges by combining Hebbian-type synaptic weight plasticity and wiring plasticity. Especially in a sparsely connected network, wiring plasticity achieves reliable computation by enabling efficient information transmission. Furthermore, the proposed rule reproduces experimental observed correlation between spine dynamics and task performance. PMID:27303271

FPGA Implementation of Generalized Hebbian Algorithm for Texture Classification

PubMed Central

Lin, Shiow-Jyu; Hwang, Wen-Jyi; Lee, Wei-Hao

2012-01-01

This paper presents a novel hardware architecture for principal component analysis. The architecture is based on the Generalized Hebbian Algorithm (GHA) because of its simplicity and effectiveness. The architecture is separated into three portions: the weight vector updating unit, the principal computation unit and the memory unit. In the weight vector updating unit, the computation of different synaptic weight vectors shares the same circuit for reducing the area costs. To show the effectiveness of the circuit, a texture classification system based on the proposed architecture is physically implemented by Field Programmable Gate Array (FPGA). It is embedded in a System-On-Programmable-Chip (SOPC) platform for performance measurement. Experimental results show that the proposed architecture is an efficient design for attaining both high speed performance and low area costs. PMID:22778640

Two symmetry-breaking mechanisms for the development of orientation selectivity in a neural system

NASA Astrophysics Data System (ADS)

Cho, Myoung Won; Chun, Min Young

2015-11-01

Orientation selectivity is a remarkable feature of the neurons located in the primary visual cortex. Provided that the visual neurons acquire orientation selectivity through activity-dependent Hebbian learning, the development process could be understood as a kind of symmetry-breaking phenomenon in the view of physics. This paper examines the key mechanisms of the orientation selectivity development process. Be found that at least two different mechanisms, which lead to the development of orientation selectivity by breaking the radial symmetry in receptive fields. The first is a simultaneous symmetry-breaking mechanism occurring based on the competition between neighboring neurons, and the second is a spontaneous one occurring based on the nonlinearity in interactions. Only the second mechanism leads to the formation of a columnar pattern whose characteristics is in accord with those observed in an animal experiment.

Wains: a pattern-seeking artificial life species.

PubMed

de Buitléir, Amy; Russell, Michael; Daly, Mark

2012-01-01

We describe the initial phase of a research project to develop an artificial life framework designed to extract knowledge from large data sets with minimal preparation or ramp-up time. In this phase, we evolved an artificial life population with a new brain architecture. The agents have sufficient intelligence to discover patterns in data and to make survival decisions based on those patterns. The species uses diploid reproduction, Hebbian learning, and Kohonen self-organizing maps, in combination with novel techniques such as using pattern-rich data as the environment and framing the data analysis as a survival problem for artificial life. The first generation of agents mastered the pattern discovery task well enough to thrive. Evolution further adapted the agents to their environment by making them a little more pessimistic, and also by making their brains more efficient.

Information flow in layered networks of non-monotonic units

NASA Astrophysics Data System (ADS)

Schittler Neves, Fabio; Martim Schubert, Benno; Erichsen, Rubem, Jr.

2015-07-01

Layered neural networks are feedforward structures that yield robust parallel and distributed pattern recognition. Even though much attention has been paid to pattern retrieval properties in such systems, many aspects of their dynamics are not yet well characterized or understood. In this work we study, at different temperatures, the memory activity and information flows through layered networks in which the elements are the simplest binary odd non-monotonic function. Our results show that, considering a standard Hebbian learning approach, the network information content has its maximum always at the monotonic limit, even though the maximum memory capacity can be found at non-monotonic values for small enough temperatures. Furthermore, we show that such systems exhibit rich macroscopic dynamics, including not only fixed point solutions of its iterative map, but also cyclic and chaotic attractors that also carry information.

A computational developmental model for specificity and transfer in perceptual learning.

PubMed

Solgi, Mojtaba; Liu, Taosheng; Weng, Juyang

2013-01-04

How and under what circumstances the training effects of perceptual learning (PL) transfer to novel situations is critical to our understanding of generalization and abstraction in learning. Although PL is generally believed to be highly specific to the trained stimulus, a series of psychophysical studies have recently shown that training effects can transfer to untrained conditions under certain experimental protocols. In this article, we present a brain-inspired, neuromorphic computational model of the Where-What visuomotor pathways which successfully explains both the specificity and transfer of perceptual learning. The major architectural novelty is that each feature neuron has both sensory and motor inputs. The network of neurons is autonomously developed from experience, using a refined Hebbian-learning rule and lateral competition, which altogether result in neuronal recruitment. Our hypothesis is that certain paradigms of experiments trigger two-way (descending and ascending) off-task processes about the untrained condition which lead to recruitment of more neurons in lower feature representation areas as well as higher concept representation areas for the untrained condition, hence the transfer. We put forward a novel proposition that gated self-organization of the connections during the off-task processes accounts for the observed transfer effects. Simulation results showed transfer of learning across retinal locations in a Vernier discrimination task in a double-training procedure, comparable to previous psychophysical data (Xiao et al., 2008). To the best of our knowledge, this model is the first neurally-plausible model to explain both transfer and specificity in a PL setting.

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

PubMed

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.

Unsupervised learning of contextual constraints in neural networks for simultaneous visual processing of multiple objects

NASA Astrophysics Data System (ADS)

Marshall, Jonathan A.

1992-12-01

A simple self-organizing neural network model, called an EXIN network, that learns to process sensory information in a context-sensitive manner, is described. EXIN networks develop efficient representation structures for higher-level visual tasks such as segmentation, grouping, transparency, depth perception, and size perception. Exposure to a perceptual environment during a developmental period serves to configure the network to perform appropriate organization of sensory data. A new anti-Hebbian inhibitory learning rule permits superposition of multiple simultaneous neural activations (multiple winners), while maintaining contextual consistency constraints, instead of forcing winner-take-all pattern classifications. The activations can represent multiple patterns simultaneously and can represent uncertainty. The network performs parallel parsing, credit attribution, and simultaneous constraint satisfaction. EXIN networks can learn to represent multiple oriented edges even where they intersect and can learn to represent multiple transparently overlaid surfaces defined by stereo or motion cues. In the case of stereo transparency, the inhibitory learning implements both a uniqueness constraint and permits coactivation of cells representing multiple disparities at the same image location. Thus two or more disparities can be active simultaneously without interference. This behavior is analogous to that of Prazdny's stereo vision algorithm, with the bonus that each binocular point is assigned a unique disparity. In a large implementation, such a NN would also be able to represent effectively the disparities of a cloud of points at random depths, like human observers, and unlike Prazdny's method

LTP saturation and spatial learning disruption: effects of task variables and saturation levels.

PubMed

Barnes, C A; Jung, M W; McNaughton, B L; Korol, D L; Andreasson, K; Worley, P F

1994-10-01

The prediction that "saturation" of LTP/LTE at hippocampal synapses should impair spatial learning was reinvestigated in the light of a more specific consideration of the theory of Hebbian associative networks, which predicts a nonlinear relationship between LTP "saturation" and memory impairment. This nonlinearity may explain the variable results of studies that have addressed the effects of LTP "saturation" on behavior. The extent of LTP "saturation" in fascia dentata produced by the standard chronic LTP stimulation protocol was assessed both electrophysiologically and through the use of an anatomical marker (activation of the immediate-early gene zif268). Both methods point to the conclusion that the standard protocols used to induce LTP do not "saturate" the process at any dorsoventral level, and leave the ventral half of the hippocampus virtually unaffected. LTP-inducing, bilateral perforant path stimulation led to a significant deficit in the reversal of a well-learned spatial response on the Barnes circular platform task as reported previously, yet in the same animals produced no deficit in learning the Morris water task (for which previous results have been conflicting). The behavioral deficit was not a consequence of any after-discharge in the hippocampal EEG. In contrast, administration of maximal electroconvulsive shock led to robust zif268 activation throughout the hippocampus, enhancement of synaptic responses, occlusion of LTP produced by discrete high-frequency stimulation, and spatial learning deficits in the water task. These data provide further support for the involvement of LTP-like synaptic enhancement in spatial learning.

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

PubMed

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.

Learning tinnitus

NASA Astrophysics Data System (ADS)

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.

A Mismatch-Based Model for Memory Reconsolidation and Extinction in Attractor Networks

PubMed Central

Amaral, Olavo B.

2011-01-01

The processes of memory reconsolidation and extinction have received increasing attention in recent experimental research, as their potential clinical applications begin to be uncovered. A number of studies suggest that amnestic drugs injected after reexposure to a learning context can disrupt either of the two processes, depending on the behavioral protocol employed. Hypothesizing that reconsolidation represents updating of a memory trace in the hippocampus, while extinction represents formation of a new trace, we have built a neural network model in which either simple retrieval, reconsolidation or extinction of a stored attractor can occur upon contextual reexposure, depending on the similarity between the representations of the original learning and reexposure sessions. This is achieved by assuming that independent mechanisms mediate Hebbian-like synaptic strengthening and mismatch-driven labilization of synaptic changes, with protein synthesis inhibition preferentially affecting the former. Our framework provides a unified mechanistic explanation for experimental data showing (a) the effect of reexposure duration on the occurrence of reconsolidation or extinction and (b) the requirement of memory updating during reexposure to drive reconsolidation. PMID:21826231

Learning Pitch with STDP: A Computational Model of Place and Temporal Pitch Perception Using Spiking Neural Networks.

PubMed

Erfanian Saeedi, Nafise; Blamey, Peter J; Burkitt, Anthony N; Grayden, David B

2016-04-01

Pitch perception is important for understanding speech prosody, music perception, recognizing tones in tonal languages, and perceiving speech in noisy environments. The two principal pitch perception theories consider the place of maximum neural excitation along the auditory nerve and the temporal pattern of the auditory neurons' action potentials (spikes) as pitch cues. This paper describes a biophysical mechanism by which fine-structure temporal information can be extracted from the spikes generated at the auditory periphery. Deriving meaningful pitch-related information from spike times requires neural structures specialized in capturing synchronous or correlated activity from amongst neural events. The emergence of such pitch-processing neural mechanisms is described through a computational model of auditory processing. Simulation results show that a correlation-based, unsupervised, spike-based form of Hebbian learning can explain the development of neural structures required for recognizing the pitch of simple and complex tones, with or without the fundamental frequency. The temporal code is robust to variations in the spectral shape of the signal and thus can explain the phenomenon of pitch constancy.

Learning Pitch with STDP: A Computational Model of Place and Temporal Pitch Perception Using Spiking Neural Networks

PubMed Central

Erfanian Saeedi, Nafise; Blamey, Peter J.; Burkitt, Anthony N.; Grayden, David B.

2016-01-01

Pitch perception is important for understanding speech prosody, music perception, recognizing tones in tonal languages, and perceiving speech in noisy environments. The two principal pitch perception theories consider the place of maximum neural excitation along the auditory nerve and the temporal pattern of the auditory neurons’ action potentials (spikes) as pitch cues. This paper describes a biophysical mechanism by which fine-structure temporal information can be extracted from the spikes generated at the auditory periphery. Deriving meaningful pitch-related information from spike times requires neural structures specialized in capturing synchronous or correlated activity from amongst neural events. The emergence of such pitch-processing neural mechanisms is described through a computational model of auditory processing. Simulation results show that a correlation-based, unsupervised, spike-based form of Hebbian learning can explain the development of neural structures required for recognizing the pitch of simple and complex tones, with or without the fundamental frequency. The temporal code is robust to variations in the spectral shape of the signal and thus can explain the phenomenon of pitch constancy. PMID:27049657

Changes in muscle coordination with training.

PubMed

Carson, Richard G

2006-11-01

Three core concepts, activity-dependent coupling, the composition of muscle synergies, and Hebbian adaptation, are discussed with a view to illustrating the nature of the constraints imposed by the organization of the central nervous system on the changes in muscle coordination induced by training. It is argued that training invoked variations in the efficiency with which motor actions can be generated influence the stability of coordination by altering the potential for activity-dependent coupling between the cortical representations of the focal muscles recruited in a movement task and brain circuits that do not contribute directly to the required behavior. The behaviors that can be generated during training are also constrained by the composition of existing intrinsic muscle synergies. In circumstances in which attempts to produce forceful or high velocity movements would otherwise result in the generation of inappropriate actions, training designed to promote the development of control strategies specific to the desired movement outcome may be necessary to compensate for protogenic muscle recruitment patterns. Hebbian adaptation refers to processes whereby, for neurons that release action potentials at the same time, there is an increased probability that synaptic connections will be formed. Neural connectivity induced by the repetition of specific muscle recruitment patterns during training may, however, inhibit the subsequent acquisition of new skills. Consideration is given to the possibility that, in the presence of the appropriate sensory guidance, it is possible to gate Hebbian plasticity and to promote greater subsequent flexibility in the recruitment of the trained muscles in other task contexts.

Combinations of stroke neurorehabilitation to facilitate motor recovery: perspectives on Hebbian plasticity and homeostatic metaplasticity

PubMed Central

Takeuchi, Naoyuki; Izumi, Shin-Ichi

2015-01-01

Motor recovery after stroke involves developing new neural connections, acquiring new functions, and compensating for impairments. These processes are related to neural plasticity. Various novel stroke rehabilitation techniques based on basic science and clinical studies of neural plasticity have been developed to aid motor recovery. Current research aims to determine whether using combinations of these techniques can synergistically improve motor recovery. When different stroke neurorehabilitation therapies are combined, the timing of each therapeutic program must be considered to enable optimal neural plasticity. Synchronizing stroke rehabilitation with voluntary neural and/or muscle activity can lead to motor recovery by targeting Hebbian plasticity. This reinforces the neural connections between paretic muscles and the residual motor area. Homeostatic metaplasticity, which stabilizes the activity of neurons and neural circuits, can either augment or reduce the synergic effect depending on the timing of combination therapy and types of neurorehabilitation that are used. Moreover, the possibility that the threshold and degree of induced plasticity can be altered after stroke should be noted. This review focuses on the mechanisms underlying combinations of neurorehabilitation approaches and their future clinical applications. We suggest therapeutic approaches for cortical reorganization and maximal functional gain in patients with stroke, based on the processes of Hebbian plasticity and homeostatic metaplasticity. Few of the possible combinations of stroke neurorehabilitation have been tested experimentally; therefore, further studies are required to determine the appropriate combination for motor recovery. PMID:26157374

Brain connections of words, perceptions and actions: A neurobiological model of spatio-temporal semantic activation in the human cortex.

PubMed

Tomasello, Rosario; Garagnani, Max; Wennekers, Thomas; Pulvermüller, Friedemann

2017-04-01

Neuroimaging and patient studies show that different areas of cortex respectively specialize for general and selective, or category-specific, semantic processing. Why are there both semantic hubs and category-specificity, and how come that they emerge in different cortical regions? Can the activation time-course of these areas be predicted and explained by brain-like network models? In this present work, we extend a neurocomputational model of human cortical function to simulate the time-course of cortical processes of understanding meaningful concrete words. The model implements frontal and temporal cortical areas for language, perception, and action along with their connectivity. It uses Hebbian learning to semantically ground words in aspects of their referential object- and action-related meaning. Compared with earlier proposals, the present model incorporates additional neuroanatomical links supported by connectivity studies and downscaled synaptic weights in order to control for functional between-area differences purely due to the number of in- or output links of an area. We show that learning of semantic relationships between words and the objects and actions these symbols are used to speak about, leads to the formation of distributed circuits, which all include neuronal material in connector hub areas bridging between sensory and motor cortical systems. Therefore, these connector hub areas acquire a role as semantic hubs. By differentially reaching into motor or visual areas, the cortical distributions of the emergent 'semantic circuits' reflect aspects of the represented symbols' meaning, thus explaining category-specificity. The improved connectivity structure of our model entails a degree of category-specificity even in the 'semantic hubs' of the model. The relative time-course of activation of these areas is typically fast and near-simultaneous, with semantic hubs central to the network structure activating before modality-preferential areas carrying semantic information. Copyright © 2017 The Authors. Published by Elsevier Ltd.. All rights reserved.

The Demise of the Synapse As the Locus of Memory: A Looming Paradigm Shift?

PubMed

Trettenbrein, Patrick C

2016-01-01

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 recognized. 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.

The globus pallidus pars interna in goal-oriented and routine behaviors: Resolving a long-standing paradox.

PubMed

Piron, Camille; Kase, Daisuke; Topalidou, Meropi; Goillandeau, Michel; Orignac, Hugues; N'Guyen, Tho-Haï; Rougier, Nicolas; Boraud, Thomas

2016-08-01

There is an apparent contradiction between experimental data showing that the basal ganglia are involved in goal-oriented and routine behaviors and clinical observations. Lesion or disruption by deep brain stimulation of the globus pallidus interna has been used for various therapeutic purposes ranging from the improvement of dystonia to the treatment of Tourette's syndrome. None of these approaches has reported any severe impairment in goal-oriented or automatic movement. To solve this conundrum, we trained 2 monkeys to perform a variant of a 2-armed bandit-task (with different reward contingencies). In the latter we alternated blocks of trials with choices between familiar rewarded targets that elicit routine behavior and blocks with novel pairs of targets that require an intentional learning process. Bilateral inactivation of the globus pallidus interna, by injection of muscimol, prevents animals from learning new contingencies while performance remains intact, although slower for the familiar stimuli. We replicate in silico these data by adding lateral competition and Hebbian learning in the cortical layer of the theoretical model of the cortex-basal ganglia loop that provided the framework of our experimental approach. The basal ganglia play a critical role in the deliberative process that underlies learning but are not necessary for the expression of routine movements. Our approach predicts that after pallidotomy or during stimulation, patients should have difficulty with complex decision-making processes or learning new goal-oriented behaviors. © 2016 Movement Disorder Society. © 2016 International Parkinson and Movement Disorder Society.

On the capacity of ternary Hebbian networks

NASA Technical Reports Server (NTRS)

Baram, Yoram

1991-01-01

Networks of ternary neurons storing random vectors over the set -1,0,1 by the so-called Hebbian rule are considered. It is shown that the maximal number of stored patterns that are equilibrium states of the network with probability tending to one as N tends to infinity is at least on the order of (N exp 2-1/alpha)/K, where N is the number of neurons, K is the number of nonzero elements in a pattern, and t = alpha x K, alpha between 1/2 and 1, is the threshold in the neuron function. While, for small K, this bound is similar to that obtained for fully connected binary networks, the number of interneural connections required in the ternary case is considerably smaller. Similar bounds, incorporating error probabilities, are shown to guarantee, in the same probabilistic sense, the correction of errors in the nonzero elements and in the location of these elements.

Sensory memory for odors is encoded in spontaneous correlated activity between olfactory glomeruli.

PubMed

Galán, Roberto F; Weidert, Marcel; Menzel, Randolf; Herz, Andreas V M; Galizia, C Giovanni

2006-01-01

Sensory memory is a short-lived persistence of a sensory stimulus in the nervous system, such as iconic memory in the visual system. However, little is known about the mechanisms underlying olfactory sensory memory. We have therefore analyzed the effect of odor stimuli on the first odor-processing network in the honeybee brain, the antennal lobe, which corresponds to the vertebrate olfactory bulb. We stained output neurons with a calcium-sensitive dye and measured across-glomerular patterns of spontaneous activity before and after a stimulus. Such a single-odor presentation changed the relative timing of spontaneous activity across glomeruli in accordance with Hebb's theory of learning. Moreover, during the first few minutes after odor presentation, correlations between the spontaneous activity fluctuations suffice to reconstruct the stimulus. As spontaneous activity is ubiquitous in the brain, modifiable fluctuations could provide an ideal substrate for Hebbian reverberations and sensory memory in other neural systems.

Sensorimotor Learning Biases Choice Behavior: A Learning Neural Field Model for Decision Making

PubMed Central

Schöner, Gregor; Gail, Alexander

2012-01-01

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 selection required for decision making in ambiguous choice situations. PMID:23166483

NMDA Receptors Mediate Olfactory Learning and Memory in Drosophila

PubMed Central

Xia, Shouzhen; Miyashita, Tomoyuki; Fu, Tsai-Feng; Lin, Wei-Yong; Wu, Chia-Lin; Pyzocha, Lori; Lin, Inn-Ray; Saitoe, Minoru; Tully, Tim; Chiang, Ann-Shyn

2011-01-01

Summary Background Molecular and electrophysiological properties of NMDARs suggest that they may be the Hebbian “coincidence detectors” hypothesized to underlie associative learning. Because of the nonspecificity of drugs that modulate NMDAR function or the relatively chronic genetic manipulations of various NMDAR subunits from mammalian studies, conclusive evidence for such an acute role for NMDARs in adult behavioral plasticity, however, is lacking. Moreover, a role for NMDARs in memory consolidation remains controversial. Results The Drosophila genome encodes two NMDAR homologs, dNR1 and dNR2. When coexpressed in Xenopus oocytes or Drosophila S2 cells, dNR1 and dNR2 form functional NMDARs with several of the distinguishing molecular properties observed for vertebrate NMDARs, including voltage/Mg2+-dependent activation by glutamate. Both proteins are weakly expressed throughout the entire brain but show preferential expression in several neurons surrounding the dendritic region of the mushroom bodies. Hypomorphic mutations of the essential dNR1 gene disrupt olfactory learning, and this learning defect is rescued with wild-type transgenes. Importantly, we show that Pavlovian learning is disrupted in adults within 15 hr after transient induction of a dNR1 antisense RNA transgene. Extended training is sufficient to overcome this initial learning defect, but long-term memory (LTM) specifically is abolished under these training conditions. Conclusions Our study uses a combination of molecular-genetic tools to (1) generate genomic mutations of the dNR1 gene, (2) rescue the accompanying learning deficit with a dNR1+ transgene, and (3) rapidly and transiently knockdown dNR1+ expression in adults, thereby demonstrating an evolutionarily conserved role for the acute involvement of NMDARs in associative learning and memory. PMID:15823532

NMDA receptors mediate olfactory learning and memory in Drosophila.

PubMed

Xia, Shouzhen; Miyashita, Tomoyuki; Fu, Tsai-Feng; Lin, Wei-Yong; Wu, Chia-Lin; Pyzocha, Lori; Lin, Inn-Ray; Saitoe, Minoru; Tully, Tim; Chiang, Ann-Shyn

2005-04-12

Molecular and electrophysiological properties of NMDARs suggest that they may be the Hebbian "coincidence detectors" hypothesized to underlie associative learning. Because of the nonspecificity of drugs that modulate NMDAR function or the relatively chronic genetic manipulations of various NMDAR subunits from mammalian studies, conclusive evidence for such an acute role for NMDARs in adult behavioral plasticity, however, is lacking. Moreover, a role for NMDARs in memory consolidation remains controversial. The Drosophila genome encodes two NMDAR homologs, dNR1 and dNR2. When coexpressed in Xenopus oocytes or Drosophila S2 cells, dNR1 and dNR2 form functional NMDARs with several of the distinguishing molecular properties observed for vertebrate NMDARs, including voltage/Mg(2+)-dependent activation by glutamate. Both proteins are weakly expressed throughout the entire brain but show preferential expression in several neurons surrounding the dendritic region of the mushroom bodies. Hypomorphic mutations of the essential dNR1 gene disrupt olfactory learning, and this learning defect is rescued with wild-type transgenes. Importantly, we show that Pavlovian learning is disrupted in adults within 15 hr after transient induction of a dNR1 antisense RNA transgene. Extended training is sufficient to overcome this initial learning defect, but long-term memory (LTM) specifically is abolished under these training conditions. Our study uses a combination of molecular-genetic tools to (1) generate genomic mutations of the dNR1 gene, (2) rescue the accompanying learning deficit with a dNR1+ transgene, and (3) rapidly and transiently knockdown dNR1+ expression in adults, thereby demonstrating an evolutionarily conserved role for the acute involvement of NMDARs in associative learning and memory.

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

PubMed Central

Hawkins, Jeff; Ahmad, Subutai; Cui, Yuwei

2017-01-01

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. PMID:29118696

Plasticity of cortical inhibition in dystonia is impaired after motor learning and Paired-Associative Stimulation

PubMed Central

Meunier, Sabine; Russmann, Heike; Shamim, Ejaz; Lamy, Jean-Charles; Hallett, Mark

2012-01-01

Summary Artificial induction of plasticity by paired associative stimulation (PAS) in healthy subjects (HV) demonstrates Hebbian-like plasticity in selected inhibitory networks as well as excitatory ones. In a group of 17 patients with focal hand dystonia and a group of 19 HV, we evaluated how PAS and the learning of a simple motor task influence the circuits supporting long interval intracortical inhibition (LICI, reflecting activity of GABAB interneurons) and long latency afferent inhibition (LAI, reflecting activity of somatosensory inputs to the motor cortex). In HV, PAS and motor learning induced LTP-like plasticity of excitatory networks and a lasting decrease of LAI and LICI in the motor representation of the targeted or trained muscle. The better the motor performance, the larger was the decrease of LAI. Although motor performance in the patient group was similar to that of the control group, LAI did not decrease during the motor learning as it did in the control group. In contrast, LICI was normally modulated. In patients the results after PAS did not match those obtained after motor learning: LAI was paradoxically increased and LICI did not exhibit any change. In the normal situation, decreased excitability in inhibitory circuits after induction of LTP-like plasticity may help to shape the cortical maps according to the new sensorimotor task. In patients, the abnormal or absent modulation of afferent and intracortical long-interval inhibition might indicate maladaptive plasticity that possibly contributes to the difficulty that they have to learn a new sensorimotor task.“ PMID:22429246

Inter-synaptic learning of combination rules in a cortical network model

PubMed Central

Lavigne, Frédéric; Avnaïm, Francis; Dumercy, Laurent

2014-01-01

Selecting responses in working memory while processing combinations of stimuli depends strongly on their relations stored in long-term memory. However, the learning of XOR-like combinations of stimuli and responses according to complex rules raises the issue of the non-linear separability of the responses within the space of stimuli. One proposed solution is to add neurons that perform a stage of non-linear processing between the stimuli and responses, at the cost of increasing the network size. Based on the non-linear integration of synaptic inputs within dendritic compartments, we propose here an inter-synaptic (IS) learning algorithm that determines the probability of potentiating/depressing each synapse as a function of the co-activity of the other synapses within the same dendrite. The IS learning is effective with random connectivity and without either a priori wiring or additional neurons. Our results show that IS learning generates efficacy values that are sufficient for the processing of XOR-like combinations, on the basis of the sole correlational structure of the stimuli and responses. We analyze the types of dendrites involved in terms of the number of synapses from pre-synaptic neurons coding for the stimuli and responses. The synaptic efficacy values obtained show that different dendrites specialize in the detection of different combinations of stimuli. The resulting behavior of the cortical network model is analyzed as a function of inter-synaptic vs. Hebbian learning. Combinatorial priming effects show that the retrospective activity of neurons coding for the stimuli trigger XOR-like combination-selective prospective activity of neurons coding for the expected response. The synergistic effects of inter-synaptic learning and of mixed-coding neurons are simulated. The results show that, although each mechanism is sufficient by itself, their combined effects improve the performance of the network. PMID:25221529

A theory of local learning, the learning channel, and the optimality of backpropagation.

PubMed

Baldi, Pierre; Sadowski, Peter

2016-11-01

In a physical neural system, where storage and processing are intimately intertwined, the rules for adjusting the synaptic weights can only depend on variables that are available locally, such as the activity of the pre- and post-synaptic neurons, resulting in local learning rules. A systematic framework for studying the space of local learning rules is obtained by first specifying the nature of the local variables, and then the functional form that ties them together into each learning rule. Such a framework enables also the systematic discovery of new learning rules and exploration of relationships between learning rules and group symmetries. We study polynomial local learning rules stratified by their degree and analyze their behavior and capabilities in both linear and non-linear units and networks. Stacking local learning rules in deep feedforward networks leads to deep local learning. While deep local learning can learn interesting representations, it cannot learn complex input-output functions, even when targets are available for the top layer. Learning complex input-output functions requires local deep learning where target information is communicated to the deep layers through a backward learning channel. The nature of the communicated information about the targets and the structure of the learning channel partition the space of learning algorithms. For any learning algorithm, the capacity of the learning channel can be defined as the number of bits provided about the error gradient per weight, divided by the number of required operations per weight. We estimate the capacity associated with several learning algorithms and show that backpropagation outperforms them by simultaneously maximizing the information rate and minimizing the computational cost. This result is also shown to be true for recurrent networks, by unfolding them in time. The theory clarifies the concept of Hebbian learning, establishes the power and limitations of local learning rules, introduces the learning channel which enables a formal analysis of the optimality of backpropagation, and explains the sparsity of the space of learning rules discovered so far. Copyright © 2016 Elsevier Ltd. All rights reserved.

Recurrent-neural-network-based Boolean factor analysis and its application to word clustering.

PubMed

Frolov, Alexander A; Husek, Dusan; Polyakov, Pavel Yu

2009-07-01

The objective of this paper is to introduce a neural-network-based algorithm for word clustering as an extension of the neural-network-based Boolean factor analysis algorithm (Frolov , 2007). It is shown that this extended algorithm supports even the more complex model of signals that are supposed to be related to textual documents. It is hypothesized that every topic in textual data is characterized by a set of words which coherently appear in documents dedicated to a given topic. The appearance of each word in a document is coded by the activity of a particular neuron. In accordance with the Hebbian learning rule implemented in the network, sets of coherently appearing words (treated as factors) create tightly connected groups of neurons, hence, revealing them as attractors of the network dynamics. The found factors are eliminated from the network memory by the Hebbian unlearning rule facilitating the search of other factors. Topics related to the found sets of words can be identified based on the words' semantics. To make the method complete, a special technique based on a Bayesian procedure has been developed for the following purposes: first, to provide a complete description of factors in terms of component probability, and second, to enhance the accuracy of classification of signals to determine whether it contains the factor. Since it is assumed that every word may possibly contribute to several topics, the proposed method might be related to the method of fuzzy clustering. In this paper, we show that the results of Boolean factor analysis and fuzzy clustering are not contradictory, but complementary. To demonstrate the capabilities of this attempt, the method is applied to two types of textual data on neural networks in two different languages. The obtained topics and corresponding words are at a good level of agreement despite the fact that identical topics in Russian and English conferences contain different sets of keywords.

Excitation-neurogenesis coupling in adult neural stem/progenitor cells.

PubMed

Deisseroth, Karl; Singla, Sheela; Toda, Hiroki; Monje, Michelle; Palmer, Theo D; Malenka, Robert C

2004-05-27

A wide variety of in vivo manipulations influence neurogenesis in the adult hippocampus. It is not known, however, if adult neural stem/progenitor cells (NPCs) can intrinsically sense excitatory neural activity and thereby implement a direct coupling between excitation and neurogenesis. Moreover, the theoretical significance of activity-dependent neurogenesis in hippocampal-type memory processing networks has not been explored. Here we demonstrate that excitatory stimuli act directly on adult hippocampal NPCs to favor neuron production. The excitation is sensed via Ca(v)1.2/1.3 (L-type) Ca(2+) channels and NMDA receptors on the proliferating precursors. Excitation through this pathway acts to inhibit expression of the glial fate genes Hes1 and Id2 and increase expression of NeuroD, a positive regulator of neuronal differentiation. These activity-sensing properties of the adult NPCs, when applied as an "excitation-neurogenesis coupling rule" within a Hebbian neural network, predict significant advantages for both the temporary storage and the clearance of memories.

REVIEW: Internal models in sensorimotor integration: perspectives from adaptive control theory

NASA Astrophysics Data System (ADS)

Tin, Chung; Poon, Chi-Sang

2005-09-01

Internal models and adaptive controls are empirical and mathematical paradigms that have evolved separately to describe learning control processes in brain systems and engineering systems, respectively. This paper presents a comprehensive appraisal of the correlation between these paradigms with a view to forging a unified theoretical framework that may benefit both disciplines. It is suggested that the classic equilibrium-point theory of impedance control of arm movement is analogous to continuous gain-scheduling or high-gain adaptive control within or across movement trials, respectively, and that the recently proposed inverse internal model is akin to adaptive sliding control originally for robotic manipulator applications. Modular internal models' architecture for multiple motor tasks is a form of multi-model adaptive control. Stochastic methods, such as generalized predictive control, reinforcement learning, Bayesian learning and Hebbian feedback covariance learning, are reviewed and their possible relevance to motor control is discussed. Possible applicability of a Luenberger observer and an extended Kalman filter to state estimation problems—such as sensorimotor prediction or the resolution of vestibular sensory ambiguity—is also discussed. The important role played by vestibular system identification in postural control suggests an indirect adaptive control scheme whereby system states or parameters are explicitly estimated prior to the implementation of control. This interdisciplinary framework should facilitate the experimental elucidation of the mechanisms of internal models in sensorimotor systems and the reverse engineering of such neural mechanisms into novel brain-inspired adaptive control paradigms in future.

Neuromodulated Synaptic Plasticity on the SpiNNaker Neuromorphic System

PubMed Central

Mikaitis, Mantas; Pineda García, Garibaldi; Knight, James C.; Furber, Steve B.

2018-01-01

SpiNNaker is a digital neuromorphic architecture, designed specifically for the low power simulation of large-scale spiking neural networks at speeds close to biological real-time. Unlike other neuromorphic systems, SpiNNaker allows users to develop their own neuron and synapse models as well as specify arbitrary connectivity. As a result SpiNNaker has proved to be a powerful tool for studying different neuron models as well as synaptic plasticity—believed to be one of the main mechanisms behind learning and memory in the brain. A number of Spike-Timing-Dependent-Plasticity(STDP) rules have already been implemented on SpiNNaker and have been shown to be capable of solving various learning tasks in real-time. However, while STDP is an important biological theory of learning, it is a form of Hebbian or unsupervised learning and therefore does not explain behaviors that depend on feedback from the environment. Instead, learning rules based on neuromodulated STDP (three-factor learning rules) have been shown to be capable of solving reinforcement learning tasks in a biologically plausible manner. In this paper we demonstrate for the first time how a model of three-factor STDP, with the third-factor representing spikes from dopaminergic neurons, can be implemented on the SpiNNaker neuromorphic system. Using this learning rule we first show how reward and punishment signals can be delivered to a single synapse before going on to demonstrate it in a larger network which solves the credit assignment problem in a Pavlovian conditioning experiment. Because of its extra complexity, we find that our three-factor learning rule requires approximately 2× as much processing time as the existing SpiNNaker STDP learning rules. However, we show that it is still possible to run our Pavlovian conditioning model with up to 1 × 104 neurons in real-time, opening up new research opportunities for modeling behavioral learning on SpiNNaker. PMID:29535600

Contrast normalization contributes to a biologically-plausible model of receptive-field development in primary visual cortex (V1)

PubMed Central

Willmore, Ben D.B.; Bulstrode, Harry; Tolhurst, David J.

2012-01-01

Neuronal populations in the primary visual cortex (V1) of mammals exhibit contrast normalization. Neurons that respond strongly to simple visual stimuli – such as sinusoidal gratings – respond less well to the same stimuli when they are presented as part of a more complex stimulus which also excites other, neighboring neurons. This phenomenon is generally attributed to generalized patterns of inhibitory connections between nearby V1 neurons. The Bienenstock, Cooper and Munro (BCM) rule is a neural network learning rule that, when trained on natural images, produces model neurons which, individually, have many tuning properties in common with real V1 neurons. However, when viewed as a population, a BCM network is very different from V1 – each member of the BCM population tends to respond to the same dominant features of visual input, producing an incomplete, highly redundant code for visual information. Here, we demonstrate that, by adding contrast normalization into the BCM rule, we arrive at a neurally-plausible Hebbian learning rule that can learn an efficient sparse, overcomplete representation that is a better model for stimulus selectivity in V1. This suggests that one role of contrast normalization in V1 is to guide the neonatal development of receptive fields, so that neurons respond to different features of visual input. PMID:22230381

Proposed mechanism for learning and memory erasure in a white-noise-driven sleeping cortex.

PubMed

Steyn-Ross, Moira L; Steyn-Ross, D A; Sleigh, J W; Wilson, M T; Wilcocks, Lara C

2005-12-01

Understanding the structure and purpose of sleep remains one of the grand challenges of neurobiology. Here we use a mean-field linearized theory of the sleeping cortex to derive statistics for synaptic learning and memory erasure. The growth in correlated low-frequency high-amplitude voltage fluctuations during slow-wave sleep (SWS) is characterized by a probability density function that becomes broader and shallower as the transition into rapid-eye-movement (REM) sleep is approached. At transition, the Shannon information entropy of the fluctuations is maximized. If we assume Hebbian-learning rules apply to the cortex, then its correlated response to white-noise stimulation during SWS provides a natural mechanism for a synaptic weight change that will tend to shut down reverberant neural activity. In contrast, during REM sleep the weights will evolve in a direction that encourages excitatory activity. These entropy and weight-change predictions lead us to identify the final portion of deep SWS that occurs immediately prior to transition into REM sleep as a time of enhanced erasure of labile memory. We draw a link between the sleeping cortex and Landauer's dissipation theorem for irreversible computing [R. Landauer, IBM J. Res. Devel. 5, 183 (1961)], arguing that because information erasure is an irreversible computation, there is an inherent entropy cost as the cortex transits from SWS into REM sleep.

Unsupervised learning in neural networks with short range synapses

NASA Astrophysics Data System (ADS)

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.

Proposed mechanism for learning and memory erasure in a white-noise-driven sleeping cortex

NASA Astrophysics Data System (ADS)

Steyn-Ross, Moira L.; Steyn-Ross, D. A.; Sleigh, J. W.; Wilson, M. T.; Wilcocks, Lara C.

2005-12-01

Understanding the structure and purpose of sleep remains one of the grand challenges of neurobiology. Here we use a mean-field linearized theory of the sleeping cortex to derive statistics for synaptic learning and memory erasure. The growth in correlated low-frequency high-amplitude voltage fluctuations during slow-wave sleep (SWS) is characterized by a probability density function that becomes broader and shallower as the transition into rapid-eye-movement (REM) sleep is approached. At transition, the Shannon information entropy of the fluctuations is maximized. If we assume Hebbian-learning rules apply to the cortex, then its correlated response to white-noise stimulation during SWS provides a natural mechanism for a synaptic weight change that will tend to shut down reverberant neural activity. In contrast, during REM sleep the weights will evolve in a direction that encourages excitatory activity. These entropy and weight-change predictions lead us to identify the final portion of deep SWS that occurs immediately prior to transition into REM sleep as a time of enhanced erasure of labile memory. We draw a link between the sleeping cortex and Landauer’s dissipation theorem for irreversible computing [R. Landauer, IBM J. Res. Devel. 5, 183 (1961)], arguing that because information erasure is an irreversible computation, there is an inherent entropy cost as the cortex transits from SWS into REM sleep.

Syntactic sequencing in Hebbian cell assemblies.

PubMed

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.

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

PubMed Central

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

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

PubMed

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.

A Neuromorphic Architecture for Object Recognition and Motion Anticipation Using Burst-STDP

PubMed Central

Balduzzi, David; Tononi, Giulio

2012-01-01

In this work we investigate the possibilities offered by a minimal framework of artificial spiking neurons to be deployed in silico. Here we introduce a hierarchical network architecture of spiking neurons which learns to recognize moving objects in a visual environment and determine the correct motor output for each object. These tasks are learned through both supervised and unsupervised spike timing dependent plasticity (STDP). STDP is responsible for the strengthening (or weakening) of synapses in relation to pre- and post-synaptic spike times and has been described as a Hebbian paradigm taking place both in vitro and in vivo. We utilize a variation of STDP learning, called burst-STDP, which is based on the notion that, since spikes are expensive in terms of energy consumption, then strong bursting activity carries more information than single (sparse) spikes. Furthermore, this learning algorithm takes advantage of homeostatic renormalization, which has been hypothesized to promote memory consolidation during NREM sleep. Using this learning rule, we design a spiking neural network architecture capable of object recognition, motion detection, attention towards important objects, and motor control outputs. We demonstrate the abilities of our design in a simple environment with distractor objects, multiple objects moving concurrently, and in the presence of noise. Most importantly, we show how this neural network is capable of performing these tasks using a simple leaky-integrate-and-fire (LIF) neuron model with binary synapses, making it fully compatible with state-of-the-art digital neuromorphic hardware designs. As such, the building blocks and learning rules presented in this paper appear promising for scalable fully neuromorphic systems to be implemented in hardware chips. PMID:22615855

Plasticity of cortical inhibition in dystonia is impaired after motor learning and paired-associative stimulation.

PubMed

Meunier, Sabine; Russmann, Heike; Shamim, Ejaz; Lamy, Jean-Charles; Hallett, Mark

2012-03-01

Artificial induction of plasticity by paired associative stimulation (PAS) in healthy volunteers (HV) demonstrates Hebbian-like plasticity in selected inhibitory networks as well as excitatory networks. In a group of 17 patients with focal hand dystonia and a group of 19 HV, we evaluated how PAS and the learning of a simple motor task influence the circuits supporting long-interval intracortical inhibition (LICI, reflecting activity of GABA(B) interneurons) and long-latency afferent inhibition (LAI, reflecting activity of somatosensory inputs to the motor cortex). In HV, PAS and motor learning induced long-term potentiation (LTP)-like plasticity of excitatory networks and a lasting decrease of LAI and LICI in the motor representation of the targeted or trained muscle. The better the motor performance, the larger was the decrease of LAI. Although motor performance in the patient group was similar to that of the control group, LAI did not decrease during the motor learning as it did in the control group. In contrast, LICI was normally modulated. In patients the results after PAS did not match those obtained after motor learning: LAI was paradoxically increased and LICI did not exhibit any change. In the normal situation, decreased excitability in inhibitory circuits after induction of LTP-like plasticity may help to shape the cortical maps according to the new sensorimotor task. In patients, the abnormal or absent modulation of afferent and intracortical long-interval inhibition might indicate maladaptive plasticity that possibly contributes to the difficulty that they have to learn a new sensorimotor task. © 2012 The Authors. European Journal of Neuroscience © 2012 Federation of European Neuroscience Societies and Blackwell Publishing Ltd.

A sub-1-volt analog metal oxide memristive-based synaptic device with large conductance change for energy-efficient spike-based computing systems

NASA Astrophysics Data System (ADS)

Hsieh, Cheng-Chih; Roy, Anupam; Chang, Yao-Feng; Shahrjerdi, Davood; Banerjee, Sanjay K.

2016-11-01

Nanoscale metal oxide memristors have potential in the development of brain-inspired computing systems that are scalable and efficient. In such systems, memristors represent the native electronic analogues of the biological synapses. In this work, we show cerium oxide based bilayer memristors that are forming-free, low-voltage (˜|0.8 V|), energy-efficient (full on/off switching at ˜8 pJ with 20 ns pulses, intermediate states switching at ˜fJ), and reliable. Furthermore, pulse measurements reveal the analog nature of the memristive device; that is, it can directly be programmed to intermediate resistance states. Leveraging this finding, we demonstrate spike-timing-dependent plasticity, a spike-based Hebbian learning rule. In those experiments, the memristor exhibits a marked change in the normalized synaptic strength (>30 times), when the pre- and post-synaptic neural spikes overlap. This demonstration is an important step towards the physical construction of high density and high connectivity neural networks.

Spontaneous scale-free structure in adaptive networks with synchronously dynamical linking

NASA Astrophysics Data System (ADS)

Yuan, Wu-Jie; Zhou, Jian-Fang; Li, Qun; Chen, De-Bao; Wang, Zhen

2013-08-01

Inspired by the anti-Hebbian learning rule in neural systems, we study how the feedback from dynamical synchronization shapes network structure by adding new links. Through extensive numerical simulations, we find that an adaptive network spontaneously forms scale-free structure, as confirmed in many real systems. Moreover, the adaptive process produces two nontrivial power-law behaviors of deviation strength from mean activity of the network and negative degree correlation, which exists widely in technological and biological networks. Importantly, these scalings are robust to variation of the adaptive network parameters, which may have meaningful implications in the scale-free formation and manipulation of dynamical networks. Our study thus suggests an alternative adaptive mechanism for the formation of scale-free structure with negative degree correlation, which means that nodes of high degree tend to connect, on average, with others of low degree and vice versa. The relevance of the results to structure formation and dynamical property in neural networks is briefly discussed as well.

Towards cortex sized artificial neural systems.

PubMed

Johansson, Christopher; Lansner, Anders

2007-01-01

We propose, implement, and discuss an abstract model of the mammalian neocortex. This model is instantiated with a sparse recurrently connected neural network that has spiking leaky integrator units and continuous Hebbian learning. First we study the structure, modularization, and size of neocortex, and then we describe a generic computational model of the cortical circuitry. A characterizing feature of the model is that it is based on the modularization of neocortex into hypercolumns and minicolumns. Both a floating- and fixed-point arithmetic implementation of the model are presented along with simulation results. We conclude that an implementation on a cluster computer is not communication but computation bounded. A mouse and rat cortex sized version of our model executes in 44% and 23% of real-time respectively. Further, an instance of the model with 1.6 x 10(6) units and 2 x 10(11) connections performed noise reduction and pattern completion. These implementations represent the current frontier of large-scale abstract neural network simulations in terms of network size and running speed.

Modeling complex tone perception: grouping harmonics with combination-sensitive neurons.

PubMed

Medvedev, Andrei V; Chiao, Faye; Kanwal, Jagmeet S

2002-06-01

Perception of complex communication sounds is a major function of the auditory system. To create a coherent precept of these sounds the auditory system may instantaneously group or bind multiple harmonics within complex sounds. This perception strategy simplifies further processing of complex sounds and facilitates their meaningful integration with other sensory inputs. Based on experimental data and a realistic model, we propose that associative learning of combinations of harmonic frequencies and nonlinear facilitation of responses to those combinations, also referred to as "combination-sensitivity," are important for spectral grouping. For our model, we simulated combination sensitivity using Hebbian and associative types of synaptic plasticity in auditory neurons. We also provided a parallel tonotopic input that converges and diverges within the network. Neurons in higher-order layers of the network exhibited an emergent property of multifrequency tuning that is consistent with experimental findings. Furthermore, this network had the capacity to "recognize" the pitch or fundamental frequency of a harmonic tone complex even when the fundamental frequency itself was missing.

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

PubMed Central

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 contexts. PMID:23966987

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

PubMed

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.

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

PubMed Central

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. PMID:26900845

Pattern Adaptation and Normalization Reweighting.

PubMed

Westrick, Zachary M; Heeger, David J; Landy, Michael S

2016-09-21

Adaptation to an oriented stimulus changes both the gain and preferred orientation of neural responses in V1. Neurons tuned near the adapted orientation are suppressed, and their preferred orientations shift away from the adapter. We propose a model in which weights of divisive normalization are dynamically adjusted to homeostatically maintain response products between pairs of neurons. We demonstrate that this adjustment can be performed by a very simple learning rule. Simulations of this model closely match existing data from visual adaptation experiments. We consider several alternative models, including variants based on homeostatic maintenance of response correlations or covariance, as well as feedforward gain-control models with multiple layers, and we demonstrate that homeostatic maintenance of response products provides the best account of the physiological data. Adaptation is a phenomenon throughout the nervous system in which neural tuning properties change in response to changes in environmental statistics. We developed a model of adaptation that combines normalization (in which a neuron's gain is reduced by the summed responses of its neighbors) and Hebbian learning (in which synaptic strength, in this case divisive normalization, is increased by correlated firing). The model is shown to account for several properties of adaptation in primary visual cortex in response to changes in the statistics of contour orientation. Copyright © 2016 the authors 0270-6474/16/369805-12$15.00/0.

How the mechanisms of long-term synaptic potentiation and depression serve experience-dependent plasticity in primary visual cortex

PubMed Central

Cooke, Sam F.; Bear, Mark F.

2014-01-01

Donald Hebb chose visual learning in primary visual cortex (V1) of the rodent to exemplify his theories of how the brain stores information through long-lasting homosynaptic plasticity. Here, we revisit V1 to consider roles for bidirectional ‘Hebbian’ plasticity in the modification of vision through experience. First, we discuss the consequences of monocular deprivation (MD) in the mouse, which have been studied by many laboratories over many years, and the evidence that synaptic depression of excitatory input from the thalamus is a primary contributor to the loss of visual cortical responsiveness to stimuli viewed through the deprived eye. Second, we describe a less studied, but no less interesting form of plasticity in the visual cortex known as stimulus-selective response potentiation (SRP). SRP results in increases in the response of V1 to a visual stimulus through repeated viewing and bears all the hallmarks of perceptual learning. We describe evidence implicating an important role for potentiation of thalamo-cortical synapses in SRP. In addition, we present new data indicating that there are some features of this form of plasticity that cannot be fully accounted for by such feed-forward Hebbian plasticity, suggesting contributions from intra-cortical circuit components. PMID:24298166

Causal manipulation of functional connectivity in a specific neural pathway during behaviour and at rest

PubMed Central

Johnen, Vanessa M; Neubert, Franz-Xaver; Buch, Ethan R; Verhagen, Lennart; O'Reilly, Jill X; Mars, Rogier B; Rushworth, Matthew F S

2015-01-01

Correlations in brain activity between two areas (functional connectivity) have been shown to relate to their underlying structural connections. We examine the possibility that functional connectivity also reflects short-term changes in synaptic efficacy. We demonstrate that paired transcranial magnetic stimulation (TMS) near ventral premotor cortex (PMv) and primary motor cortex (M1) with a short 8-ms inter-pulse interval evoking synchronous pre- and post-synaptic activity and which strengthens interregional connectivity between the two areas in a pattern consistent with Hebbian plasticity, leads to increased functional connectivity between PMv and M1 as measured with functional magnetic resonance imaging (fMRI). Moreover, we show that strengthening connectivity between these nodes has effects on a wider network of areas, such as decreasing coupling in a parallel motor programming stream. A control experiment revealed that identical TMS pulses at identical frequencies caused no change in fMRI-measured functional connectivity when the inter-pulse-interval was too long for Hebbian-like plasticity. DOI: http://dx.doi.org/10.7554/eLife.04585.001 PMID:25664941

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

PubMed Central

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 between words and referents. 5) Finally, the model illustrates that learning and referent selection/word recognition, though logically distinct, can be deeply and subtly related as phenomena like speed of processing and mutual exclusivity may derive in part from the way learning shapes the system. As a whole, this suggests more sophisticated ways of describing the interaction between situation- and developmental-time processes and points to the need for considering such interactions as a primary determinant of development and processing in children. PMID:23088341

A History of Spike-Timing-Dependent Plasticity

PubMed Central

Markram, Henry; Gerstner, Wulfram; Sjöström, Per Jesper

2011-01-01

How learning and memory is achieved in the brain is a central question in neuroscience. Key to today’s research into information storage in the brain is the concept of synaptic plasticity, a notion that has been heavily influenced by Hebb's (1949) postulate. Hebb conjectured that repeatedly and persistently co-active cells should increase connective strength among populations of interconnected neurons as a means of storing a memory trace, also known as an engram. Hebb certainly was not the first to make such a conjecture, as we show in this history. Nevertheless, literally thousands of studies into the classical frequency-dependent paradigm of cellular learning rules were directly inspired by the Hebbian postulate. But in more recent years, a novel concept in cellular learning has emerged, where temporal order instead of frequency is emphasized. This new learning paradigm – known as spike-timing-dependent plasticity (STDP) – has rapidly gained tremendous interest, perhaps because of its combination of elegant simplicity, biological plausibility, and computational power. But what are the roots of today’s STDP concept? Here, we discuss several centuries of diverse thinking, beginning with philosophers such as Aristotle, Locke, and Ribot, traversing, e.g., Lugaro’s plasticità and Rosenblatt’s perceptron, and culminating with the discovery of STDP. We highlight interactions between theoretical and experimental fields, showing how discoveries sometimes occurred in parallel, seemingly without much knowledge of the other field, and sometimes via concrete back-and-forth communication. We point out where the future directions may lie, which includes interneuron STDP, the functional impact of STDP, its mechanisms and its neuromodulatory regulation, and the linking of STDP to the developmental formation and continuous plasticity of neuronal networks. PMID:22007168

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

PubMed Central

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

GABAa excitation and synaptogenesis after Status Epilepticus - A computational study.

PubMed

França, Keite Lira de Almeida; de Almeida, Antônio-Carlos Guimarães; Saddow, Stephen E; Santos, Luiz Eduardo Canton; Scorza, Carla Alessandra; Scorza, Fulvio Alexandre; Rodrigues, Antônio Márcio

2018-03-08

The role of GABAergic neurotransmission on epileptogenesis has been the subject of speculation according to different approaches. However, it is a very complex task to specifically consider the action of the GABAa neurotransmitter, which, in its dependence on the intracellular level of Cl - , can change its effect from inhibitory to excitatory. We have developed a computational model that represents the dentate gyrus and is composed of three different populations of neurons (granule cells, interneurons and mossy cells) that are mutually interconnected. The interconnections of the neurons were based on compensation theory with Hebbian and anti-Hebbian rules. The model also incorporates non-synaptic mechanisms to control the ionic homeostasis and was able to reproduce ictal discharges. The goal of the work was to investigate the hypothesis that the observed aberrant sprouting is promoted by GABAa excitatory action. Conjointly with the abnormal sprouting of the mossy fibres, the simulations show a reduction of the mossy cells connections in the network and an increased inhibition of the interneurons as a response of the neuronal network to control the activity. This finding contributes to increasing the changes in the connectivity of the neuronal circuitry and to increasing the epileptiform activity occurrences.

Excitatory and inhibitory STDP jointly tune feedforward neural circuits to selectively propagate correlated spiking activity

PubMed Central

Kleberg, Florence I.; Fukai, Tomoki; Gilson, Matthieu

2014-01-01

Spike-timing-dependent plasticity (STDP) has been well established between excitatory neurons and several computational functions have been proposed in various neural systems. Despite some recent efforts, however, there is a significant lack of functional understanding of inhibitory STDP (iSTDP) and its interplay with excitatory STDP (eSTDP). Here, we demonstrate by analytical and numerical methods that iSTDP contributes crucially to the balance of excitatory and inhibitory weights for the selection of a specific signaling pathway among other pathways in a feedforward circuit. This pathway selection is based on the high sensitivity of STDP to correlations in spike times, which complements a recent proposal for the role of iSTDP in firing-rate based selection. Our model predicts that asymmetric anti-Hebbian iSTDP exceeds asymmetric Hebbian iSTDP for supporting pathway-specific balance, which we show is useful for propagating transient neuronal responses. Furthermore, we demonstrate how STDPs at excitatory–excitatory, excitatory–inhibitory, and inhibitory–excitatory synapses cooperate to improve the pathway selection. We propose that iSTDP is crucial for shaping the network structure that achieves efficient processing of synchronous spikes. PMID:24847242

Associative plasticity in intracortical inhibitory circuits in human motor cortex.

PubMed

Russmann, Heike; Lamy, Jean-Charles; Shamim, Ejaz A; Meunier, Sabine; Hallett, Mark

2009-06-01

Paired associative stimulation (PAS) is a transcranial magnetic stimulation technique inducing Hebbian-like synaptic plasticity in the human motor cortex (M1). PAS is produced by repetitive pairing of a peripheral nerve shock and a transcranial magnetic stimulus (TMS). Its effect is assessed by a change in size of a motor evoked response (MEP). MEP size results from excitatory and inhibitory influences exerted on cortical pyramidal cells, but no robust effects on inhibitory networks have been demonstrated so far. In 38 healthy volunteers, we assessed whether a PAS intervention influences three intracortical inhibitory circuits: short (SICI) and long (LICI) intracortical inhibitions reflecting activity of GABA(A) and GABA(B) interneurons, respectively, and long afferent inhibition (LAI) reflecting activity of somatosensory inputs. After PAS, MEP sizes, LICI and LAI levels were significantly changed while changes of SICI were inconsistent. The changes in LICI and LAI lasted 45 min after PAS. Their direction depended on the delay between the arrival time of the afferent volley at the cortex and the TMS-induced cortical activation during the PAS. PAS influences inhibitory circuits in M1. PAS paradigms can demonstrate Hebbian-like plasticity at selected inhibitory networks as well as excitatory networks.

Experience-driven plasticity in binocular vision

PubMed Central

Klink, P. Christiaan; Brascamp, Jan W.; Blake, Randolph; van Wezel, Richard J.A.

2010-01-01

Summary Experience-driven neuronal plasticity allows the brain to adapt its functional connectivity to recent sensory input. Here we use binocular rivalry [1], an experimental paradigm where conflicting images are presented to the individual eyes, to demonstrate plasticity in the neuronal mechanisms that convert visual information from two separated retinas into single perceptual experiences. Perception during binocular rivalry tended to initially consist of alternations between exclusive representations of monocularly defined images, but upon prolonged exposure, mixture percepts became more prevalent. The completeness of suppression, reflected in the incidence of mixture percepts, plausibly reflects the strength of inhibition that likely plays a role in binocular rivalry [2]. Recovery of exclusivity was possible, but required highly specific binocular stimulation. Documenting the prerequisites for these observed changes in perceptual exclusivity, our experiments suggest experience-driven plasticity at interocular inhibitory synapses, driven by the (lack of) correlated activity of neurons representing the conflicting stimuli. This form of plasticity is consistent with a previously proposed, but largely untested, anti-Hebbian learning mechanism for inhibitory synapses in vision [3, 4]. Our results implicate experience-driven plasticity as one governing principle in the neuronal organization of binocular vision. PMID:20674360

Extracting grid cell characteristics from place cell inputs using non-negative principal component analysis

PubMed Central

Dordek, Yedidyah; Soudry, Daniel; Meir, Ron; Derdikman, Dori

2016-01-01

Many recent models study the downstream projection from grid cells to place cells, while recent data have pointed out the importance of the feedback projection. We thus asked how grid cells are affected by the nature of the input from the place cells. We propose a single-layer neural network with feedforward weights connecting place-like input cells to grid cell outputs. Place-to-grid weights are learned via a generalized Hebbian rule. The architecture of this network highly resembles neural networks used to perform Principal Component Analysis (PCA). Both numerical results and analytic considerations indicate that if the components of the feedforward neural network are non-negative, the output converges to a hexagonal lattice. Without the non-negativity constraint, the output converges to a square lattice. Consistent with experiments, grid spacing ratio between the first two consecutive modules is −1.4. Our results express a possible linkage between place cell to grid cell interactions and PCA. DOI: http://dx.doi.org/10.7554/eLife.10094.001 PMID:26952211

Spike Train Auto-Structure Impacts Post-Synaptic Firing and Timing-Based Plasticity

PubMed Central

Scheller, Bertram; Castellano, Marta; Vicente, Raul; Pipa, Gordon

2011-01-01

Cortical neurons are typically driven by several thousand synapses. The precise spatiotemporal pattern formed by these inputs can modulate the response of a post-synaptic cell. In this work, we explore how the temporal structure of pre-synaptic inhibitory and excitatory inputs impact the post-synaptic firing of a conductance-based integrate and fire neuron. Both the excitatory and inhibitory input was modeled by renewal gamma processes with varying shape factors for modeling regular and temporally random Poisson activity. We demonstrate that the temporal structure of mutually independent inputs affects the post-synaptic firing, while the strength of the effect depends on the firing rates of both the excitatory and inhibitory inputs. In a second step, we explore the effect of temporal structure of mutually independent inputs on a simple version of Hebbian learning, i.e., hard bound spike-timing-dependent plasticity. We explore both the equilibrium weight distribution and the speed of the transient weight dynamics for different mutually independent gamma processes. We find that both the equilibrium distribution of the synaptic weights and the speed of synaptic changes are modulated by the temporal structure of the input. Finally, we highlight that the sensitivity of both the post-synaptic firing as well as the spike-timing-dependent plasticity on the auto-structure of the input of a neuron could be used to modulate the learning rate of synaptic modification. PMID:22203800

Reading a radiologist's mind: monitoring rising and falling interest levels while scanning chest x-rays

NASA Astrophysics Data System (ADS)

Alzubaidi, Mohammad; Patel, Ameet; Panchanathan, Sethuraman; Black, John A., Jr.

2010-02-01

Radiological images constitute a special class of images that are captured (or computed) specifically for the purpose of diagnosing patients. However, because these are not "natural" images, radiologists must be trained to interpret them through a process called "perceptual learning". However, because perceptual learning is implicit, experienced radiologists may sometimes find it difficult to explicitly (i.e. verbally) train less experienced colleagues. As a result, current methods of training can take years before a new radiologist is fully competent to independently interpret medical images. We hypothesize that eye tracking technology (coupled with multimedia technology) can be used to accelerate the process of perceptual training, through a Hebbian learning process. This would be accomplished by providing a radiologist-in-training with real-time feedback as he/she is fixating on important regions of an image. Of course this requires that the training system have information about what regions of an image are important - information that could presumably be solicited from experienced radiologists. However, our previous work has suggested that experienced radiologists are not always aware of those regions of an image that attract their attention, but are not clinically significant - information that is very important to a radiologist in training. This paper discusses a study in which local entropy computations were done on scan path data, and were found to provide a quantitative measure of the moment-by-moment interest level of radiologists as they scanned chest x-rays. The results also showed a striking contrast between the moment-by-moment deployment of attention between experienced radiologists and radiologists in training.

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

PubMed

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.

Neural plasticity and behavior - sixty years of conceptual advances.

PubMed

Sweatt, J David

2016-10-01

This brief review summarizes 60 years of conceptual advances that have demonstrated a role for active changes in neuronal connectivity as a controller of behavior and behavioral change. Seminal studies in the first phase of the six-decade span of this review firmly established the cellular basis of behavior - a concept that we take for granted now, but which was an open question at the time. Hebbian plasticity, including long-term potentiation and long-term depression, was then discovered as being important for local circuit refinement in the context of memory formation and behavioral change and stabilization in the mammalian central nervous system. Direct demonstration of plasticity of neuronal circuit function in vivo, for example, hippocampal neurons forming place cell firing patterns, extended this concept. However, additional neurophysiologic and computational studies demonstrated that circuit development and stabilization additionally relies on non-Hebbian, homoeostatic, forms of plasticity, such as synaptic scaling and control of membrane intrinsic properties. Activity-dependent neurodevelopment was found to be associated with cell-wide adjustments in post-synaptic receptor density, and found to occur in conjunction with synaptic pruning. Pioneering cellular neurophysiologic studies demonstrated the critical roles of transmembrane signal transduction, NMDA receptor regulation, regulation of neural membrane biophysical properties, and back-propagating action potential in critical time-dependent coincidence detection in behavior-modifying circuits. Concerning the molecular mechanisms underlying these processes, regulation of gene transcription was found to serve as a bridge between experience and behavioral change, closing the 'nature versus nurture' divide. Both active DNA (de)methylation and regulation of chromatin structure have been validated as crucial regulators of gene transcription during learning. The discovery of protein synthesis dependence on the acquisition of behavioral change was an influential discovery in the neurochemistry of behavioral modification. Higher order cognitive functions such as decision making and spatial and language learning were also discovered to hinge on neural plasticity mechanisms. The role of disruption of these processes in intellectual disabilities, memory disorders, and drug addiction has recently been clarified based on modern genetic techniques, including in the human. The area of neural plasticity and behavior has seen tremendous advances over the last six decades, with many of those advances being specifically in the neurochemistry domain. This review provides an overview of the progress in the area of neuroplasticity and behavior over the life-span of the Journal of Neurochemistry. To organize the broad literature base, the review collates progress into fifteen broad categories identified as 'conceptual advances', as viewed by the author. The fifteen areas are delineated in the figure above. This article is part of the 60th Anniversary special issue. © 2016 International Society for Neurochemistry.

A Re-Examination of Hebbian-Covariance Rules and Spike Timing-Dependent Plasticity in Cat Visual Cortex in vivo

PubMed Central

Frégnac, Yves; Pananceau, Marc; René, Alice; Huguet, Nazyed; Marre, Olivier; Levy, Manuel; Shulz, Daniel E.

2010-01-01

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-occurrence 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 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 literature leads us to re-examine the experimental status of spike timing-dependent potentiation in adult cortex. We propose the existence of a correlation-based threshold in vivo, limiting the expression of STDP-induced changes outside the critical period, and which accounts for the stability of synaptic weights during sensory cortical processing in the absence of attention or reward-gated supervision. PMID:21423533

Resonant spatiotemporal learning in large random recurrent networks.

PubMed

Daucé, Emmanuel; Quoy, Mathias; Doyon, Bernard

2002-09-01

Taking a global analogy with the structure of perceptual biological systems, we present a system composed of two layers of real-valued sigmoidal neurons. The primary layer receives stimulating spatiotemporal signals, and the secondary layer is a fully connected random recurrent network. This secondary layer spontaneously displays complex chaotic dynamics. All connections have a constant time delay. We use for our experiments a Hebbian (covariance) learning rule. This rule slowly modifies the weights under the influence of a periodic stimulus. The effect of learning is twofold: (i) it simplifies the secondary-layer dynamics, which eventually stabilizes to a periodic orbit; and (ii) it connects the secondary layer to the primary layer, and realizes a feedback from the secondary to the primary layer. This feedback signal is added to the incoming signal, and matches it (i.e., the secondary layer performs a one-step prediction of the forthcoming stimulus). After learning, a resonant behavior can be observed: the system resonates with familiar stimuli, which activates a feedback signal. In particular, this resonance allows the recognition and retrieval of partial signals, and dynamic maintenance of the memory of past stimuli. This resonance is highly sensitive to the temporal relationships and to the periodicity of the presented stimuli. When we present stimuli which do not match in time or space, the feedback remains silent. The number of different stimuli for which resonant behavior can be learned is analyzed. As with Hopfield networks, the capacity is proportional to the size of the second, recurrent layer. Moreover, the high capacity displayed allows the implementation of our model on real-time systems interacting with their environment. Such an implementation is reported in the case of a simple behavior-based recognition task on a mobile robot. Finally, we present some functional analogies with biological systems in terms of autonomy and dynamic binding, and present some hypotheses on the computational role of feedback connections.

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

PubMed

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.

Hebbian Plasticity Realigns Grid Cell Activity with External Sensory Cues in Continuous Attractor Models

PubMed Central

Mulas, Marcello; Waniek, Nicolai; Conradt, Jörg

2016-01-01

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 over time 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. PMID:26924979

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

PubMed Central

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

Role of ionotropic glutamate receptors in LTP in rat hippocampal CA1 oriens-lacunosum moleculare interneurons

PubMed Central

Oren, Iris; Nissen, Wiebke; Kullmann, Dimitri M.; Somogyi, Peter; Lamsa, Karri P.

2009-01-01

Some interneurons of the hippocampus exhibit NMDA receptor-independent long-term potentiation (LTP) that is induced by presynaptic glutamate release when the postsynaptic membrane potential is hyperpolarized. This ‘anti-Hebbian’ form of LTP is prevented by postsynaptic depolarization or by blocking AMPA and kainate receptors. Although both AMPA and kainate receptors are expressed in hippocampal interneurons, their relative roles in anti-Hebbian LTP are not known. Because interneuron diversity potentially conceals simple rules underlying different forms of plasticity, we focus on glutamatergic synapses onto a subset of interneurons with dendrites in stratum oriens and a main ascending axon that projects to stratum lacunosum-moleculare, the O-LM cells. We show that anti-Hebbian LTP in O-LM interneurons has consistent induction and expression properties, and is prevented by selective inhibition of AMPA receptors. The majority of the ionotropic glutamatergic synaptic current in these cells is mediated by inwardly rectifying Ca2+ -permeable AMPA receptors. Although GluR5-containing kainate receptors contribute to synaptic currents at high stimulus frequency, they are not required for LTP induction. Glutamatergic synapses on O-LM cells thus behave in a homogeneous manner, and exhibit LTP dependent on Ca2+-permeable AMPA receptors. PMID:19176803

Impact of a Differential Learning Approach on Practical Exam Performance: A Controlled Study in a Preclinical Dental Course.

PubMed

Pabel, Sven-Olav; Pabel, Anne-Kathrin; Schmickler, Jan; Schulz, Xenia; Wiegand, Annette

2017-09-01

The aim of this study was to evaluate if differential learning in a preclinical dental course impacted the performance of dental students in a practical exam (preparation of a gold partial crown) immediately after the training session and 20 weeks later compared to conventional learning. This controlled study was performed in a preclinical course in operative dentistry at a dental school in Germany. Third-year students were trained in preparing gold partial crowns by using either the conventional learning (n=41) or the differential learning approach (n=32). The differential learning approach consisted of 20 movement exercises with a continuous change of movement execution during the learning session, while the conventional learning approach was mainly based on repetition, a methodological series of exercises, and correction of preparations during the training phase. Practical exams were performed immediately after the training session (T1) and 20 weeks later (T2, retention test). Preparations were rated by four independent and blinded examiners. At T1, no significant difference between the performance (exam passed) of the two groups was detected (conventional learning: 54.3%, differential learning: 68.0%). At T2, significantly more students passed the exam when trained by the differential learning approach (68.8%) than by the conventional learning approach (18.9%). Interrater reliability was moderate (Kappa: 0.57, T1) or substantial (Kappa: 0.67, T2), respectively. These results suggest that a differential learning approach can increase the manual skills of dental students.

A framework for plasticity implementation on the SpiNNaker neural architecture.

PubMed

Galluppi, Francesco; Lagorce, Xavier; Stromatias, Evangelos; Pfeiffer, Michael; Plana, Luis A; Furber, Steve B; Benosman, Ryad B

2014-01-01

Many of the precise biological mechanisms of synaptic plasticity remain elusive, but simulations of neural networks have greatly enhanced our understanding of how specific global functions arise from the massively parallel computation of neurons and local Hebbian or spike-timing dependent plasticity rules. For simulating large portions of neural tissue, this has created an increasingly strong need for large scale simulations of plastic neural networks on special purpose hardware platforms, because synaptic transmissions and updates are badly matched to computing style supported by current architectures. Because of the great diversity of biological plasticity phenomena and the corresponding diversity of models, there is a great need for testing various hypotheses about plasticity before committing to one hardware implementation. Here we present a novel framework for investigating different plasticity approaches on the SpiNNaker distributed digital neural simulation platform. The key innovation of the proposed architecture is to exploit the reconfigurability of the ARM processors inside SpiNNaker, dedicating a subset of them exclusively to process synaptic plasticity updates, while the rest perform the usual neural and synaptic simulations. We demonstrate the flexibility of the proposed approach by showing the implementation of a variety of spike- and rate-based learning rules, including standard Spike-Timing dependent plasticity (STDP), voltage-dependent STDP, and the rate-based BCM rule. We analyze their performance and validate them by running classical learning experiments in real time on a 4-chip SpiNNaker board. The result is an efficient, modular, flexible and scalable framework, which provides a valuable tool for the fast and easy exploration of learning models of very different kinds on the parallel and reconfigurable SpiNNaker system.

A framework for plasticity implementation on the SpiNNaker neural architecture

PubMed Central

Galluppi, Francesco; Lagorce, Xavier; Stromatias, Evangelos; Pfeiffer, Michael; Plana, Luis A.; Furber, Steve B.; Benosman, Ryad B.

2015-01-01

Many of the precise biological mechanisms of synaptic plasticity remain elusive, but simulations of neural networks have greatly enhanced our understanding of how specific global functions arise from the massively parallel computation of neurons and local Hebbian or spike-timing dependent plasticity rules. For simulating large portions of neural tissue, this has created an increasingly strong need for large scale simulations of plastic neural networks on special purpose hardware platforms, because synaptic transmissions and updates are badly matched to computing style supported by current architectures. Because of the great diversity of biological plasticity phenomena and the corresponding diversity of models, there is a great need for testing various hypotheses about plasticity before committing to one hardware implementation. Here we present a novel framework for investigating different plasticity approaches on the SpiNNaker distributed digital neural simulation platform. The key innovation of the proposed architecture is to exploit the reconfigurability of the ARM processors inside SpiNNaker, dedicating a subset of them exclusively to process synaptic plasticity updates, while the rest perform the usual neural and synaptic simulations. We demonstrate the flexibility of the proposed approach by showing the implementation of a variety of spike- and rate-based learning rules, including standard Spike-Timing dependent plasticity (STDP), voltage-dependent STDP, and the rate-based BCM rule. We analyze their performance and validate them by running classical learning experiments in real time on a 4-chip SpiNNaker board. The result is an efficient, modular, flexible and scalable framework, which provides a valuable tool for the fast and easy exploration of learning models of very different kinds on the parallel and reconfigurable SpiNNaker system. PMID:25653580

Advanced soft computing diagnosis method for tumour grading.

PubMed

Papageorgiou, E I; Spyridonos, P P; Stylios, C D; Ravazoula, P; Groumpos, P P; Nikiforidis, G N

2006-01-01

To develop an advanced diagnostic method for urinary bladder tumour grading. A novel soft computing modelling methodology based on the augmentation of fuzzy cognitive maps (FCMs) with the unsupervised active Hebbian learning (AHL) algorithm is applied. One hundred and twenty-eight cases of urinary bladder cancer were retrieved from the archives of the Department of Histopathology, University Hospital of Patras, Greece. All tumours had been characterized according to the classical World Health Organization (WHO) grading system. To design the FCM model for tumour grading, three experts histopathologists defined the main histopathological features (concepts) and their impact on grade characterization. The resulted FCM model consisted of nine concepts. Eight concepts represented the main histopathological features for tumour grading. The ninth concept represented the tumour grade. To increase the classification ability of the FCM model, the AHL algorithm was applied to adjust the weights of the FCM. The proposed FCM grading model achieved a classification accuracy of 72.5%, 74.42% and 95.55% for tumours of grades I, II and III, respectively. An advanced computerized method to support tumour grade diagnosis decision was proposed and developed. The novelty of the method is based on employing the soft computing method of FCMs to represent specialized knowledge on histopathology and on augmenting FCMs ability using an unsupervised learning algorithm, the AHL. The proposed method performs with reasonably high accuracy compared to other existing methods and at the same time meets the physicians' requirements for transparency and explicability.

Developmental metaplasticity in neural circuit codes of firing and structure.

PubMed

Baram, Yoram

2017-01-01

Firing-rate dynamics have been hypothesized to mediate inter-neural information transfer in the brain. While the Hebbian paradigm, relating learning and memory to firing activity, has put synaptic efficacy variation at the center of cortical plasticity, we suggest that the external expression of plasticity by changes in the firing-rate dynamics represents a more general notion of plasticity. Hypothesizing that time constants of plasticity and firing dynamics increase with age, and employing the filtering property of the neuron, we obtain the elementary code of global attractors associated with the firing-rate dynamics in each developmental stage. We define a neural circuit connectivity code as an indivisible set of circuit structures generated by membrane and synapse activation and silencing. Synchronous firing patterns under parameter uniformity, and asynchronous circuit firing are shown to be driven, respectively, by membrane and synapse silencing and reactivation, and maintained by the neuronal filtering property. Analytic, graphical and simulation representation of the discrete iteration maps and of the global attractor codes of neural firing rate are found to be consistent with previous empirical neurobiological findings, which have lacked, however, a specific correspondence between firing modes, time constants, circuit connectivity and cortical developmental stages. Copyright © 2016 Elsevier Ltd. All rights reserved.

Uninformative memories will prevail: the storage of correlated representations and its consequences.

PubMed

Kropff, Emilio; Treves, Alessandro

2007-11-01

Autoassociative networks were proposed in the 80's as simplified models of memory function in the brain, using recurrent connectivity with Hebbian plasticity to store patterns of neural activity that can be later recalled. This type of computation has been suggested to take place in the CA3 region of the hippocampus and at several levels in the cortex. One of the weaknesses of these models is their apparent inability to store correlated patterns of activity. We show, however, that a small and biologically plausible modification in the "learning rule" (associating to each neuron a plasticity threshold that reflects its popularity) enables the network to handle correlations. We study the stability properties of the resulting memories (in terms of their resistance to the damage of neurons or synapses), finding a novel property of autoassociative networks: not all memories are equally robust, and the most informative are also the most sensitive to damage. We relate these results to category-specific effects in semantic memory patients, where concepts related to "non-living things" are usually more resistant to brain damage than those related to "living things," a phenomenon suspected to be rooted in the correlation between representations of concepts in the cortex.

Theory of optimal balance predicts and explains the amplitude and decay time of synaptic inhibition

PubMed Central

Kim, Jaekyung K.; Fiorillo, Christopher D.

2017-01-01

Synaptic inhibition counterbalances excitation, but it is not known what constitutes optimal inhibition. We previously proposed that perfect balance is achieved when the peak of an excitatory postsynaptic potential (EPSP) is exactly at spike threshold, so that the slightest variation in excitation determines whether a spike is generated. Using simulations, we show that the optimal inhibitory postsynaptic conductance (IPSG) increases in amplitude and decay rate as synaptic excitation increases from 1 to 800 Hz. As further proposed by theory, we show that optimal IPSG parameters can be learned through anti-Hebbian rules. Finally, we compare our theoretical optima to published experimental data from 21 types of neurons, in which rates of synaptic excitation and IPSG decay times vary by factors of about 100 (5–600 Hz) and 50 (1–50 ms), respectively. From an infinite range of possible decay times, theory predicted experimental decay times within less than a factor of 2. Across a distinct set of 15 types of neuron recorded in vivo, theory predicted the amplitude of synaptic inhibition within a factor of 1.7. Thus, the theory can explain biophysical quantities from first principles. PMID:28281523

Synaptic plasticity in a cerebellum-like structure depends on temporal order

NASA Astrophysics Data System (ADS)

Bell, Curtis C.; Han, Victor Z.; Sugawara, Yoshiko; Grant, Kirsty

1997-05-01

Cerebellum-like structures in fish appear to act as adaptive sensory processors, in which learned predictions about sensory input are generated and subtracted from actual sensory input, allowing unpredicted inputs to stand out1-3. Pairing sensory input with centrally originating predictive signals, such as corollary discharge signals linked to motor commands, results in neural responses to the predictive signals alone that are Negative images' of the previously paired sensory responses. Adding these 'negative images' to actual sensory inputs minimizes the neural response to predictable sensory features. At the cellular level, sensory input is relayed to the basal region of Purkinje-like cells, whereas predictive signals are relayed by parallel fibres to the apical dendrites of the same cells4. The generation of negative images could be explained by plasticity at parallel fibre synapses5-7. We show here that such plasticity exists in the electrosensory lobe of mormyrid electric fish and that it has the necessary properties for such a model: it is reversible, anti-hebbian (excitatory postsynaptic potentials (EPSPs) are depressed after pairing with a postsynaptic spike) and tightly dependent on the sequence of pre- and postsynaptic events, with depression occurring only if the postsynaptic spike follows EPSP onset within 60 ms.

A model for evolution of overlapping community networks

NASA Astrophysics Data System (ADS)

Karan, Rituraj; Biswal, Bibhu

2017-05-01

A model is proposed for the evolution of network topology in social networks with overlapping community structure. Starting from an initial community structure that is defined in terms of group affiliations, the model postulates that the subsequent growth and loss of connections is similar to the Hebbian learning and unlearning in the brain and is governed by two dominant factors: the strength and frequency of interaction between the members, and the degree of overlap between different communities. The temporal evolution from an initial community structure to the current network topology can be described based on these two parameters. It is possible to quantify the growth occurred so far and predict the final stationary state to which the network is likely to evolve. Applications in epidemiology or the spread of email virus in a computer network as well as finding specific target nodes to control it are envisaged. While facing the challenge of collecting and analyzing large-scale time-resolved data on social groups and communities one faces the most basic questions: how do communities evolve in time? This work aims to address this issue by developing a mathematical model for the evolution of community networks and studying it through computer simulation.

Theory of optimal balance predicts and explains the amplitude and decay time of synaptic inhibition

NASA Astrophysics Data System (ADS)

Kim, Jaekyung K.; Fiorillo, Christopher D.

2017-03-01

Synaptic inhibition counterbalances excitation, but it is not known what constitutes optimal inhibition. We previously proposed that perfect balance is achieved when the peak of an excitatory postsynaptic potential (EPSP) is exactly at spike threshold, so that the slightest variation in excitation determines whether a spike is generated. Using simulations, we show that the optimal inhibitory postsynaptic conductance (IPSG) increases in amplitude and decay rate as synaptic excitation increases from 1 to 800 Hz. As further proposed by theory, we show that optimal IPSG parameters can be learned through anti-Hebbian rules. Finally, we compare our theoretical optima to published experimental data from 21 types of neurons, in which rates of synaptic excitation and IPSG decay times vary by factors of about 100 (5-600 Hz) and 50 (1-50 ms), respectively. From an infinite range of possible decay times, theory predicted experimental decay times within less than a factor of 2. Across a distinct set of 15 types of neuron recorded in vivo, theory predicted the amplitude of synaptic inhibition within a factor of 1.7. Thus, the theory can explain biophysical quantities from first principles.

A robotic approach to understanding the role and the mechanism of vicarious trial-and-error in a T-maze task.

PubMed

Matsuda, Eiko; Hubert, Julien; Ikegami, Takashi

2014-01-01

Vicarious trial-and-error (VTE) is a behavior observed in rat experiments that seems to suggest self-conflict. This behavior is seen mainly when the rats are uncertain about making a decision. The presence of VTE is regarded as an indicator of a deliberative decision-making process, that is, searching, predicting, and evaluating outcomes. This process is slower than automated decision-making processes, such as reflex or habituation, but it allows for flexible and ongoing control of behavior. In this study, we propose for the first time a robotic model of VTE to see if VTE can emerge just from a body-environment interaction and to show the underlying mechanism responsible for the observation of VTE and the advantages provided by it. We tried several robots with different parameters, and we have found that they showed three different types of VTE: high numbers of VTE at the beginning of learning, decreasing numbers afterward (similar VTE pattern to experiments with rats), low during the whole learning period, and high numbers all the time. Therefore, we were able to reproduce the phenomenon of VTE in a model robot using only a simple dynamical neural network with Hebbian learning, which suggests that VTE is an emergent property of a plastic and embodied neural network. From a comparison of the three types of VTE, we demonstrated that 1) VTE is associated with chaotic activity of neurons in our model and 2) VTE-showing robots were robust to environmental perturbations. We suggest that the instability of neuronal activity found in VTE allows ongoing learning to rebuild its strategy continuously, which creates robust behavior. Based on these results, we suggest that VTE is caused by a similar mechanism in biology and leads to robust decision making in an analogous way.

Training with Differential Outcomes Enhances Discriminative Learning and Visuospatial Recognition Memory in Children Born Prematurely

ERIC Educational Resources Information Center

Martinez, Lourdes; Mari-Beffa, Paloma; Roldan-Tapia, Dolores; Ramos-Lizana, Julio; Fuentes, Luis J.; Estevez, Angeles F.

2012-01-01

Previous studies have demonstrated that discriminative learning is facilitated when a particular outcome is associated with each relation to be learned. When this training procedure is applied (the differential outcome procedure; DOP), learning is faster and more accurate than when the more common non-differential outcome procedure is used. This…

A Systemic View of the Learning and Differentiation of Scientific Concepts: The Case of Electric Current and Voltage Revisited

ERIC Educational Resources Information Center

Koponen, Ismo T.; Kokkonen, Tommi

2014-01-01

In learning conceptual knowledge in physics, a common problem is the incompleteness of a learning process, where students' personal, often undifferentiated concepts take on more scientific and differentiated form. With regard to such concept learning and differentiation, this study proposes a systemic view in which concepts are considered as…

Self-Organization of Microcircuits in Networks of Spiking Neurons with Plastic Synapses.

PubMed

Ocker, Gabriel Koch; Litwin-Kumar, Ashok; Doiron, Brent

2015-08-01

The synaptic connectivity of cortical networks features an overrepresentation of certain wiring motifs compared to simple random-network models. This structure is shaped, in part, by synaptic plasticity that promotes or suppresses connections between neurons depending on their joint spiking activity. Frequently, theoretical studies focus on how feedforward inputs drive plasticity to create this network structure. We study the complementary scenario of self-organized structure in a recurrent network, with spike timing-dependent plasticity driven by spontaneous dynamics. We develop a self-consistent theory for the evolution of network structure by combining fast spiking covariance with a slow evolution of synaptic weights. Through a finite-size expansion of network dynamics we obtain a low-dimensional set of nonlinear differential equations for the evolution of two-synapse connectivity motifs. With this theory in hand, we explore how the form of the plasticity rule drives the evolution of microcircuits in cortical networks. When potentiation and depression are in approximate balance, synaptic dynamics depend on weighted divergent, convergent, and chain motifs. For additive, Hebbian STDP these motif interactions create instabilities in synaptic dynamics that either promote or suppress the initial network structure. Our work provides a consistent theoretical framework for studying how spiking activity in recurrent networks interacts with synaptic plasticity to determine network structure.

Self-Organization of Microcircuits in Networks of Spiking Neurons with Plastic Synapses

PubMed Central

Ocker, Gabriel Koch; Litwin-Kumar, Ashok; Doiron, Brent

2015-01-01

The synaptic connectivity of cortical networks features an overrepresentation of certain wiring motifs compared to simple random-network models. This structure is shaped, in part, by synaptic plasticity that promotes or suppresses connections between neurons depending on their joint spiking activity. Frequently, theoretical studies focus on how feedforward inputs drive plasticity to create this network structure. We study the complementary scenario of self-organized structure in a recurrent network, with spike timing-dependent plasticity driven by spontaneous dynamics. We develop a self-consistent theory for the evolution of network structure by combining fast spiking covariance with a slow evolution of synaptic weights. Through a finite-size expansion of network dynamics we obtain a low-dimensional set of nonlinear differential equations for the evolution of two-synapse connectivity motifs. With this theory in hand, we explore how the form of the plasticity rule drives the evolution of microcircuits in cortical networks. When potentiation and depression are in approximate balance, synaptic dynamics depend on weighted divergent, convergent, and chain motifs. For additive, Hebbian STDP these motif interactions create instabilities in synaptic dynamics that either promote or suppress the initial network structure. Our work provides a consistent theoretical framework for studying how spiking activity in recurrent networks interacts with synaptic plasticity to determine network structure. PMID:26291697

Differential theory of learning for efficient neural network pattern recognition

NASA Astrophysics Data System (ADS)

Hampshire, John B., II; Vijaya Kumar, Bhagavatula

1993-09-01

We describe a new theory of differential learning by which a broad family of pattern classifiers (including many well-known neural network paradigms) can learn stochastic concepts efficiently. We describe the relationship between a classifier's ability to generate well to unseen test examples and the efficiency of the strategy by which it learns. We list a series of proofs that differential learning is efficient in its information and computational resource requirements, whereas traditional probabilistic learning strategies are not. The proofs are illustrated by a simple example that lends itself to closed-form analysis. We conclude with an optical character recognition task for which three different types of differentially generated classifiers generalize significantly better than their probabilistically generated counterparts.

Differential theory of learning for efficient neural network pattern recognition

NASA Astrophysics Data System (ADS)

Hampshire, John B., II; Vijaya Kumar, Bhagavatula

1993-08-01

We describe a new theory of differential learning by which a broad family of pattern classifiers (including many well-known neural network paradigms) can learn stochastic concepts efficiently. We describe the relationship between a classifier's ability to generalize well to unseen test examples and the efficiency of the strategy by which it learns. We list a series of proofs that differential learning is efficient in its information and computational resource requirements, whereas traditional probabilistic learning strategies are not. The proofs are illustrated by a simple example that lends itself to closed-form analysis. We conclude with an optical character recognition task for which three different types of differentially generated classifiers generalize significantly better than their probabilistically generated counterparts.

Understanding and responding the students in learning mathematics through the differentiated instruction

NASA Astrophysics Data System (ADS)

Hapsari, T.; Darhim; Dahlan, J. A.

2018-05-01

This research discusses the differentiated instruction, a mathematic learning which is as expected by the students in connection with the differentiated instruction itself, its implementation, and the students’ responses. This research employs a survey method which involves 62 students as the research respondents. The mathematics learning types required by the students and their responses to the differentiated instruction are examined through questionnaire and interview. The mathematics learning types in orderly required by the students, from the highest frequency cover the easily understood instructions, slowly/not rushing teaching, fun, not complicated, interspersed with humour, various question practices, not too serious, and conducive class atmosphere for the instructions. Implementing the differentiated instruction is not easy. The teacher should be able to constantly assess the students, s/he should have good knowledge of relevant materials and instructions, and properly prepare the instructions, although it is time-consuming. The differentiated instruction is implemented on the instructions of numerical pattern materials. The strategies implemented are flexible grouping, tiered assignment, and compacting. The students positively respond the differentiated learning instruction that they become more motivated and involved in the instruction.

Differential Learning as a Key Training Approach to Improve Creative and Tactical Behavior in Soccer.

PubMed

Santos, Sara; Coutinho, Diogo; Gonçalves, Bruno; Schöllhorn, Wolfgang; Sampaio, Jaime; Leite, Nuno

2018-03-01

The aim of this study was to identify the effects of a differential-learning program, embedded in small-sided games, on the creative and tactical behavior of youth soccer players. Forty players from under-13 (U13) and under-15 (U15) were allocated into control and experimental groups and were tested using a randomized pretest to posttest design using small-sided games situations. The experimental group participated in a 5-month differential-learning program embodied in small-sided games situations, while the control group participated in a typical small-sided games training program. In-game creativity was assessed through notational analyses of the creative components, and the players' positional data were used to compute tactical-derived variables. The findings suggested that differential learning facilitated the development of creative components, mainly concerning attempts (U13, small; U15, small), versatility (U13, moderate; U15, small), and originality (U13, unclear; U15, small) of players' actions. Likewise, the differential-learning approach provided a decrease in fails during the game in both experimental groups (moderate). Moreover, differential learning seemed to favor regularity in pitch-positioning behavior for the distance between players' dyads (U13, small; U15, small), the distance to the team target (U13, moderate; U15, small), and the distance to the opponent target (U13, moderate; U15, small). The differential-learning program stressed creative and positional behavior in both age groups with a distinct magnitude of effects, with the U13 players demonstrating higher improvements over the U15 players. Overall, these findings confirmed that the technical variability promoted by differential learning nurtures regularity of positioning behavior.

The Effects of Differential Learning and Traditional Learning Trainings on Technical Development of Football Players

ERIC Educational Resources Information Center

Bozkurt, Sinan

2018-01-01

There are several different methods of learning motor skills, like traditional (linear) and differential (nonlinear) learning training. The traditional motor learning approach proposes that learners improve a skill just by repeating it. According to the teaching principles, exercises are selected along continua from easy to hard and from simple to…

Scene segmentation by spike synchronization in reciprocally connected visual areas. I. Local effects of cortical feedback.

PubMed

Knoblauch, Andreas; Palm, Günther

2002-09-01

To investigate scene segmentation in the visual system we present a model of two reciprocally connected visual areas using spiking neurons. Area P corresponds to the orientation-selective subsystem of the primary visual cortex, while the central visual area C is modeled as associative memory representing stimulus objects according to Hebbian learning. Without feedback from area C, a single stimulus results in relatively slow and irregular activity, synchronized only for neighboring patches (slow state), while in the complete model activity is faster with an enlarged synchronization range (fast state). When presenting a superposition of several stimulus objects, scene segmentation happens on a time scale of hundreds of milliseconds by alternating epochs of the slow and fast states, where neurons representing the same object are simultaneously in the fast state. Correlation analysis reveals synchronization on different time scales as found in experiments (designated as tower, castle, and hill peaks). On the fast time scale (tower peaks, gamma frequency range), recordings from two sites coding either different or the same object lead to correlograms that are either flat or exhibit oscillatory modulations with a central peak. This is in agreement with experimental findings, whereas standard phase-coding models would predict shifted peaks in the case of different objects.

Enhancement of human cognitive performance using transcranial magnetic stimulation (TMS)

PubMed Central

Luber, Bruce; Lisanby, and Sarah H.

2014-01-01

Here we review the usefulness of transcranial magnetic stimulation (TMS) in modulating cortical networks in ways that might produce performance enhancements in healthy human subjects. To date over sixty studies have reported significant improvements in speed and accuracy in a variety of tasks involving perceptual, motor, and executive processing. Two basic categories of enhancement mechanisms are suggested by this literature: direct modulation of a cortical region or network that leads to more efficient processing, and addition-by-subtraction, which is disruption of processing which competes or distracts from task performance. Potential applications of TMS cognitive enhancement, including research into cortical function, rehabilitation therapy in neurological and psychiatric illness, and accelerated skill acquisition in healthy individuals are discussed, as are methods of optimizing the magnitude and duration of TMS-induced performance enhancement, such as improvement of targeting through further integration of brain imaging with TMS. One technique, combining multiple sessions of TMS with concurrent TMS/task performance to induce Hebbian-like learning, appears to be promising for prolonging enhancement effects. While further refinements in the application of TMS to cognitive enhancement can still be made, and questions remain regarding the mechanisms underlying the observed effects, this appears to be a fruitful area of investigation that may shed light on the basic mechanisms of cognitive function and their therapeutic modulation. PMID:23770409

Summary of Research on Online and Blended Learning Programs That Offer Differentiated Learning Options. REL 2017-228

ERIC Educational Resources Information Center

Brodersen, R. Marc; Melluzzo, Daniel

2017-01-01

This report summarizes the methodology, measures, and findings of research on the influence on student achievement outcomes of K-12 online and blended face-to-face and online learning programs that offer differentiated learning options. The report also describes the characteristics of the learning programs. Most of the examined programs used…

The Impact of Differentiation on Instructional Practices in the Elementary Classroom

ERIC Educational Resources Information Center

Thompson, Virginia

2009-01-01

Differentiation is an instructional approach that considers a student's learning readiness, learning style, and learning interest to meet academic needs. This curriculum innovation is grounded in the multiple intelligence theory of learning. It is also one method of meeting the expectations of the No Child Left Behind initiative. While the current…

Information-driven self-organization: the dynamical system approach to autonomous robot behavior.

PubMed

Ay, Nihat; Bernigau, Holger; Der, Ralf; Prokopenko, Mikhail

2012-09-01

In recent years, information theory has come into the focus of researchers interested in the sensorimotor dynamics of both robots and living beings. One root for these approaches is the idea that living beings are information processing systems and that the optimization of these processes should be an evolutionary advantage. Apart from these more fundamental questions, there is much interest recently in the question how a robot can be equipped with an internal drive for innovation or curiosity that may serve as a drive for an open-ended, self-determined development of the robot. The success of these approaches depends essentially on the choice of a convenient measure for the information. This article studies in some detail the use of the predictive information (PI), also called excess entropy or effective measure complexity, of the sensorimotor process. The PI of a process quantifies the total information of past experience that can be used for predicting future events. However, the application of information theoretic measures in robotics mostly is restricted to the case of a finite, discrete state-action space. This article aims at applying the PI in the dynamical systems approach to robot control. We study linear systems as a first step and derive exact results for the PI together with explicit learning rules for the parameters of the controller. Interestingly, these learning rules are of Hebbian nature and local in the sense that the synaptic update is given by the product of activities available directly at the pertinent synaptic ports. The general findings are exemplified by a number of case studies. In particular, in a two-dimensional system, designed at mimicking embodied systems with latent oscillatory locomotion patterns, it is shown that maximizing the PI means to recognize and amplify the latent modes of the robotic system. This and many other examples show that the learning rules derived from the maximum PI principle are a versatile tool for the self-organization of behavior in complex robotic systems.

Maximizing Student Success with Differentiated Learning

ERIC Educational Resources Information Center

Morgan, Hani

2014-01-01

Students tend to comprehend little and lose focus of classroom instruction when their teachers fail to use instructional strategies that match students' learning styles. Differentiated instruction can alleviate or eliminate this disengagement. This article describes a case involving a child having difficulty learning and shows how…

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

PubMed

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.

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

PubMed Central

Hannon, Brenda

2013-01-01

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. PMID:24347814

Three Steps Lead to Differentiation

ERIC Educational Resources Information Center

Bowgren, Linda; Sever, Kathryn

2010-01-01

Much has been written about the value, need, and complexity of differentiating learning within every classroom based on student readiness, motivation and interest, apparent skills, learning preferences or styles, and identified cognitive needs. Teachers are encouraged to look at differentiation for students not as a formula for teaching, but…

Arrhenius-kinetics evidence for quantum tunneling in microbial "social" decision rates.

PubMed

Clark, Kevin B

2010-11-01

Social-like bacteria, fungi and protozoa communicate chemical and behavioral signals to coordinate their specializations into an ordered group of individuals capable of fitter ecological performance. Examples of microbial "social" behaviors include sporulation and dispersion, kin recognition and nonclonal or paired reproduction. Paired reproduction by ciliates is believed to involve intra- and intermate selection through pheromone-stimulated "courting" rituals. Such social maneuvering minimizes survival-reproduction tradeoffs while sorting superior mates from inferior ones, lowering the vertical spread of deleterious genes in geographically constricted populations and possibly promoting advantageous genetic innovations. In a previous article, I reported findings that the heterotrich Spirostomum ambiguum can out-complete mating rivals in simulated social trials by learning behavioral heuristics which it then employs to store and select sets of altruistic and deceptive signaling strategies. Frequencies of strategy use typically follow Maxwell-Boltzmann (MB), Fermi-Dirac (FD) or Bose-Einstein (BE) statistical distributions. For ciliates most adept at social decision making, a brief classical MB computational phase drives signaling behavior into a later quantum BE computational phase that condenses or favors the selection of a single fittest strategy. Appearance of the network analogue of BE condensation coincides with Hebbian-like trial-and-error learning and is consistent with the idea that cells behave as heat engines, where loss of energy associated with specific cellular machinery critical for mating decisions effectively reduces the temperature of intracellular enzymes cohering into weak Fröhlich superposition. I extend these findings by showing the rates at which ciliates switch serial behavioral strategies agree with principles of chemical reactions exhibiting linear and nonlinear Arrhenius kinetics during respective classical and quantum computations. Nonlinear Arrhenius kinetics in ciliate decision making suggest transitions from one signaling strategy to another result from a computational analogue of quantum tunneling in social information processing.

Model, analysis, and evaluation of the effects of analog VLSI arithmetic on linear subspace-based image recognition.

PubMed

Carvajal, Gonzalo; Figueroa, Miguel

2014-07-01

Typical image recognition systems operate in two stages: feature extraction to reduce the dimensionality of the input space, and classification based on the extracted features. Analog Very Large Scale Integration (VLSI) is an attractive technology to achieve compact and low-power implementations of these computationally intensive tasks for portable embedded devices. However, device mismatch limits the resolution of the circuits fabricated with this technology. Traditional layout techniques to reduce the mismatch aim to increase the resolution at the transistor level, without considering the intended application. Relating mismatch parameters to specific effects in the application level would allow designers to apply focalized mismatch compensation techniques according to predefined performance/cost tradeoffs. This paper models, analyzes, and evaluates the effects of mismatched analog arithmetic in both feature extraction and classification circuits. For the feature extraction, we propose analog adaptive linear combiners with on-chip learning for both Least Mean Square (LMS) and Generalized Hebbian Algorithm (GHA). Using mathematical abstractions of analog circuits, we identify mismatch parameters that are naturally compensated during the learning process, and propose cost-effective guidelines to reduce the effect of the rest. For the classification, we derive analog models for the circuits necessary to implement Nearest Neighbor (NN) approach and Radial Basis Function (RBF) networks, and use them to emulate analog classifiers with standard databases of face and hand-writing digits. Formal analysis and experiments show how we can exploit adaptive structures and properties of the input space to compensate the effects of device mismatch at the application level, thus reducing the design overhead of traditional layout techniques. Results are also directly extensible to multiple application domains using linear subspace methods. Copyright © 2014 Elsevier Ltd. All rights reserved.

Equilibrium Propagation: Bridging the Gap between Energy-Based Models and Backpropagation

PubMed Central

Scellier, Benjamin; Bengio, Yoshua

2017-01-01

We introduce Equilibrium Propagation, a learning framework for energy-based models. It involves only one kind of neural computation, performed in both the first phase (when the prediction is made) and the second phase of training (after the target or prediction error is revealed). Although this algorithm computes the gradient of an objective function just like Backpropagation, it does not need a special computation or circuit for the second phase, where errors are implicitly propagated. Equilibrium Propagation shares similarities with Contrastive Hebbian Learning and Contrastive Divergence while solving the theoretical issues of both algorithms: our algorithm computes the gradient of a well-defined objective function. Because the objective function is defined in terms of local perturbations, the second phase of Equilibrium Propagation corresponds to only nudging the prediction (fixed point or stationary distribution) toward a configuration that reduces prediction error. In the case of a recurrent multi-layer supervised network, the output units are slightly nudged toward their target in the second phase, and the perturbation introduced at the output layer propagates backward in the hidden layers. We show that the signal “back-propagated” during this second phase corresponds to the propagation of error derivatives and encodes the gradient of the objective function, when the synaptic update corresponds to a standard form of spike-timing dependent plasticity. This work makes it more plausible that a mechanism similar to Backpropagation could be implemented by brains, since leaky integrator neural computation performs both inference and error back-propagation in our model. The only local difference between the two phases is whether synaptic changes are allowed or not. We also show experimentally that multi-layer recurrently connected networks with 1, 2, and 3 hidden layers can be trained by Equilibrium Propagation on the permutation-invariant MNIST task. PMID:28522969

Undergraduate Students' Perceptions of Collaborative Learning in a Differential Equations Mathematics Course

ERIC Educational Resources Information Center

Hajra, Sayonita Ghosh; Das, Ujjaini

2015-01-01

This paper uses collaborative learning strategies to examine students' perceptions in a differential equations mathematics course. Students' perceptions were analyzed using three collaborative learning strategies including collaborative activity, group-quiz and online discussion. The study results show that students identified both strengths and…

Teacher Perception on Differentiated Instruction and its Influence on Instructional Practice

ERIC Educational Resources Information Center

Burkett, Jacquelyn Ann

2013-01-01

Differentiated Instruction is an approach to teaching which meets the diverse academic needs of students by considering learner readiness, interest and learning style. The approach is grounded in the socio-cultural, multiple intelligence and learning style theories. In addition, differentiation is a research based method for meeting the…

Learning Styles in the Classroom: Educational Benefit or Planning Exercise?

ERIC Educational Resources Information Center

Allcock, Sarah J.; Hulme, Julie A.

2010-01-01

Differentiation of teaching is encouraged to accommodate student diversity. This study investigated whether using learning styles as a basis for differentiation improved A-level student performance, compared to differentiation on the basis of academic ability. Matched classes of A-level psychology students participated. In one class, learning…

Perceptions about Implementation of Differentiated Instruction

ERIC Educational Resources Information Center

Robinson, Lora; Maldonado, Nancy; Whaley, Jerita

2014-01-01

The absence of differentiated instruction in many classrooms stifles success for students who do not learn the same way as their peers. Providing teachers with the knowledge and tools to differentiate in their classrooms may increase test scores and help low achieving students find success, while expanding the learning growth of gifted and…

Learning to Differentiate: A Phenomenological Investigation of Middle School Teachers' Expertise Development

ERIC Educational Resources Information Center

Schroeder-Davis, Sondra Lyn

2009-01-01

Differentiation, an approach to designing and delivering appropriate curriculum and instruction to diverse learners, has been promoted as a means of facilitating student academic achievement. However, little is known about the learning processes by which some teachers develop expertise in differentiation, information important for maximizing…

Differentiated Instruction and the Need to Integrate Teaching and Practice

ERIC Educational Resources Information Center

Pham, Huong L.

2012-01-01

Differentiated instruction is becoming critical in higher education due to student diversity and background knowledge. Differentiated instruction does not mean matching teaching styles with learning styles as suggested by the learning styles theory. Findings in recent research studies have proved the lack of credible evidence for the utility of…

Differentiating Instruction in Physical Education: Personalization of Learning

ERIC Educational Resources Information Center

Colquitt, Gavin; Pritchard, Tony; Johnson, Christine; McCollum, Starla

2017-01-01

Differentiated instruction (DI) is a complex conceptual model and philosophy that is implemented in many traditional classroom settings. The primary focus of DI is to personalize the learning process by taking into account individual differences among students' varied levels of readiness, interest and learning profile. Varied assessments are used…

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

PubMed

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.

Differential Involvement of Hippocampal Calcineurin during Learning and Reversal Learning in a Y-Maze Task

ERIC Educational Resources Information Center

Havekes, Robbert; Nijholt, Ingrid M.; Luiten, Paul G. M.; Van der Zee, Eddy A.

2006-01-01

The regulation and function of the calcium-dependent phosphatase calcineurin (CaN, protein phosphatase 2B) in learning and memory remain unclear, although recent work indicates that CaN may play a differential role in training and reversal training. To gain more insight into the involvement of CaN in these two types of learning, hippocampal CaN…

Task Rotation: Strategies for Differentiating Activities and Assessments by Learning Style. A Strategic Teacher PLC Guide

ERIC Educational Resources Information Center

Silver, Harvey; Moirao, Daniel; Jackson, Joyce

2011-01-01

One of the hardest jobs in teaching is to differentiate learning activities and assessments to your students' learning styles. But you and your colleagues can learn how to do this together when each of you has this guide to the Task Rotation strategy from our ultimate guide to teaching strategies, "The Strategic Teacher". Use the guide in your…

Learning and Recall of Medical Treatment-Related Information in Older Adults Using the Differential Outcomes Procedure

PubMed Central

Plaza, Victoria; Molina, Michael; Fuentes, Luis J.; Estévez, Angeles F.

2018-01-01

It has recently been reported that the differential outcomes procedure (DOP) might be one of the therapeutical techniques focused at promoting autonomy in the elderly to deal with their medical issues. Molina et al. (2015) found that a group of healthy young adults improved their learning and long-term retention of six disorder/pill associations when each relationship to be learned was associated with a particular reinforcer (the differential outcomes condition) compared to when they were randomly administered (the non-differential outcomes condition). In the present study, we extend these findings to older adults who usually show difficulties to remember to take their medications as prescribed. Participants were asked to learn the association between three pills and the specific time at the day when they had to take each medication. Two memory tests were also performed 1 h and 1 week after completing the training phase. Results showed a faster learning of the task and long-term retention of the previously learned associations (pill/time of day) when differential outcomes were used. Furthermore, the older adults’ performance in the learning and memory phases did not differ from that of the younger adults in the DOP condition. These findings demonstrate that this procedure can help elderly people to ameliorate not only their learning, but also their long-term memory difficulties, suggesting the potential for the DOP to promote adherence to treatment in this population. PMID:29491846

Differentiated Learning: From Policy to Classroom

ERIC Educational Resources Information Center

Mills, Martin; Monk, Sue; Keddie, Amanda; Renshaw, Peter; Christie, Pam; Geelan, David; Gowlett, Christina

2014-01-01

This paper explores the impact of a Teaching and Learning Audit of all government schools in Queensland, Australia. This audit has a concern with the extent to which schools "differentiate classroom learning". We note that in England, since September 2012, one of the standards that teachers have been expected to demonstrate is an ability…

Imprinting and Recalling Cortical Ensembles

PubMed Central

Carrillo-Reid, Luis; Yang, Weijian; Bando, Yuki; Peterka, Darcy S.; Yuste, Rafael

2017-01-01

Neuronal ensembles are coactive groups of neurons that may represent emergent building blocks of neural circuits. They could be formed by Hebbian plasticity, whereby synapses between coactive neurons are strengthened. Here we report that repetitive activation with two-photon optogenetics of neuronal populations in visual cortex of awake mice generates artificially induced ensembles which recur spontaneously after being imprinted and do not disrupt preexistent ones. Moreover, imprinted ensembles can be recalled by single cell stimulation and remain coactive on consecutive days. Our results demonstrate the persistent reconfiguration of cortical circuits by two-photon optogenetics into neuronal ensembles that can perform pattern completion. PMID:27516599

Does Spike-Timing-Dependent Synaptic Plasticity Couple or Decouple Neurons Firing in Synchrony?

PubMed Central

Knoblauch, Andreas; Hauser, Florian; Gewaltig, Marc-Oliver; Körner, Edgar; Palm, Günther

2012-01-01

Spike synchronization is thought to have a constructive role for feature integration, attention, associative learning, and the formation of bidirectionally connected Hebbian cell assemblies. By contrast, theoretical studies on spike-timing-dependent plasticity (STDP) report an inherently decoupling influence of spike synchronization on synaptic connections of coactivated neurons. For example, bidirectional synaptic connections as found in cortical areas could be reproduced only by assuming realistic models of STDP and rate coding. We resolve this conflict by theoretical analysis and simulation of various simple and realistic STDP models that provide a more complete characterization of conditions when STDP leads to either coupling or decoupling of neurons firing in synchrony. In particular, we show that STDP consistently couples synchronized neurons if key model parameters are matched to physiological data: First, synaptic potentiation must be significantly stronger than synaptic depression for small (positive or negative) time lags between presynaptic and postsynaptic spikes. Second, spike synchronization must be sufficiently imprecise, for example, within a time window of 5–10 ms instead of 1 ms. Third, axonal propagation delays should not be much larger than dendritic delays. Under these assumptions synchronized neurons will be strongly coupled leading to a dominance of bidirectional synaptic connections even for simple STDP models and low mean firing rates at the level of spontaneous activity. PMID:22936909

How Do Professional Learning Communities Aid and Hamper Professional Learning of Beginning Teachers Related to Differentiated Instruction?

ERIC Educational Resources Information Center

De Neve, Debbie; Devos, Geert

2017-01-01

Research has shown that adequate support from the school environment is necessary to help beginning teachers in applying differentiated instruction (DI), but how schools can aid in this process remains unclear. This qualitative study explores how professional learning communities (PLCs), an indicator of a supportive school environment, can enhance…

Using Professional Learning Communities to Advance Preservice Teachers' Understanding of Differentiation within Writing Instruction

ERIC Educational Resources Information Center

Kuehl, Rachelle

2018-01-01

The purpose of this study was to explore the use of Professional Learning Communities (PLCs) in preservice teacher education as a tool for learning about differentiation within writing instruction. Using online dialogue journals, preservice teachers communicated with elementary students about a shared text and met in ongoing PLC groups to examine…

Improving Teaching Quality and Problem Solving Ability through Contextual Teaching and Learning in Differential Equations: A Lesson Study Approach

ERIC Educational Resources Information Center

Khotimah, Rita Pramujiyanti; Masduki

2016-01-01

Differential equations is a branch of mathematics which is closely related to mathematical modeling that arises in real-world problems. Problem solving ability is an essential component to solve contextual problem of differential equations properly. The purposes of this study are to describe contextual teaching and learning (CTL) model in…

Unpacking Socio-Economic Risks for Reading and Academic Self-Concept in Primary School: Differential Effects and the Role of the Preschool Home Learning Environment

ERIC Educational Resources Information Center

Crampton, Alexandria; Hall, James

2017-01-01

Background: Uncertainty remains concerning how children's reading and academic self-concept are related and how these are differentially affected by social disadvantage and home learning environments. Aims: To contrast the impacts of early socio-economic risks and preschool home learning environments upon British children's reading abilities and…

Dirac Sea and its Evolution

NASA Astrophysics Data System (ADS)

Volfson, Boris

2013-09-01

The hypothesis of transition from a chaotic Dirac Sea, via highly unstable positronium, into a Simhony Model of stable face-centered cubic lattice structure of electrons and positrons securely bound in vacuum space, is considered. 13.75 Billion years ago, the new lattice, which, unlike a Dirac Sea, is permeable by photons and phonons, made the Universe detectable. Many electrons and positrons ended up annihilating each other producing energy quanta and neutrino-antineutrino pairs. The weak force of the electron-positron crystal lattice, bombarded by the chirality-changing neutrinos, may have started capturing these neutrinos thus transforming from cubic crystals into a quasicrystal lattice. Unlike cubic crystal lattice, clusters of quasicrystals are "slippery" allowing the formation of centers of local torsion, where gravity condenses matter into galaxies, stars and planets. In the presence of quanta, in a quasicrystal lattice, the Majorana neutrinos' rotation flips to the opposite direction causing natural transformations in a category comprised of three components; two others being positron and electron. In other words, each particle-antiparticle pair "e-" and "e+", in an individual crystal unit, could become either a quasi- component "e- ve e+", or a quasi- component "e+ - ve e-". Five-to-six six billion years ago, a continuous stimulation of the quasicrystal aetherial lattice by the same, similar, or different, astronomical events, could have triggered Hebbian and anti-Hebbian learning processes. The Universe may have started writing script into its own aether in a code most appropriate for the quasicrystal aether "hardware": Eight three-dimensional "alphabet" characters, each corresponding to the individual quasi-crystal unit shape. They could be expressed as quantum Turing machine qubits, or, alternatively, in a binary code. The code numerals could contain terminal and nonterminal symbols of the Chomsky's hierarchy, wherein, the showers of quanta, forming the cosmic microwave background radiation, may re-script a quasi-component "e- ve e+" (in the binary code case, same as numeral "0") into a quasi-component "e+ -ve e-" (numeral "1"), or vice versa. According to both, the Chomsky"s logic, and the rules applicable to Majorana particles, terminals "e-" and "e+" cannot be changed using the rules of grammar, while nonterminals "ve" and "-ve" can. Under "quantum" showers, the quasi- unit cells re-shape, resulting in re-combination of the clusters that they form, with the affected pattern become same as, similar to, or different from, other pattern(s). The process of self-learning may have occurred as a natural response to various astronomical events and cosmic cataclysms: The same astronomical activity in two different areas resulted in the emission of the same energy forming the same secondary quasicrystal pattern. Different but similar astronomical activity resulted in the emission of a similar amount of energy forming a similar secondary quasicrystal pattern. Different astronomical activity resulted in the emission of a different amount of energy forming a different secondary quasicrystal pattern. Since quasicrystals conduct energy in one direction and don't conduct energy in the other, the control over quanta flows allows aether to scribe a script onto itself through changing its own quasi- patterns. The paper, as published below, is a lecture summary. The full text is published on website: www.borisvolfson.org

Generalized query-based active learning to identify differentially methylated regions in DNA.

PubMed

Haque, Md Muksitul; Holder, Lawrence B; Skinner, Michael K; Cook, Diane J

2013-01-01

Active learning is a supervised learning technique that reduces the number of examples required for building a successful classifier, because it can choose the data it learns from. This technique holds promise for many biological domains in which classified examples are expensive and time-consuming to obtain. Most traditional active learning methods ask very specific queries to the Oracle (e.g., a human expert) to label an unlabeled example. The example may consist of numerous features, many of which are irrelevant. Removing such features will create a shorter query with only relevant features, and it will be easier for the Oracle to answer. We propose a generalized query-based active learning (GQAL) approach that constructs generalized queries based on multiple instances. By constructing appropriately generalized queries, we can achieve higher accuracy compared to traditional active learning methods. We apply our active learning method to find differentially DNA methylated regions (DMRs). DMRs are DNA locations in the genome that are known to be involved in tissue differentiation, epigenetic regulation, and disease. We also apply our method on 13 other data sets and show that our method is better than another popular active learning technique.

Multiple Intelligences for Differentiated Learning

ERIC Educational Resources Information Center

Williams, R. Bruce

2007-01-01

There is an intricate literacy to Gardner's multiple intelligences theory that unlocks key entry points for differentiated learning. Using a well-articulated framework, rich with graphic representations, Williams provides a comprehensive discussion of multiple intelligences. He moves the teacher and students from curiosity, to confidence, to…

Investigating Pre-service Science Teachers' Developing Professional Knowledge Through the Lens of Differentiated Instruction

NASA Astrophysics Data System (ADS)

Goodnough, Karen

2010-03-01

In this study, the author implemented a problem-based learning (PBL) experience that allowed students in an advanced science methodology course to explore differentiated instruction. Through working systematically in small, collaborative groups, students explored the nature of differentiated instruction. The objective of the study was to examine pre-service teachers’ developing conceptions of differentiated instruction (DI) as a way to teach for diversity. The author adopted action research as a strategy to explore students’ perceptions of DI in the context of science teaching and learning. Several data collection methods and sources were adopted in the study, including student-generated products, student interviews, classroom observation, and journal writing. Outcomes report on students’ perceptions of both the potential and challenges associated with adopting a DI approach to science teaching and learning.

Museums, Adventures, Discovery Activities: Gifted Curriculum Intrinsically Differentiated.

ERIC Educational Resources Information Center

Haensly, Patricia A.

This paper discusses how museums, adventure programs, and discovery activities can become an intrinsically differentiated gifted curriculum for gifted learners. Museums and adventure programs are a forum for meaningful learning activities. The contextual characteristics of effectively designed settings for learning activities can, if the…

Content, Process, and Product: Modeling Differentiated Instruction

ERIC Educational Resources Information Center

Taylor, Barbara Kline

2015-01-01

Modeling differentiated instruction is one way to demonstrate how educators can incorporate instructional strategies to address students' needs, interests, and learning styles. This article discusses how secondary teacher candidates learn to focus on content--the "what" of instruction; process--the "how" of instruction;…

"Read a Hundred Times and the Meaning Will Appear..." Changes in Chinese University Students' Views of the Temporal Structure of Learning

ERIC Educational Resources Information Center

Marton, Ference; Wen, Qiufang; Wong, Kam Cheung

2005-01-01

It has been shown earlier that while some high school students (younger on the average) do not differentiate between memorization and understanding, others (older on the average) do so (Marton, Watkins and Tang, Learning and Instruction 7, 21-48, 1997). Those who do differentiate impose a sequential ordering on the two: "When you learn you…

Learning Rationales and Virtual Reality Technology in Education.

ERIC Educational Resources Information Center

Chiou, Guey-Fa

1995-01-01

Defines and describes virtual reality technology and differentiates between virtual learning environment, learning material, and learning tools. Links learning rationales to virtual reality technology to pave conceptual foundations for application of virtual reality technology education. Constructivism, case-based learning, problem-based learning,…

Information Compression, Multiple Alignment, and the Representation and Processing of Knowledge in the Brain.

PubMed

Wolff, J Gerard

2016-01-01

The SP theory of intelligence , with its realization in the SP computer model , aims to simplify and integrate observations and concepts across artificial intelligence, mainstream computing, mathematics, and human perception and cognition, with information compression as a unifying theme. This paper describes how abstract structures and processes in the theory may be realized in terms of neurons, their interconnections, and the transmission of signals between neurons. This part of the SP theory- SP-neural -is a tentative and partial model for the representation and processing of knowledge in the brain. Empirical support for the SP theory-outlined in the paper-provides indirect support for SP-neural. In the abstract part of the SP theory (SP-abstract), all kinds of knowledge are represented with patterns , where a pattern is an array of atomic symbols in one or two dimensions. In SP-neural, the concept of a "pattern" is realized as an array of neurons called a pattern assembly , similar to Hebb's concept of a "cell assembly" but with important differences. Central to the processing of information in SP-abstract is information compression via the matching and unification of patterns (ICMUP) and, more specifically, information compression via the powerful concept of multiple alignment , borrowed and adapted from bioinformatics. Processes such as pattern recognition, reasoning and problem solving are achieved via the building of multiple alignments, while unsupervised learning is achieved by creating patterns from sensory information and also by creating patterns from multiple alignments in which there is a partial match between one pattern and another. It is envisaged that, in SP-neural, short-lived neural structures equivalent to multiple alignments will be created via an inter-play of excitatory and inhibitory neural signals. It is also envisaged that unsupervised learning will be achieved by the creation of pattern assemblies from sensory information and from the neural equivalents of multiple alignments, much as in the non-neural SP theory-and significantly different from the "Hebbian" kinds of learning which are widely used in the kinds of artificial neural network that are popular in computer science. The paper discusses several associated issues, with relevant empirical evidence.

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

PubMed

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, which is, in part, determined by neuroanatomical structure. As the neurocomputational model provides a mechanistic explanation of how memory-related "disembodied" neuronal activity emerges in "embodied" APCs, it may be key to solving aspects of the embodiment debate and eventually to a better understanding of cognitive brain functions. Copyright © 2014 The Authors. Published by Elsevier Ltd.. All rights reserved.

Imprinting and recalling cortical ensembles.

PubMed

Carrillo-Reid, Luis; Yang, Weijian; Bando, Yuki; Peterka, Darcy S; Yuste, Rafael

2016-08-12

Neuronal ensembles are coactive groups of neurons that may represent building blocks of cortical circuits. These ensembles could be formed by Hebbian plasticity, whereby synapses between coactive neurons are strengthened. Here we report that repetitive activation with two-photon optogenetics of neuronal populations from ensembles in the visual cortex of awake mice builds neuronal ensembles that recur spontaneously after being imprinted and do not disrupt preexisting ones. Moreover, imprinted ensembles can be recalled by single- cell stimulation and remain coactive on consecutive days. Our results demonstrate the persistent reconfiguration of cortical circuits by two-photon optogenetics into neuronal ensembles that can perform pattern completion. Copyright © 2016, American Association for the Advancement of Science.

Gifted/Talented Curriculum Bulletin No. 2: Differentiating.

ERIC Educational Resources Information Center

Goldman, Nancy T.

The document provides a collection of curriculum ideas and suggestions for teaching gifted and talented students, with emphasis on self-directed learning and differentiation of the curriculum. Section I outlines steps for developinq a self-directed learning environment and includes resource materials lists and sample evaluation sheets. Among…

Relationship between learning environment characteristics and academic engagement.

PubMed

Opdenakker, Marie-Christine; Minnaert, Alexander

2011-08-01

The relationship between learning environment characteristics and academic engagement of 777 Grade 6 children located in 41 learning environments was explored. Questionnaires were used to tap learning environment perceptions of children, their academic engagement, and their ethnic-cultural background. The basis of the learning environment questionnaire was the International System for Teacher Observation and Feedback (ISTOF). Factor analysis indicated three factors: the teacher as a helpful and good instructor (having good instructional skills, clear instruction), the teacher as promoter of active learning and differentiation, and the teacher as manager and organizer of classroom activities. Multilevel analysis indicated that about 12% of the differences in engagement between children was related to the learning environment. All the mentioned learning environment characteristics mattered, but the teacher as a helpful, good instructor was most important followed by the teacher as promoter of active learning and differentiation.

Hidden physics models: Machine learning of nonlinear partial differential equations

NASA Astrophysics Data System (ADS)

Raissi, Maziar; Karniadakis, George Em

2018-03-01

While there is currently a lot of enthusiasm about "big data", useful data is usually "small" and expensive to acquire. In this paper, we present a new paradigm of learning partial differential equations from small data. In particular, we introduce hidden physics models, which are essentially data-efficient learning machines capable of leveraging the underlying laws of physics, expressed by time dependent and nonlinear partial differential equations, to extract patterns from high-dimensional data generated from experiments. The proposed methodology may be applied to the problem of learning, system identification, or data-driven discovery of partial differential equations. Our framework relies on Gaussian processes, a powerful tool for probabilistic inference over functions, that enables us to strike a balance between model complexity and data fitting. The effectiveness of the proposed approach is demonstrated through a variety of canonical problems, spanning a number of scientific domains, including the Navier-Stokes, Schrödinger, Kuramoto-Sivashinsky, and time dependent linear fractional equations. The methodology provides a promising new direction for harnessing the long-standing developments of classical methods in applied mathematics and mathematical physics to design learning machines with the ability to operate in complex domains without requiring large quantities of data.

Changing Students' Approaches to Study through Classroom Exercises.

ERIC Educational Resources Information Center

Gibbs, Graham

1983-01-01

Differentiates among learning to study, teaching study skills, and helping people learn how to learn. Concentrates on learning to learn--a developmental process in which people's conceptions of learning evolve--and describes strategies for helping students learn how to learn to change their approaches to study tasks. (JOW)

Differential associative training enhances olfactory acuity in Drosophila melanogaster.

PubMed

Barth, Jonas; Dipt, Shubham; Pech, Ulrike; Hermann, Moritz; Riemensperger, Thomas; Fiala, André

2014-01-29

Training can improve the ability to discriminate between similar, confusable stimuli, including odors. One possibility of enhancing behaviorally expressed discrimination (i.e., sensory acuity) relies on differential associative learning, during which animals are forced to detect the differences between similar stimuli. Drosophila represents a key model organism for analyzing neuronal mechanisms underlying both odor processing and olfactory learning. However, the ability of flies to enhance fine discrimination between similar odors through differential associative learning has not been analyzed in detail. We performed associative conditioning experiments using chemically similar odorants that we show to evoke overlapping neuronal activity in the fly's antennal lobes and highly correlated activity in mushroom body lobes. We compared the animals' performance in discriminating between these odors after subjecting them to one of two types of training: either absolute conditioning, in which only one odor is reinforced, or differential conditioning, in which one odor is reinforced and a second odor is explicitly not reinforced. First, we show that differential conditioning decreases behavioral generalization of similar odorants in a choice situation. Second, we demonstrate that this learned enhancement in olfactory acuity relies on both conditioned excitation and conditioned inhibition. Third, inhibitory local interneurons in the antennal lobes are shown to be required for behavioral fine discrimination between the two similar odors. Fourth, differential, but not absolute, training causes decorrelation of odor representations in the mushroom body. In conclusion, differential training with similar odors ultimately induces a behaviorally expressed contrast enhancement between the two similar stimuli that facilitates fine discrimination.

Differential Endocannabinoid Regulation of Extinction in Appetitive and Aversive Barnes Maze Tasks

ERIC Educational Resources Information Center

Harloe, John P.; Thorpe, Andrew J.; Lichtman, Aron H.

2008-01-01

CB[subscript 1] receptor-compromised animals show profound deficits in extinguishing learned behavior from aversive conditioning tasks, but display normal extinction learning in appetitive operant tasks. However, it is difficult to discern whether the differential involvement of the endogenous cannabinoid system on extinction results from the…

Differentiation for Gifted Learners: Going beyond the Basics

ERIC Educational Resources Information Center

Heacox, Diane; Cash, Richard M.

2014-01-01

Within a group of advanced learners, the variety of abilities, talents, interests, and learning styles can be formidable. For the first time, this book connects the unique learning differences among gifted students to the specific teaching methods used to tailor their educational experiences. Differentiated instruction for gifted and talented…

Getting a Puff: A Social Learning Test of Adolescents Smoking

ERIC Educational Resources Information Center

Monroe, Jacquelyn

2004-01-01

This article is a description of a study that sought to examine the applicability of Ronald Akers' social learning theory. According to Akers' theory, differential associations with smokers, differential reinforcements for smoking, favorable definitions of smoking and the availability of role models (imitation) offer an explanation as to why…

Gene Network Construction from Microarray Data Identifies a Key Network Module and Several Candidate Hub Genes in Age-Associated Spatial Learning Impairment

PubMed Central

Uddin, Raihan; Singh, Shiva M.

2017-01-01

As humans age many suffer from a decrease in normal brain functions including spatial learning impairments. This study aimed to better understand the molecular mechanisms in age-associated spatial learning impairment (ASLI). We used a mathematical modeling approach implemented in Weighted Gene Co-expression Network Analysis (WGCNA) to create and compare gene network models of young (learning unimpaired) and aged (predominantly learning impaired) brains from a set of exploratory datasets in rats in the context of ASLI. The major goal was to overcome some of the limitations previously observed in the traditional meta- and pathway analysis using these data, and identify novel ASLI related genes and their networks based on co-expression relationship of genes. This analysis identified a set of network modules in the young, each of which is highly enriched with genes functioning in broad but distinct GO functional categories or biological pathways. Interestingly, the analysis pointed to a single module that was highly enriched with genes functioning in “learning and memory” related functions and pathways. Subsequent differential network analysis of this “learning and memory” module in the aged (predominantly learning impaired) rats compared to the young learning unimpaired rats allowed us to identify a set of novel ASLI candidate hub genes. Some of these genes show significant repeatability in networks generated from independent young and aged validation datasets. These hub genes are highly co-expressed with other genes in the network, which not only show differential expression but also differential co-expression and differential connectivity across age and learning impairment. The known function of these hub genes indicate that they play key roles in critical pathways, including kinase and phosphatase signaling, in functions related to various ion channels, and in maintaining neuronal integrity relating to synaptic plasticity and memory formation. Taken together, they provide a new insight and generate new hypotheses into the molecular mechanisms responsible for age associated learning impairment, including spatial learning. PMID:29066959

Gene Network Construction from Microarray Data Identifies a Key Network Module and Several Candidate Hub Genes in Age-Associated Spatial Learning Impairment.

PubMed

Uddin, Raihan; Singh, Shiva M

2017-01-01

As humans age many suffer from a decrease in normal brain functions including spatial learning impairments. This study aimed to better understand the molecular mechanisms in age-associated spatial learning impairment (ASLI). We used a mathematical modeling approach implemented in Weighted Gene Co-expression Network Analysis (WGCNA) to create and compare gene network models of young (learning unimpaired) and aged (predominantly learning impaired) brains from a set of exploratory datasets in rats in the context of ASLI. The major goal was to overcome some of the limitations previously observed in the traditional meta- and pathway analysis using these data, and identify novel ASLI related genes and their networks based on co-expression relationship of genes. This analysis identified a set of network modules in the young, each of which is highly enriched with genes functioning in broad but distinct GO functional categories or biological pathways. Interestingly, the analysis pointed to a single module that was highly enriched with genes functioning in "learning and memory" related functions and pathways. Subsequent differential network analysis of this "learning and memory" module in the aged (predominantly learning impaired) rats compared to the young learning unimpaired rats allowed us to identify a set of novel ASLI candidate hub genes. Some of these genes show significant repeatability in networks generated from independent young and aged validation datasets. These hub genes are highly co-expressed with other genes in the network, which not only show differential expression but also differential co-expression and differential connectivity across age and learning impairment. The known function of these hub genes indicate that they play key roles in critical pathways, including kinase and phosphatase signaling, in functions related to various ion channels, and in maintaining neuronal integrity relating to synaptic plasticity and memory formation. Taken together, they provide a new insight and generate new hypotheses into the molecular mechanisms responsible for age associated learning impairment, including spatial learning.

Differentiation of teaching and learning mathematics: an action research study in tertiary education

NASA Astrophysics Data System (ADS)

Konstantinou-Katzi, Panagiota; Tsolaki, Eleni; Meletiou-Mavrotheris, Maria; Koutselini, Mary

2013-04-01

Diversity and differentiation within our classrooms, at all levels of education, is nowadays a fact. It has been one of the biggest challenges for educators to respond to the needs of all students in such a mixed-ability classroom. Teachers' inability to deal with students with different levels of readiness in a different way leads to school failure and all the negative outcomes that come with it. Differentiation of teaching and learning helps addressing this problem by respecting the different levels that exist in the classroom, and by responding to the needs of each learner. This article presents an action research study where a team of mathematics instructors and an expert in curriculum development developed and implemented a differentiated instruction learning environment in a first-year engineering calculus class at a university in Cyprus. This study provides evidence that differentiated instruction has a positive effect on student engagement and motivation and improves students' understanding of difficult calculus concepts.

The amount of decrease of the background white noise intensity as a cue for differentiation training.

PubMed

Zieliński, K

1981-01-01

The course of differentiation learning, using the conditioned emotional response (CER) method, was investigated in two groups of 16 rats. The discriminative stimuli consisted of decreases in the 80 dB background white noise to either 70 dB or 60 dB. Differentiation learning was more efficient with the larger decrease of background noise intensity as the CS(+) and the smaller decrease as the CS(-) than vice versa.

Small-scale anomaly detection in panoramic imaging using neural models of low-level vision

NASA Astrophysics Data System (ADS)

Casey, Matthew C.; Hickman, Duncan L.; Pavlou, Athanasios; Sadler, James R. E.

2011-06-01

Our understanding of sensory processing in animals has reached the stage where we can exploit neurobiological principles in commercial systems. In human vision, one brain structure that offers insight into how we might detect anomalies in real-time imaging is the superior colliculus (SC). The SC is a small structure that rapidly orients our eyes to a movement, sound or touch that it detects, even when the stimulus may be on a small-scale; think of a camouflaged movement or the rustle of leaves. This automatic orientation allows us to prioritize the use of our eyes to raise awareness of a potential threat, such as a predator approaching stealthily. In this paper we describe the application of a neural network model of the SC to the detection of anomalies in panoramic imaging. The neural approach consists of a mosaic of topographic maps that are each trained using competitive Hebbian learning to rapidly detect image features of a pre-defined shape and scale. What makes this approach interesting is the ability of the competition between neurons to automatically filter noise, yet with the capability of generalizing the desired shape and scale. We will present the results of this technique applied to the real-time detection of obscured targets in visible-band panoramic CCTV images. Using background subtraction to highlight potential movement, the technique is able to correctly identify targets which span as little as 3 pixels wide while filtering small-scale noise.

Teaching differential diagnosis to nurse practitioner students in a distance program.

PubMed

Colella, Christine L; Beery, Theresa A

2014-08-01

An interactive case study (ICS) is a novel way to enhance the teaching of differential diagnosis to distance learning nurse practitioner students. Distance education renders the use of many teaching strategies commonly used with face-to-face students difficult, if not impossible. To meet this new pedagogical dilemma and to provide excellence in education, the ICS was developed. Kolb's theory of experiential learning supported efforts to follow the utilization of the ICS. This study sought to determine whether learning outcomes for the distance learning students were equivalent to those of on-campus students who engaged in a live-patient encounter. Accuracy of differential diagnosis lists generated by onsite and online students was compared. Equivalency testing assessed clinical, rather than only statistical, significance in data from 291 students. The ICS responses from the distance learning and onsite students differed by 4.9%, which was within the a priori equivalence estimate of 10%. Narrative data supported the findings. Copyright 2014, SLACK Incorporated.

Peer-Assisted Learning.

ERIC Educational Resources Information Center

Topping, Keith, Ed.; Ehly, Stewart, Ed.

Peer-Assisted Learning (PAL) involves students consciously assisting others to learn, and in so doing, learning more effectively themselves. PAL encompasses peer tutoring, peer modeling, peer education, peer counseling, peer monitoring, and peer assessment, which are differentiated from other more general "cooperative learning" methods.…

Differentiating Instruction for Students with Learning Disabilities: Best Teaching Practices for General and Special Educators.

ERIC Educational Resources Information Center

Bender, William N.

This book provides classroom-proven strategies designed to empower the teacher to target instructional modifications to the content, process, and products for students with learning disabilities in the general and special education classrooms. Chapter 1 presents the concept of differentiated instruction and how that concept translates into…

Differentiated Teaching & Learning in Heterogeneous Classrooms: Strategies for Meeting the Needs of All Students.

ERIC Educational Resources Information Center

Kronberg, Robi; York-Barr, Jennifer; Arnold, Kathy; Gombos, Shawn; Truex, Sharon; Vallejo, Barb; Stevenson, Jane

This guide provides conceptual as well as practical information for meeting the needs of all learners in heterogeneous classrooms. The first six sections discuss the growing heterogeneity in today's classrooms, the rationale for differentiated teaching and learning, the changing roles of teachers and students, the importance of creating classroom…

Differentiated Instruction for Students with Disabilities: Using DI in the Music Classroom

ERIC Educational Resources Information Center

Darrow, Alice-Ann

2015-01-01

Students come to the music classroom with different educational readiness, learning styles, abilities, and preferences. In addition to these learner differences, classrooms in the United States are becoming more linguistically and culturally diverse each year. Differentiated instruction is an approach to teaching and learning that allows for these…

Introduction of the Notion of Differential Equations by Modelling Based Teaching

ERIC Educational Resources Information Center

Budinski, Natalija; Takaci, Djurdjica

2011-01-01

This paper proposes modelling based learning as a tool for learning and teaching mathematics. The example of modelling real world problems leading to the exponential function as the solution of differential equations is described, as well as the observations about students' activities during the process. The students were acquainted with the…

Differentiated Instructional Strategies to Accommodate Students with Varying Needs and Learning Styles

ERIC Educational Resources Information Center

Gentry, Ruben; Sallie, April P.; Sanders, Carrie A.

2013-01-01

Students enter classrooms with different abilities, learning styles, and personalities. Educators are mandated to see that all students meet the standards of their district and state. Through the use of differentiated instructional strategies, teachers can meet the varying needs of all students and help them to meet and exceed the established…

Building Measures of Instructional Differentiation from Teacher Checklists

ERIC Educational Resources Information Center

Williams, Ryan; Swanlund, Andrew; Miller, Shazia; Konstantopoulos, Spyros; van der Ploeg, Arie

2012-01-01

Differentiated instruction is commonly believed to be critical to improving the quality and efficiency of teachers' instructional repertoires (Fischer & Rose, 2001; Tomlinson, 2004). Tomlinson (2000) describes differentiation in four domains: content, process, product, and learning environment. Content differentiation involves varying…

Arrhenius-kinetics evidence for quantum tunneling in microbial “social” decision rates

PubMed Central

2010-01-01

Social-like bacteria, fungi and protozoa communicate chemical and behavioral signals to coordinate their specializations into an ordered group of individuals capable of fitter ecological performance. Examples of microbial “social” behaviors include sporulation and dispersion, kin recognition and nonclonal or paired reproduction. Paired reproduction by ciliates is believed to involve intra- and intermate selection through pheromone-stimulated “courting” rituals. Such social maneuvering minimizes survival-reproduction tradeoffs while sorting superior mates from inferior ones, lowering the vertical spread of deleterious genes in geographically constricted populations and possibly promoting advantageous genetic innovations. In a previous article, I reported findings that the heterotrich Spirostomum ambiguum can out-complete mating rivals in simulated social trials by learning behavioral heuristics which it then employs to store and select sets of altruistic and deceptive signaling strategies. Frequencies of strategy use typically follow Maxwell-Boltzmann (MB), Fermi-Dirac (FD) or Bose-Einstein (BE) statistical distributions. For ciliates most adept at social decision making, a brief classical MB computational phase drives signaling behavior into a later quantum BE computational phase that condenses or favors the selection of a single fittest strategy. Appearance of the network analogue of BE condensation coincides with Hebbian-like trial-and-error learning and is consistent with the idea that cells behave as heat engines, where loss of energy associated with specific cellular machinery critical for mating decisions effectively reduces the temperature of intracellular enzymes cohering into weak Fröhlich superposition. I extend these findings by showing the rates at which ciliates switch serial behavioral strategies agree with principles of chemical reactions exhibiting linear and nonlinear Arrhenius kinetics during respective classical and quantum computations. Nonlinear Arrhenius kinetics in ciliate decision making suggest transitions from one signaling strategy to another result from a computational analogue of quantum tunneling in social information processing. PMID:21331234

The Future of Personalized Learning for Students with Disabilities

ERIC Educational Resources Information Center

Worthen, Maria

2016-01-01

Personalized learning models can give each student differentiated learning experiences based on their needs, interests, and strengths, including students with disabilities. Personalized learning can pinpoint specific gaps in student learning, identify where a student is on his or her learning pathway, and provide the appropriate interventions to…

Pedagogical Management of Learning Activities of Students in the Electronic Educational Environment of the University: A Differentiated Approach

ERIC Educational Resources Information Center

Toktarova, Vera Ivanovna

2015-01-01

The present study considers issues related to the planning and implementation of the system of pedagogical management of learning activities of students in the context of modern electronic educational environment of the higher education institution. As a methodological basis considered a differentiated approach based on flexible individual…

Improving Student Academic Success through Differentiated Teaching within a Specialized Learning Resource Center

ERIC Educational Resources Information Center

Alexander, Roy E.

2013-01-01

The purpose of this quantitative study was to improve the academic success of students through the utilization of differentiated teaching within a specialized Learning Resource Center. The research study site is a private coeducational K-11 school located in Northern Georgia. The school provides motivated and disciplined students with a rigorous…

Differential Regulation of Brain-Derived Neurotrophic Factor Transcripts during the Consolidation of Fear Learning

ERIC Educational Resources Information Center

Ressler, Kerry J.; Rattiner, Lisa M.; Davis, Michael

2004-01-01

Brain-derived neurotrophic factor (BDNF) has been implicated as a molecular mediator of learning and memory. The BDNF gene contains four differentially regulated promoters that generate four distinct mRNA transcripts, each containing a unique noncoding 5[prime]-exon and a common 3[prime]-coding exon. This study describes novel evidence for the…

Initiating Differentiated Instruction in General Education Classrooms with Inclusion Learning Support Students: A Multiple Case Study

ERIC Educational Resources Information Center

Berbaum, K. A.

2009-01-01

The purpose of this multiple case study was to describe and evaluate the experience of 5 general education teachers from a northeastern urban middle school as they integrated differentiated instruction with students who have specific learning disabilities. Educators are challenged to implement instruction that engages students with specific…

Using the Personalized System of Instruction to Differentiate Instruction in Fitness

ERIC Educational Resources Information Center

Viness, Stephanie; Colquitt, Gavin; Pritchard, Tony; Johnson, Christine

2017-01-01

The purpose of this article is to provide an overview of how PE teachers can personalize learning to meet a variety of student needs. Differentiated instruction (DI) is a term frequently used in classroom-based learning to describe a method of personalization for individual students. The term can also describe a theoretical model for teaching and…

Improved Pedagogy for Linear Differential Equations by Reconsidering How We Measure the Size of Solutions

ERIC Educational Resources Information Center

Tisdell, Christopher C.

2017-01-01

For over 50 years, the learning of teaching of "a priori" bounds on solutions to linear differential equations has involved a Euclidean approach to measuring the size of a solution. While the Euclidean approach to "a priori" bounds on solutions is somewhat manageable in the learning and teaching of the proofs involving…

Teacher's Differentiated Instruction Practices and Implementation Challenges for Learning Disabilities in Jordan

ERIC Educational Resources Information Center

Siam, Karam; Al-Natour, Mayada

2016-01-01

This study aimed to identify the differentiated instruction practices used by Jordanian teachers and the challenges they faced when teaching students with learning disabilities in Amman. The sample of the study consisted of 194 teachers. It followed a mixed method design and consisted of two parts. First, a quantitative analysis of a questionnaire…

Organisational Learning and the Organisational Life Cycle: The Differential Aspects of an Integrated Relationship in SMEs

ERIC Educational Resources Information Center

Tam, Steven; Gray, David E.

2016-01-01

Purpose: The purpose of this study is to relate the practice of organisational learning in small- and medium-sized enterprises (SMEs) to the organisational life cycle (OLC), contextualising the differential aspects of an integrated relationship between them. Design/methodology/approach: It is a mixed-method study with two consecutive phases. In…

Do Learning Difficulties Differentiate Elementary Teachers' Attributional Patterns for Students' Academic Failure? A Comparison between Greek Regular and Special Education Teachers

ERIC Educational Resources Information Center

Vlachou, Anastasia; Eleftheriadou, Dimitra; Metallidou, Panayiota

2014-01-01

This study aimed to (a) investigate whether the presence of learning difficulties (LD) in primary school children differentiates Greek teachers' attributional patterns, emotional responses, expectations and evaluative feedback for the children's academic failures and (b) to examine possible differences between regular and special education…

Generalization of Fear to Respiratory Sensations.

PubMed

Schroijen, Mathias; Pappens, Meike; Schruers, Koen; Van den Bergh, Omer; Vervliet, Bram; Van Diest, Ilse

2015-09-01

Interoceptive fear conditioning (IFC), fear generalization and a lack of safety learning have all been hypothesized to play a role in the pathogenesis of panic disorder, but have never been examined in a single paradigm. The present study aims to investigate whether healthy participants (N=43) can learn both fear and safety to an interoceptive sensation, and whether such learning generalizes to other, similar sensations. Two intensities of inspiratory breathing impairment (induced by two pressure threshold loads of 6 and 25 cm H2O) served as interoceptive conditional stimuli (CSs) in a differential conditioning paradigm. An inspiratory occlusion was used as the unconditioned stimulus (US). Generalization was tested 24h after conditioning, using four generalization stimuli with intensities in-between CS+ and CS- (GSs: 8-10.5-14-18.5 cm H2O). Measures included US-expectancy, startle blink EMG responses, electrodermal activity and respiration. Perceptual discrimination of interoceptive CSs and GSs was explored with a discrimination task prior to acquisition and after generalization. Results indicate that differential fear learning was established for US-expectancy ratings. The group with a low intensity CS+ and a high intensity CS- showed the typical pattern of differential fear responding and a similarity-based generalization gradient. In contrast, the high intensity CS+ and low intensity CS- group showed impaired differential learning and complete generalization of fear. Our findings suggest that interoceptive fear learning and generalization are modulated by stimulus intensity and that the occurrence of discriminatory learning is closely related to fear generalization. Copyright © 2015. Published by Elsevier Ltd.

Equalization of Synaptic Efficacy by Synchronous Neural Activity

NASA Astrophysics Data System (ADS)

Cho, Myoung Won; Choi, M. Y.

2007-11-01

It is commonly believed that spike timings of a postsynaptic neuron tend to follow those of the presynaptic neuron. Such orthodromic firing may, however, cause a conflict with the functional integrity of complex neuronal networks due to asymmetric temporal Hebbian plasticity. We argue that reversed spike timing in a synapse is a typical phenomenon in the cortex, which has a stabilizing effect on the neuronal network structure. We further demonstrate how the firing causality in a synapse is perturbed by synchronous neural activity and how the equilibrium property of spike-timing dependent plasticity is determined principally by the degree of synchronization. Remarkably, even noise-induced activity and synchrony of neurons can result in equalization of synaptic efficacy.

Traditional Instruction of Differential Equations and Conceptual Learning

ERIC Educational Resources Information Center

Arslan, Selahattin

2010-01-01

Procedural and conceptual learning are two types of learning, related to two types of knowledge, which are often referred to in mathematics education. Procedural learning involves only memorizing operations with no understanding of underlying meanings. Conceptual learning involves understanding and interpreting concepts and the relations between…

Learning Consequences of Mobile-Computing Technologies: Differential Impacts on Integrative Learning and Skill-Focused Learning

ERIC Educational Resources Information Center

Kumi, Richard; Reychav, Iris; Sabherwal, Rajiv

2016-01-01

Many educational institutions are integrating mobile-computing technologies (MCT) into the classroom to improve learning outcomes. There is also a growing interest in research to understand how MCT influence learning outcomes. The diversity of results in prior research indicates that computer-mediated learning has different effects on various…

The Differential Outcomes Procedure Enhances Adherence to Treatment: A Simulated Study with Healthy Adults

PubMed Central

Molina, Michael; Plaza, Victoria; Fuentes, Luis J.; Estévez, Angeles F.

2015-01-01

Memory for medical recommendations is a prerequisite for good adherence to treatment, and therefore to ameliorate the negative effects of the disease, a problem that mainly affects people with memory deficits. We conducted a simulated study to test the utility of a procedure (the differential outcomes procedure, DOP) that may improve adherence to treatment by increasing the patient’s learning and retention of medical recommendations regarding medication. The DOP requires the structure of a conditional discriminative learning task in which correct choice responses to specific stimulus–stimulus associations are reinforced with a particular reinforcer or outcome. In two experiments, participants had to learn and retain in their memory the pills that were associated with particular disorders. To assess whether the DOP improved long-term retention of the learned disorder/pill associations, participants were asked to perform two recognition memory tests, 1 h and 1 week after completing the learning phase. The results showed that compared with the standard non-differential outcomes procedure, the DOP produced better learning and long-term retention of the previously learned associations. These findings suggest that the DOP can be used as a useful complementary technique in intervention programs targeted at increasing adherence to clinical recommendations. PMID:26913010

Analyzing a Service-Learning Experience Using a Social Justice Lens

ERIC Educational Resources Information Center

Tinkler, Barri; Hannah, C. Lynne; Tinkler, Alan; Miller, Elizabeth

2014-01-01

This mixed methods study explores a service-learning experience embedded in a social foundations course in a teacher education program. The authors differentiate learning outcomes for social justice and charity service-learning, and utilize this framework to examine whether the service-learning experience fosters a social justice perspective. The…

The Differential Impact of Pre-College and Self-Regulatory Factors on Academic Achievement of University Students with and without Learning Disabilities.

ERIC Educational Resources Information Center

Ruban, Lilia; McCoach, D. Betsy; Nora, Amaury

Even though research on academic self-regulation has proliferated in recent years, no studies have investigated the question of whether the perceived usefulness and the use of standard self-regulated learning strategies and compensation strategies provide a differential prediction of academic achievement for college students with and without…

Improving Junior High Schools' Critical Thinking Skills Based on Test Three Different Models of Learning

ERIC Educational Resources Information Center

Fuad, Nur Miftahul; Zubaidah, Siti; Mahanal, Susriyati; Suarsini, Endang

2017-01-01

The aims of this study were (1) to find out the differences in critical thinking skills among students who were given three different learning models: differentiated science inquiry combined with mind map, differentiated science inquiry model, and conventional model, (2) to find out the differences of critical thinking skills among male and female…

Differentiated Instruction: An Analysis of Approaches and Applications

ERIC Educational Resources Information Center

Smeeton, Gina

2016-01-01

The purpose of this study was to find common perceptions and practices of differentiated instruction by fifth grade teachers. Currently, there is little research on the perspectives of teachers who are learning about and utilizing differentiated instruction in the classroom. This study reviews teacher perceptions about differentiated instruction,…

Deferential Differentiation: What Types of Differentiation Do Students Want?

ERIC Educational Resources Information Center

Kanevsky, Lannie

2011-01-01

Deferential differentiation occurs when the curriculum modification process defers to students' preferred ways of learning rather than relying on teachers' judgments. The preferences of 416 students identified as gifted (grades 3-8) for features of differentiated curriculum recommended for gifted students were compared with those of 230 students…

Differential Fee: From Conceptualisation to Implementation.

ERIC Educational Resources Information Center

Ng, Todd C. Y.; Wong, Andrew L. S.

A differential fee policy was adopted at the Open Learning Institute (OLI) of Hong Kong after a process of conceptualization, planning, proposal, approval and implementation. Under the differential fee policy different tuition rates were charged for different courses. The differential fee policy was conceptualized at OLI based on economics,…

Learning by Doing.

ERIC Educational Resources Information Center

Schettler, Joel

2002-01-01

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

Extending Social Learning Theory to Explain Victimization Among Gang and Ex-Gang Offenders.

PubMed

Gagnon, Analisa

2018-03-01

This study is among the first to extend and test social learning theory's ability to understand property and violent victimization. It specifically tests whether aspects of definitions, differential reinforcement, and differential association/modeling can explain the three types of victimization of gang members: actual experience, perception of likelihood, and fear. The sample consists of over 300 male and female gang members incarcerated in jails throughout Florida. The results show that all three types of victimization can be explained by the three aspects of social learning theory.

Assessing Students' Development in Learning Approaches According to Initial Learning Profiles: A Person-Oriented Perspective

ERIC Educational Resources Information Center

Vanthournout, Gert; Coertjens, Liesje; Gijbels, David; Donche, Vincent; Van Petegem, Peter

2013-01-01

Research regarding the development of students' learning approaches have at times reported unexpected or lack of expected changes. The current study explores the idea of differential developments in learning approaches according to students' initial learning profiles as a possible explanation for these outcomes. A learning profile is conceived as…

Efficient differentially private learning improves drug sensitivity prediction.

PubMed

Honkela, Antti; Das, Mrinal; Nieminen, Arttu; Dikmen, Onur; Kaski, Samuel

2018-02-06

Users of a personalised recommendation system face a dilemma: recommendations can be improved by learning from data, but only if other users are willing to share their private information. Good personalised predictions are vitally important in precision medicine, but genomic information on which the predictions are based is also particularly sensitive, as it directly identifies the patients and hence cannot easily be anonymised. Differential privacy has emerged as a potentially promising solution: privacy is considered sufficient if presence of individual patients cannot be distinguished. However, differentially private learning with current methods does not improve predictions with feasible data sizes and dimensionalities. We show that useful predictors can be learned under powerful differential privacy guarantees, and even from moderately-sized data sets, by demonstrating significant improvements in the accuracy of private drug sensitivity prediction with a new robust private regression method. Our method matches the predictive accuracy of the state-of-the-art non-private lasso regression using only 4x more samples under relatively strong differential privacy guarantees. Good performance with limited data is achieved by limiting the sharing of private information by decreasing the dimensionality and by projecting outliers to fit tighter bounds, therefore needing to add less noise for equal privacy. The proposed differentially private regression method combines theoretical appeal and asymptotic efficiency with good prediction accuracy even with moderate-sized data. As already the simple-to-implement method shows promise on the challenging genomic data, we anticipate rapid progress towards practical applications in many fields. This article was reviewed by Zoltan Gaspari and David Kreil.

Preferred-Actual Learning Environment "Spaces" and Earth Science Outcomes in Taiwan

ERIC Educational Resources Information Center

Chang, Chun-Yen; Hsiao, Chien-Hua; Barufaldi, James P.

2006-01-01

This study examines the possibilities of differential impacts on students' earth science learning outcomes between different preferred-actual learning environment spaces by using a newly developed ESCLEI (Earth Science Classroom Learning Environment Instrument). The instrument emphasizes three simultaneously important classroom components:…

Differential Effects of Music and Video Gaming During Breaks on Auditory and Visual Learning.

PubMed

Liu, Shuyan; Kuschpel, Maxim S; Schad, Daniel J; Heinz, Andreas; Rapp, Michael A

2015-11-01

The interruption of learning processes by breaks filled with diverse activities is common in everyday life. This study investigated the effects of active computer gaming and passive relaxation (rest and music) breaks on auditory versus visual memory performance. Young adults were exposed to breaks involving (a) open eyes resting, (b) listening to music, and (c) playing a video game, immediately after memorizing auditory versus visual stimuli. To assess learning performance, words were recalled directly after the break (an 8:30 minute delay) and were recalled and recognized again after 7 days. Based on linear mixed-effects modeling, it was found that playing the Angry Birds video game during a short learning break impaired long-term retrieval in auditory learning but enhanced long-term retrieval in visual learning compared with the music and rest conditions. These differential effects of video games on visual versus auditory learning suggest specific interference of common break activities on learning.

Exploring a "Space" for Emergent Learning to Occur: Encouraging Creativity and Innovation in the Workplace

ERIC Educational Resources Information Center

Armson, Genevieve

2009-01-01

This research set out to explore perceptions about the concept of an emergent learning space within private organisations, as the current literature on learning does not adequately differentiate between organised learning and emergent learning. The research objectives explored the existence of, and perceived level of organisational encouragement…

Emotional Mood as a Context for Learning and Recall.

ERIC Educational Resources Information Center

Bower, Gordon H.; And Others

1978-01-01

In experiments where hypnotized subjects learned one word list while happy or sad, retention proved to be surprisingly independent of the congruence of learning and testing moods. Learning mood provided a helpful retrieval cue and differentiating context only where subjects learned two word lists, one while happy, one while sad. (EJS)

Abraham Lincoln and Harry Potter: Children's Differentiation between Historical and Fantasy Characters

ERIC Educational Resources Information Center

Corriveau, Kathleen H.; Kim, Angie L.; Schwalen, Courtney E.; Harris, Paul L.

2009-01-01

Based on the testimony of others, children learn about a variety of figures that they never meet. We ask when and how they are able to differentiate between the historical figures that they learn about (e.g., Abraham Lincoln) and fantasy characters (e.g., Harry Potter). Experiment 1 showed that both younger (3- and 4-year-olds) and older children…

How To Differentiate Instruction. Adapted from Chapter 3 of "Powerful Lesson Planning Models: The Art of 1,000 Decisions." A Skylight Guide. Grades K-12.

ERIC Educational Resources Information Center

Skowron, Janice

This booklet helps teachers understand how to increase student motivation and interest and how to design lively, creative, and interactive lessons that will enrich learning. Chapter 1, "The Differences That Impact Learning," describes the differentiated classroom and provides teachers with an understanding of the many variations of how…

Convergent Validity of the Mullen Scales of Early Learning and the Differential Ability Scales in Children with Autism Spectrum Disorders

ERIC Educational Resources Information Center

Bishop, Somer L.; Guthrie, Whitney; Coffing, Mia; Lord, Catherine

2011-01-01

Despite widespread use of the Mullen Scales of Early Learning (MSEL; E. M. Mullen, 1995) as a cognitive test for children with autism spectrum disorders and other developmental disabilities, the instrument has not been independently validated for use in these populations. Convergent validity of the MSEL and the Differential Ability Scales (DAS; C.…

The Effect of Differentiating Instruction Using Multiple Intelligences on Achievement in and Attitudes towards Science in Middle School Students with Learning Disabilities

ERIC Educational Resources Information Center

Gomaa, Omema Mostafa Kamel

2014-01-01

This study investigated the effect of using differentiated instruction using multiple intelligences on achievement in and attitudes towards science in middle school students with learning disabilities. A total of 61 students identified with LD participated. The sample was randomly divided into two groups; experimental (n= 31 boys )and control (n=…

Hares, Tortoises and the Social Construction of the Pupil: Differentiated Learning in French and English Primary Schools

ERIC Educational Resources Information Center

Raveaud, Maroussia

2005-01-01

This article examines differentiation by task as it is used and perceived in French and English primary schools. It highlights the influence of national context on teaching and learning. The study rests on classroom observations in 12 Key Stage 1 classes located in socially disadvantaged areas. The first section sets the mission of schooling in a…

Differential Learning as a Key Training Approach to Improve Creative and Tactical Behavior in Soccer

ERIC Educational Resources Information Center

Santos, Sara; Coutinho, Diogo; Gonçalves, Bruno; Schöllhorn, Wolfgang; Sampaio, Jaime; Leite, Nuno

2018-01-01

Purpose: The aim of this study was to identify the effects of a differential-learning program, embedded in small-sided games, on the creative and tactical behavior of youth soccer players. Forty players from under-13 (U13) and under-15 (U15) were allocated into control and experimental groups and were tested using a randomized pretest to posttest…

Autonomous development and learning in artificial intelligence and robotics: Scaling up deep learning to human-like learning.

PubMed

Oudeyer, Pierre-Yves

2017-01-01

Autonomous lifelong development and learning are fundamental capabilities of humans, differentiating them from current deep learning systems. However, other branches of artificial intelligence have designed crucial ingredients towards autonomous learning: curiosity and intrinsic motivation, social learning and natural interaction with peers, and embodiment. These mechanisms guide exploration and autonomous choice of goals, and integrating them with deep learning opens stimulating perspectives.

Rethinking Differentiation--Using Teachers' Time Most Effectively

ERIC Educational Resources Information Center

Marshall, Kim

2016-01-01

The goals of differentiation are laudable, but in recent years, many question whether it is really possible for a teacher to tailor instruction for 20 to 30 different students and whether it's desirable to differentiate by learning styles. Differentiation is just one factor in effective instruction. Supervisors who walk into a classroom looking…

Examining Differential Math Performance by Gender and Opportunity to Learn

ERIC Educational Resources Information Center

Albano, Anthony D.; Rodriguez, Michael C.

2013-01-01

Although a substantial amount of research has been conducted on differential item functioning in testing, studies have focused on detecting differential item functioning rather than on explaining how or why it may occur. Some recent work has explored sources of differential functioning using explanatory and multilevel item response models. This…

Differentiating Instruction in the Primary Grades with the Big6[TM

ERIC Educational Resources Information Center

Jansen, Barbara A.

2009-01-01

Tomlinson defines differentiated instruction as "attempts to meet students where they are in the learning process and move them along as quickly and as far as possible in the context of a mixed-ability classroom." Successful differentiation equals student engagement and understanding. Instruction can be differentiated in several ways: by student…

Informed Conjecturing of Solutions for Differential Equations in a Modeling Context

ERIC Educational Resources Information Center

Winkel, Brian

2015-01-01

We examine two differential equations. (i) first-order exponential growth or decay; and (ii) second order, linear, constant coefficient differential equations, and show the advantage of learning differential equations in a modeling context for informed conjectures of their solution. We follow with a discussion of the complete analysis afforded by…

An analysis of mathematical connection ability based on student learning style on visualization auditory kinesthetic (VAK) learning model with self-assessment

NASA Astrophysics Data System (ADS)

Apipah, S.; Kartono; Isnarto

2018-03-01

This research aims to analyze the quality of VAK learning with self-assessment toward the ability of mathematical connection performed by students and to analyze students’ mathematical connection ability based on learning styles in VAK learning model with self-assessment. This research applies mixed method type with concurrent embedded design. The subject of this research consists of VIII grade students from State Junior High School 9 Semarang who apply visual learning style, auditory learning style, and kinesthetic learning style. The data of learning style is collected by using questionnaires, the data of mathematical connection ability is collected by performing tests, and the data of self-assessment is collected by using assessment sheets. The quality of learning is qualitatively valued from planning stage, realization stage, and valuation stage. The result of mathematical connection ability test is analyzed quantitatively by mean test, conducting completeness test, mean differentiation test, and mean proportional differentiation test. The result of the research shows that VAK learning model results in well-qualified learning regarded from qualitative and quantitative sides. Students with visual learning style perform the highest mathematical connection ability, students with kinesthetic learning style perform average mathematical connection ability, and students with auditory learning style perform the lowest mathematical connection ability.

Aversive learning of odor-heat associations in ants.

PubMed

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.

[Multilingualism and child psychiatry: on differential diagnoses of language disorder, specific learning disorder, and selective mutism].

PubMed

Tamiya, Satoshi

2014-01-01

Multilingualism poses unique psychiatric problems, especially in the field of child psychiatry. The author discusses several linguistic and transcultural issues in relation to Language Disorder, Specific Learning Disorder and Selective Mutism. Linguistic characteristics of multiple language development, including so-called profile effects and code-switching, need to be understood for differential diagnosis. It is also emphasized that Language Disorder in a bilingual person is not different or worse than that in a monolingual person. Second language proficiency, cultural background and transfer from the first language all need to be considered in an evaluation for Specific Learning Disorder. Selective Mutism has to be differentiated from the silent period observed in the normal successive bilingual development. The author concludes the review by remarking on some caveats around methods of language evaluation in a multilingual person.

Sample Complexity Bounds for Differentially Private Learning

PubMed Central

Chaudhuri, Kamalika; Hsu, Daniel

2013-01-01

This work studies the problem of privacy-preserving classification – namely, learning a classifier from sensitive data while preserving the privacy of individuals in the training set. In particular, the learning algorithm is required in this problem to guarantee differential privacy, a very strong notion of privacy that has gained significant attention in recent years. A natural question to ask is: what is the sample requirement of a learning algorithm that guarantees a certain level of privacy and accuracy? We address this question in the context of learning with infinite hypothesis classes when the data is drawn from a continuous distribution. We first show that even for very simple hypothesis classes, any algorithm that uses a finite number of examples and guarantees differential privacy must fail to return an accurate classifier for at least some unlabeled data distributions. This result is unlike the case with either finite hypothesis classes or discrete data domains, in which distribution-free private learning is possible, as previously shown by Kasiviswanathan et al. (2008). We then consider two approaches to differentially private learning that get around this lower bound. The first approach is to use prior knowledge about the unlabeled data distribution in the form of a reference distribution chosen independently of the sensitive data. Given such a reference , we provide an upper bound on the sample requirement that depends (among other things) on a measure of closeness between and the unlabeled data distribution. Our upper bound applies to the non-realizable as well as the realizable case. The second approach is to relax the privacy requirement, by requiring only label-privacy – namely, that the only labels (and not the unlabeled parts of the examples) be considered sensitive information. An upper bound on the sample requirement of learning with label privacy was shown by Chaudhuri et al. (2006); in this work, we show a lower bound. PMID:25285183

Learning-dependent plasticity in human auditory cortex during appetitive operant conditioning.

PubMed

Puschmann, Sebastian; Brechmann, André; Thiel, Christiane M

2013-11-01

Animal experiments provide evidence that learning to associate an auditory stimulus with a reward causes representational changes in auditory cortex. However, most studies did not investigate the temporal formation of learning-dependent plasticity during the task but rather compared auditory cortex receptive fields before and after conditioning. We here present a functional magnetic resonance imaging study on learning-related plasticity in the human auditory cortex during operant appetitive conditioning. Participants had to learn to associate a specific category of frequency-modulated tones with a reward. Only participants who learned this association developed learning-dependent plasticity in left auditory cortex over the course of the experiment. No differential responses to reward predicting and nonreward predicting tones were found in auditory cortex in nonlearners. In addition, learners showed similar learning-induced differential responses to reward-predicting and nonreward-predicting tones in the ventral tegmental area and the nucleus accumbens, two core regions of the dopaminergic neurotransmitter system. This may indicate a dopaminergic influence on the formation of learning-dependent plasticity in auditory cortex, as it has been suggested by previous animal studies. Copyright © 2012 Wiley Periodicals, Inc.

Reconcilable Differences: Standards-based Teaching and Differentiation.

ERIC Educational Resources Information Center

Tomlinson, Carol Ann

2000-01-01

There is no contradiction between effective standards-based instruction and differentiation. Curriculum tells teachers what to teach; differentiation tells how. Teachers can challenge all learners by providing standards-based materials and tasks calling for varied difficulty levels, scaffolding, instructional styles, and learning times. (MLH)

Increasing Participation through Differentiation

ERIC Educational Resources Information Center

Christenson, Bridget; Wager, Anita A.

2012-01-01

One of the many challenges teachers face is trying to differentiate instruction so all students have equal opportunities to participate, learn, and engage. To provide guidelines for differentiated instruction in mathematics, staff from the Madison Metropolitan School District in Wisconsin created a pedagogical framework for teaching called…

Active and Passive Spatial Learning in Human Navigation: Acquisition of Graph Knowledge

ERIC Educational Resources Information Center

Chrastil, Elizabeth R.; Warren, William H.

2015-01-01

It is known that active exploration of a new environment leads to better spatial learning than does passive visual exposure. We ask whether specific components of active learning differentially contribute to particular forms of spatial knowledge--the "exploration-specific learning hypothesis". Previously, we found that idiothetic…

Learning Profiles: The Learning Crisis Is Not (Mostly) about Enrollment

ERIC Educational Resources Information Center

Sandefur, Justin; Pritchett, Lant; Beatty, Amanda

2016-01-01

The differential patterns of grade progression have direct implications for the calculation of learning profiles. Researchers measure learning in primary school using survey data on reading and math skills of a nationally representative, population-based sample of children in India, Pakistan, Kenya, Tanzania, and Uganda. Research demonstrates that…

A Contextualized, Differential Sequence Mining Method to Derive Students' Learning Behavior Patterns

ERIC Educational Resources Information Center

Kinnebrew, John S.; Loretz, Kirk M.; Biswas, Gautam

2013-01-01

Computer-based learning environments can produce a wealth of data on student learning interactions. This paper presents an exploratory data mining methodology for assessing and comparing students' learning behaviors from these interaction traces. The core algorithm employs a novel combination of sequence mining techniques to identify deferentially…

Communicative Task-Based Learning: What Does It Resolve?

ERIC Educational Resources Information Center

Bruton, Anthony

A discussion of communicative task-based second language learning looks at what differentiates a number of related classroom approaches: (1) two orientations, one focusing on learning-to-communicate and the other on communicating-to-learn; (2) emphasis on input, reception, and analysis in contrast to emphasis on communicative tasks; (3)…

To Learn or Not To Learn: Understanding Student Motivation.

ERIC Educational Resources Information Center

Lumsden, Linda S.

1995-01-01

A multitude of factors affect the attitudes and behaviors that students bring to the learning situation. This document discusses some motivation-related terms and concepts. It then examines several factors that affect students' basic beliefs about and attitudes toward learning. The first section differentiates between the following terms: ability…

Self-Directed Learning in the Process of Work: Conceptual Considerations--Empirical Evidences.

ERIC Educational Resources Information Center

Straka, Gerald A.; Schaefer, Cornelia

With reference to the literature on adult self-directed learning, a model termed the "Two-Shell Model of Motivated Self-Directed Learning" was formulated that differentiates sociohistorical environmental conditions, internal conditions, and activities related to four concepts (interest, learning strategies, control, and evaluation). The…

Why Is Externally-Facilitated Regulated Learning More Effective than Self-Regulated Learning with Hypermedia?

ERIC Educational Resources Information Center

Azevedo, Roger; Moos, Daniel C.; Greene, Jeffrey A.; Winters, Fielding I.; Cromley, Jennifer G.

2008-01-01

We examined how self-regulated learning (SRL) and externally-facilitated self-regulated learning (ERL) differentially affected adolescents' learning about the circulatory system while using hypermedia. A total of 128 middle-school and high school students with little prior knowledge of the topic were randomly assigned to either the SRL or ERL…

Scene segmentation by spike synchronization in reciprocally connected visual areas. II. Global assemblies and synchronization on larger space and time scales.

PubMed

Knoblauch, Andreas; Palm, Günther

2002-09-01

We present further simulation results of the model of two reciprocally connected visual areas proposed in the first paper [Knoblauch and Palm (2002) Biol Cybern 87:151-167]. One area corresponds to the orientation-selective subsystem of the primary visual cortex, the other is modeled as an associative memory representing stimulus objects according to Hebbian learning. We examine the scene-segmentation capability of our model on larger time and space scales, and relate it to experimental findings. Scene segmentation is achieved by attention switching on a time-scale longer than the gamma range. We find that the time-scale can vary depending on habituation parameters in the range of tens to hundreds of milliseconds. The switching process can be related to findings concerning attention and biased competition, and we reproduce experimental poststimulus time histograms (PSTHs) of single neurons under different stimulus and attentional conditions. In a larger variant the model exhibits traveling waves of activity on both slow and fast time-scales, with properties similar to those found in experiments. An apparent weakness of our standard model is the tendency to produce anti-phase correlations for fast activity from the two areas. Increasing the inter-areal delays in our model produces alternations of in-phase and anti-phase oscillations. The experimentally observed in-phase correlations can most naturally be obtained by the involvement of both fast and slow inter-areal connections; e.g., by two axon populations corresponding to fast-conducting myelinated and slow-conducting unmyelinated axons.

Neural Encoding and Integration of Learned Probabilistic Sequences in Avian Sensory-Motor Circuitry

PubMed Central

Brainard, Michael S.

2013-01-01

Many complex behaviors, such as human speech and birdsong, reflect a set of categorical actions that can be flexibly organized into variable sequences. However, little is known about how the brain encodes the probabilities of such sequences. Behavioral sequences are typically characterized by the probability of transitioning from a given action to any subsequent action (which we term “divergence probability”). In contrast, we hypothesized that neural circuits might encode the probability of transitioning to a given action from any preceding action (which we term “convergence probability”). The convergence probability of repeatedly experienced sequences could naturally become encoded by Hebbian plasticity operating on the patterns of neural activity associated with those sequences. To determine whether convergence probability is encoded in the nervous system, we investigated how auditory-motor neurons in vocal premotor nucleus HVC of songbirds encode different probabilistic characterizations of produced syllable sequences. We recorded responses to auditory playback of pseudorandomly sequenced syllables from the bird's repertoire, and found that variations in responses to a given syllable could be explained by a positive linear dependence on the convergence probability of preceding sequences. Furthermore, convergence probability accounted for more response variation than other probabilistic characterizations, including divergence probability. Finally, we found that responses integrated over >7–10 syllables (∼700–1000 ms) with the sign, gain, and temporal extent of integration depending on convergence probability. Our results demonstrate that convergence probability is encoded in sensory-motor circuitry of the song-system, and suggest that encoding of convergence probability is a general feature of sensory-motor circuits. PMID:24198363

The role of differentiation and standards-based grading in the science learning of struggling and advanced learners in a detracked high school honors biology classroom

NASA Astrophysics Data System (ADS)

MacDonald, Michelina Ruth Carter

The accountability movement in education resulting from the passage of The No Child Left Behind Act of 2001 has brought to light the disparities that exist in student achievement in the United States which play out along racial and socioeconomic lines. Three educational practices hold promise for reducing this achievement gap: differentiated instruction, standards-based assessment, and elimination of academic tracking. The purpose of this practitioner research study was to examine the ways that differentiation and standards-based assessment can support struggling learners and challenge advanced learners in a detracked, honors biology classroom. To gain insight into the role that differentiation and standards-based assessment played in supporting struggling and advanced learners, I used practitioner research to examine the development and implementation of a differentiated, standards-based instructional unit around the conceptual topic of protein synthesis. I collected multiple data pieces for 10 students in the study: two advanced learners, four struggling learners, and four strong learners who struggled in biology. Data analyzed included formative, self-, and summative assessment results; student artifacts; informal and formal student interviews; and, a practitioner reflection journal chronicling critical incidents and actions taken during the development and implementation of this unit and notes from peer debriefing during and following the unit's implementation. As I analyzed the data collected, my four findings fell into two overarching categories related to student grouping. My first three findings reflect what I learned about homogeneous grouping: (1) Pre-assessment based on unit outcomes is not useful for determining groups for tiered instruction; (2) Decisions about differentiation and grouping for differentiation must be made in the act of teaching using formative assessment results; and, (3) Flexible grouping structures are effective for both struggling and advanced learners. My fourth finding reflects what I learned about heterogeneous grouping: (4) Heterogeneously grouping students for argumentation through engagement in science inquiry serves both to reinforce proficiency of learning goals for struggling learners and simultaneously push all learners towards advanced proficiency. These findings indicate how planning for and implementing a differentiated, standards-based instructional unit can support the learning needs of both struggling and advanced learners in a detracked, honors biology classroom.

The implementation of inclusive education in South Africa: Reflections arising from a workshop for teachers and therapists to introduce Universal Design for Learning.

PubMed

Dalton, Elizabeth M; Mckenzie, Judith Anne; Kahonde, Callista

2012-01-01

South Africa has adopted an inclusive education policy in order to address barriers to learning in the education system. However, the implementation of this policy is hampered by the lack of teachers' skills and knowledge in differentiating the curriculum to address a wide range of learning needs. In this paper we provided a background to inclusive education policy in South Africa and a brief exposition of an instructional design approach, Universal Design for Learning (UDL) that addresses a wide range of learning needs in a single classroom. We reported on a workshop conducted with teachers and therapists in South Africa as a first attempt to introduce UDL in this context. Knowledge of UDL was judged to be appropriate and useful by the course participants in the South African context as a strategy for curriculum differentiation in inclusive classrooms. Furthermore, knowledge of the UDL framework facilitates dialogue between teachers and therapists and provides a relatively simple and comprehensive approach for curriculum differentiation. We therefore conclude that there is potential for this approach that can be expanded through further teacher training.

The implementation of inclusive education in South Africa: Reflections arising from a workshop for teachers and therapists to introduce Universal Design for Learning

PubMed Central

Dalton, Elizabeth M.; Kahonde, Callista

2012-01-01

South Africa has adopted an inclusive education policy in order to address barriers to learning in the education system. However, the implementation of this policy is hampered by the lack of teachers’ skills and knowledge in differentiating the curriculum to address a wide range of learning needs. In this paper we provided a background to inclusive education policy in South Africa and a brief exposition of an instructional design approach, Universal Design for Learning (UDL) that addresses a wide range of learning needs in a single classroom. We reported on a workshop conducted with teachers and therapists in South Africa as a first attempt to introduce UDL in this context. Knowledge of UDL was judged to be appropriate and useful by the course participants in the South African context as a strategy for curriculum differentiation in inclusive classrooms. Furthermore, knowledge of the UDL framework facilitates dialogue between teachers and therapists and provides a relatively simple and comprehensive approach for curriculum differentiation. We therefore conclude that there is potential for this approach that can be expanded through further teacher training. PMID:28729974

Deep Learning Representation from Electroencephalography of Early-Stage Creutzfeldt-Jakob Disease and Features for Differentiation from Rapidly Progressive Dementia.

PubMed

Morabito, Francesco Carlo; Campolo, Maurizio; Mammone, Nadia; Versaci, Mario; Franceschetti, Silvana; Tagliavini, Fabrizio; Sofia, Vito; Fatuzzo, Daniela; Gambardella, Antonio; Labate, Angelo; Mumoli, Laura; Tripodi, Giovanbattista Gaspare; Gasparini, Sara; Cianci, Vittoria; Sueri, Chiara; Ferlazzo, Edoardo; Aguglia, Umberto

2017-03-01

A novel technique of quantitative EEG for differentiating patients with early-stage Creutzfeldt-Jakob disease (CJD) from other forms of rapidly progressive dementia (RPD) is proposed. The discrimination is based on the extraction of suitable features from the time-frequency representation of the EEG signals through continuous wavelet transform (CWT). An average measure of complexity of the EEG signal obtained by permutation entropy (PE) is also included. The dimensionality of the feature space is reduced through a multilayer processing system based on the recently emerged deep learning (DL) concept. The DL processor includes a stacked auto-encoder, trained by unsupervised learning techniques, and a classifier whose parameters are determined in a supervised way by associating the known category labels to the reduced vector of high-level features generated by the previous processing blocks. The supervised learning step is carried out by using either support vector machines (SVM) or multilayer neural networks (MLP-NN). A subset of EEG from patients suffering from Alzheimer's Disease (AD) and healthy controls (HC) is considered for differentiating CJD patients. When fine-tuning the parameters of the global processing system by a supervised learning procedure, the proposed system is able to achieve an average accuracy of 89%, an average sensitivity of 92%, and an average specificity of 89% in differentiating CJD from RPD. Similar results are obtained for CJD versus AD and CJD versus HC.

Differentiated Instruction: Understanding the Personal Factors and Organizational Conditions that Facilitate Differentiated Instruction in Elementary Mathematics Classrooms

ERIC Educational Resources Information Center

Abbati, Diana Guglielmo

2012-01-01

Differentiated instruction is a widely held practice used by teachers to provide diverse learners with complex learning opportunities in the area of mathematics. Research on differentiated instruction shows a multitude of factors that support high quality instruction in mixed-ability elementary classrooms. These factors include small-class size,…

Perceived facial expressions of emotion as motivational incentives: evidence from a differential implicit learning paradigm.

PubMed

Schultheiss, Oliver C; Pang, Joyce S; Torges, Cynthia M; Wirth, Michelle M; Treynor, Wendy; Derryberry, Douglas

2005-03-01

Participants (N = 216) were administered a differential implicit learning task during which they were trained and tested on 3 maximally distinct 2nd-order visuomotor sequences, with sequence color serving as discriminative stimulus. During training, 1 sequence each was followed by an emotional face, a neutral face, and no face, using backward masking. Emotion (joy, surprise, anger), face gender, and exposure duration (12 ms, 209 ms) were varied between participants; implicit motives were assessed with a picture-story exercise. For power-motivated individuals, low-dominance facial expressions enhanced and high-dominance expressions impaired learning. For affiliation-motivated individuals, learning was impaired in the context of hostile faces. These findings did not depend on explicit learning of fixed sequences or on awareness of sequence-face contingencies. Copyright 2005 APA, all rights reserved.

Statistical Mechanics of Node-perturbation Learning with Noisy Baseline

NASA Astrophysics Data System (ADS)

Hara, Kazuyuki; Katahira, Kentaro; Okada, Masato

2017-02-01

Node-perturbation learning is a type of statistical gradient descent algorithm that can be applied to problems where the objective function is not explicitly formulated, including reinforcement learning. It estimates the gradient of an objective function by using the change in the object function in response to the perturbation. The value of the objective function for an unperturbed output is called a baseline. Cho et al. proposed node-perturbation learning with a noisy baseline. In this paper, we report on building the statistical mechanics of Cho's model and on deriving coupled differential equations of order parameters that depict learning dynamics. We also show how to derive the generalization error by solving the differential equations of order parameters. On the basis of the results, we show that Cho's results are also apply in general cases and show some general performances of Cho's model.

Doping Among Professional Athletes in Iran: A Test of Akers's Social Learning Theory.

PubMed

Kabiri, Saeed; Cochran, John K; Stewart, Bernadette J; Sharepour, Mahmoud; Rahmati, Mohammad Mahdi; Shadmanfaat, Syede Massomeh

2018-04-01

The use of performance-enhancing drugs (PED) is common among Iranian professional athletes. As this phenomenon is a social problem, the main purpose of this research is to explain why athletes engage in "doping" activity, using social learning theory. For this purpose, a sample of 589 professional athletes from Rasht, Iran, was used to test assumptions related to social learning theory. The results showed that there are positive and significant relationships between the components of social learning theory (differential association, differential reinforcement, imitation, and definitions) and doping behavior (past, present, and future use of PED). The structural modeling analysis indicated that the components of social learning theory accounts for 36% of the variance in past doping behavior, 35% of the variance in current doping behavior, and 32% of the variance in future use of PED.

School Leadership Actions to Support Differentiated Instruction

ERIC Educational Resources Information Center

Byars, Jennifer Pallon

2011-01-01

Schools are required to meet a range of students' learning needs and effective school leadership is needed for the implementation of pedagogical practices responsive to the challenges of increasing student diversity and academic accountability. Literature on differentiated instruction and its constituent elements suggests differentiation results…

Perceptions of Missouri Elementary Principals to Lead Differentiated Instruction Initiatives

ERIC Educational Resources Information Center

Eftink, Adrian

2014-01-01

The following document represents a Problem Based Learning Project (PBL) around the central theme of differentiated instruction leadership. "As demonstrated through literature the emerging problem was elementary school principals lack the necessary understanding and needed preparation in differentiated instruction (DI) leadership to support…

Some Aspects of Mathematical Model of Collaborative Learning

ERIC Educational Resources Information Center

Nakamura, Yasuyuki; Yasutake, Koichi; Yamakawa, Osamu

2012-01-01

There are some mathematical learning models of collaborative learning, with which we can learn how students obtain knowledge and we expect to design effective education. We put together those models and classify into three categories; model by differential equations, so-called Ising spin and a stochastic process equation. Some of the models do not…

Using Dual Eye-Tracking Measures to Differentiate between Collaboration on Procedural and Conceptual Learning Activities

ERIC Educational Resources Information Center

Belenky, Daniel; Ringenberg, Michael; Olsen, Jennifer; Aleven, Vincent; Rummel, Nikol

2013-01-01

Dual eye-tracking measures enable novel ways to test predictions about collaborative learning. For example, the research project we are engaging in uses measures of gaze recurrence to help understand how collaboration may differ when students are completing various learning activities focused on different learning objectives. Specifically, we…

Identifying Learning Behaviors by Contextualizing Differential Sequence Mining with Action Features and Performance Evolution

ERIC Educational Resources Information Center

Kinnebrew, John S.; Biswas, Gautam

2012-01-01

Our learning-by-teaching environment, Betty's Brain, captures a wealth of data on students' learning interactions as they teach a virtual agent. This paper extends an exploratory data mining methodology for assessing and comparing students' learning behaviors from these interaction traces. The core algorithm employs sequence mining techniques to…

Inter-Judge Agreement in Classifying Students as Learning Disabled.

ERIC Educational Resources Information Center

Epps, Susan; And Others

Eighteen judges with backgrounds in assessment, decision making, and learning disabilities were asked to use an array of information to differentiate learning disabled (LD) and non-learning disabled students. Each judge was provided with forms containing information on 42 test or subtest scores of 50 school-identified LD students and 49 non-LD…

The Effect of Universal Design for Learning (UDL) Application on E-Learning Acceptance: A Structural Equation Model

ERIC Educational Resources Information Center

Al-Azawei, Ahmed; Parslow, Patrick; Lundqvist, Karsten

2017-01-01

Standardising learning content and teaching approaches is not considered to be the best practice in contemporary education. This approach does not differentiate learners based on their individual abilities and preferences. The present research integrates a pedagogical theory "Universal Design for Learning" ("UDL") with an…

Ego depletion interferes with rule-defined category learning but not non-rule-defined category learning.

PubMed

Minda, John P; Rabi, Rahel

2015-01-01

Considerable research on category learning has suggested that many cognitive and environmental factors can have a differential effect on the learning of rule-defined (RD) categories as opposed to the learning of non-rule-defined (NRD) categories. Prior research has also suggested that ego depletion can temporarily reduce the capacity for executive functioning and cognitive flexibility. The present study examined whether temporarily reducing participants' executive functioning via a resource depletion manipulation would differentially impact RD and NRD category learning. Participants were either asked to write a story with no restrictions (the control condition), or without using two common letters (the ego depletion condition). Participants were then asked to learn either a set of RD categories or a set of NRD categories. Resource depleted participants performed more poorly than controls on the RD task, but did not differ from controls on the NRD task, suggesting that self regulatory resources are required for successful RD category learning. These results lend support to multiple systems theories and clarify the role of self-regulatory resources within this theory.

Ego depletion interferes with rule-defined category learning but not non-rule-defined category learning

PubMed Central

Minda, John P.; Rabi, Rahel

2015-01-01

Considerable research on category learning has suggested that many cognitive and environmental factors can have a differential effect on the learning of rule-defined (RD) categories as opposed to the learning of non-rule-defined (NRD) categories. Prior research has also suggested that ego depletion can temporarily reduce the capacity for executive functioning and cognitive flexibility. The present study examined whether temporarily reducing participants’ executive functioning via a resource depletion manipulation would differentially impact RD and NRD category learning. Participants were either asked to write a story with no restrictions (the control condition), or without using two common letters (the ego depletion condition). Participants were then asked to learn either a set of RD categories or a set of NRD categories. Resource depleted participants performed more poorly than controls on the RD task, but did not differ from controls on the NRD task, suggesting that self regulatory resources are required for successful RD category learning. These results lend support to multiple systems theories and clarify the role of self-regulatory resources within this theory. PMID:25688220

Differentiating Science Instruction: Success Stories of High School Science Teachers

ERIC Educational Resources Information Center

Maeng, Jennifer Lynn Cunningham

2011-01-01

This study investigated the characteristics and practices of high school science teachers who differentiate instruction. Specifically teachers' beliefs about science teaching and student learning and how they planned for and implemented differentiated instruction in their classrooms were explored. Understanding how high school science teachers…

Wage differentials between college graduates with and without learning disabilities.

PubMed

Dickinson, David L; Verbeek, Roelant L

2002-01-01

Wage differential studies examining legally protected groups typically focus on gender or racial differences. Legislation also fully protects individuals with learning disabilities (LD). This article is the first to decompose wage differentials between adults with and without LD. An original data set of college graduates with documented LD was constructed, and these individuals were compared to a control group from the National Longitudinal Survey of Youth (NLSY). Our results show that much of the observed lower wages for individuals with LD is due to differences in productivity characteristics. However, there is an unexplained portion of the wage gap that could possibly be considered wage discrimination against individuals with LD. This possibility seems smaller due to the fact that the subsample of the employers who knew of the employee's learning disabilities did not appear to pay significantly lower wages to these individuals. Alternative hypotheses are discussed, as are sample-specific issues.

ERP-based detection of brain pathology in rat models for preclinical Alzheimer's disease

NASA Astrophysics Data System (ADS)

Nouriziabari, Seyed Berdia

Early pathological features of Alzheimer's disease (AD) include the accumulation of hyperphosphorylated tau protein (HP-tau) in the entorhinal cortex and progressive loss of basal forebrain (BF) cholinergic neurons. These pathologies are known to remain asymptomatic for many years before AD is clinically diagnosed; however, they may induce aberrant brain processing which can be captured as an abnormality in event-related potentials (ERPs). Here, we examined cortical ERPs while a differential associative learning paradigm was applied to adult male rats with entorhinal HP-tau, pharmacological blockade of muscarinic acetylcholine receptors, or both conditions. Despite no impairment in differential associative and reversal learning, each pathological feature induced distinct abnormality in cortical ERPs to an extent that was sufficient for machine classifiers to accurately detect a specific type of pathology based on these ERP features. These results highlight a potential use of ERPs during differential associative learning as a biomarker for asymptomatic AD pathology.

Machine Learning to Differentiate Between Positive and Negative Emotions Using Pupil Diameter

PubMed Central

Babiker, Areej; Faye, Ibrahima; Prehn, Kristin; Malik, Aamir

2015-01-01

Pupil diameter (PD) has been suggested as a reliable parameter for identifying an individual’s emotional state. In this paper, we introduce a learning machine technique to detect and differentiate between positive and negative emotions. We presented 30 participants with positive and negative sound stimuli and recorded pupillary responses. The results showed a significant increase in pupil dilation during the processing of negative and positive sound stimuli with greater increase for negative stimuli. We also found a more sustained dilation for negative compared to positive stimuli at the end of the trial, which was utilized to differentiate between positive and negative emotions using a machine learning approach which gave an accuracy of 96.5% with sensitivity of 97.93% and specificity of 98%. The obtained results were validated using another dataset designed for a different study and which was recorded while 30 participants processed word pairs with positive and negative emotions. PMID:26733912

The Differentiated School: Making Revolutionary Changes in Teaching and Learning

ERIC Educational Resources Information Center

Tomlinson, Carol Ann; Brimijoin, Kay; Narvaez, Lane

2008-01-01

While there are lots of books with information on why and how to differentiate instruction, here at last is a book that gets to the heart of why some efforts to differentiate flop while others lead to sweeping, positive results. When it's your job to support differentiated instruction in a single school or systemwide, you need the guidance from…

Compressive sampling by artificial neural networks for video

NASA Astrophysics Data System (ADS)

Szu, Harold; Hsu, Charles; Jenkins, Jeffrey; Reinhardt, Kitt

2011-06-01

We describe a smart surveillance strategy for handling novelty changes. Current sensors seem to keep all, redundant or not. The Human Visual System's Hubel-Wiesel (wavelet) edge detection mechanism pays attention to changes in movement, which naturally produce organized sparseness because a stagnant edge is not reported to the brain's visual cortex by retinal neurons. Sparseness is defined as an ordered set of ones (movement or not) relative to zeros that could be pseudo-orthogonal among themselves; then suited for fault tolerant storage and retrieval by means of Associative Memory (AM). The firing is sparse at the change locations. Unlike purely random sparse masks adopted in medical Compressive Sensing, these organized ones have an additional benefit of using the image changes to make retrievable graphical indexes. We coined this organized sparseness as Compressive Sampling; sensing but skipping over redundancy without altering the original image. Thus, we turn illustrate with video the survival tactics which animals that roam the Earth use daily. They acquire nothing but the space-time changes that are important to satisfy specific prey-predator relationships. We have noticed a similarity between the mathematical Compressive Sensing and this biological mechanism used for survival. We have designed a hardware implementation of the Human Visual System's Compressive Sampling scheme. To speed up further, our mixedsignal circuit design of frame differencing is built in on-chip processing hardware. A CMOS trans-conductance amplifier is designed here to generate a linear current output using a pair of differential input voltages from 2 photon detectors for change detection---one for the previous value and the other the subsequent value, ("write" synaptic weight by Hebbian outer products; "read" by inner product & pt. NL threshold) to localize and track the threat targets.

Closing the Achievement Gap with Curriculum Enrichment and Differentiation: One School's Story

ERIC Educational Resources Information Center

Beecher, Margaret; Sweeny, Sheelah M.

2008-01-01

This article summarizes a unique approach to reducing the achievement gap that strategically blended differentiated curriculum with schoolwide enrichment teaching and learning. The theories of enrichment and instructional differentiation were translated into practice in an elementary school that had previously embraced a remedial paradigm. This…

Differentiated Instruction in a Calculus Curriculum for College Students in Taiwan

ERIC Educational Resources Information Center

Chen, Jing-Hua; Chen, Yi-Chou

2018-01-01

Objectives: To explore differentiated instruction within a calculus curriculum. For college students to learn concentration, motivation and the impact of academic achievement; explore the attitudes and ideas of students on differentiated instruction within a calculus curriculum; build up the diversity of mathematics education within varied…

Leadership for Differentiating Schools & Classrooms.

ERIC Educational Resources Information Center

Tomlinson, Carol Ann; Allan, Susan Demirsky

Differentiation is simply a teacher attending to the learning needs of a particular student or small group of students, rather than teaching a class as though all individuals in it were basically alike. This book explores in 10 chapters how school leaders can develop responsive, personalized, and differentiated classrooms: (1) "Understanding…

Evaluating the Efficacy of Using Pre-Differentiated and Enriched Mathematics Curricula for Grade 3 Students

ERIC Educational Resources Information Center

McCoach, D. Betsy; Gubbins, E. Jean; Foreman, Jennifer; Rambo, Karen E.; Rubenstein, Lisa DaVia

2013-01-01

Although research on the effectiveness of differentiated and enriched instruction in improving the achievement of diverse students is still emerging, some studies suggest that students in academically diverse classrooms benefited academically from differentiated learning experiences. The primary research question explored in this study was…

PISA Mathematics and Reading Performance Differences of Mainstream European and Turkish Immigrant Students

ERIC Educational Resources Information Center

Arikan, Serkan; van de Vijver, Fons J. R.; Yagmur, Kutlay

2017-01-01

Lower reading and mathematics performance of Turkish immigrant students as compared to mainstream European students could reflect differential learning outcomes, differential socioeconomic backgrounds of the groups, differential mainstream language proficiency, and/or test bias. Using PISA reading and mathematics scores of these groups, we…

Influence of a Metacognitive Scaffolding for Information Search in B-"Learning" Courses on Learning Achievement and Its Relationship with Cognitive and Learning Style

ERIC Educational Resources Information Center

Huertas, Adriana; López, Omar; Sanabria, Luis

2017-01-01

The research's objective is to evaluate the differential effect that a metacognitive scaffolding for information web searches has on learning achievement of high school students with different cognitive style in the field dependence and independence dimension and on learning style in the dimension proposed by Honey and Alonso known as CHAEA. One…

A Knowledge Acquisition Approach to Developing Mindtools for Organizing and Sharing Differentiating Knowledge in a Ubiquitous Learning Environment

ERIC Educational Resources Information Center

Hwang, Gwo-Jen; Chu, Hui-Chun; Lin, Yu-Shih; Tsai, Chin-Chung

2011-01-01

Previous studies have reported the importance and benefits of situating students in a real-world learning environment with access to digital-world resources. At the same time, researchers have indicated the need to develop learning guidance mechanisms or tools for assisting students to learn in such a complex learning scenario. In this study, a…

Effects of Image-Based and Text-Based Active Learning Exercises on Student Examination Performance in a Musculoskeletal Anatomy Course

ERIC Educational Resources Information Center

Gross, M. Melissa; Wright, Mary C.; Anderson, Olivia S.

2017-01-01

Research on the benefits of visual learning has relied primarily on lecture-based pedagogy, but the potential benefits of combining active learning strategies with visual and verbal materials on learning anatomy has not yet been explored. In this study, the differential effects of text-based and image-based active learning exercises on examination…

Differentiated Learning Environment--A Classroom for Quadratic Equation, Function and Graphs

ERIC Educational Resources Information Center

Dinç, Emre

2017-01-01

This paper will cover the design of a learning environment as a classroom regarding the Quadratic Equations, Functions and Graphs. The goal of the learning environment offered in the paper is to design a classroom where students will enjoy the process, use their skills they already have during the learning process, control and plan their learning…

Experiential Learning and Its Role in Training and Improved Practice in High Level Sports Officiating

ERIC Educational Resources Information Center

Grover, Kenda S.

2014-01-01

This qualitative study investigated how high level sports officials engage in experiential learning to improve their practice. Adult learning occurs in formal, nonformal and informal environments, and in some cases it is difficult to differentiate between these settings. In the case of cycling officials, learning begins in a nonformal environment…

Regulation during Cooperative and Collaborative Learning: A Theory-Based Review of Terms and Concepts

ERIC Educational Resources Information Center

Schoor, Cornelia; Narciss, Susanne; Körndle, Hermann

2015-01-01

This article reviews the terms and concepts that have been used for describing regulation of learning during cooperative and collaborative learning and suggests differentiating them on the basis of which parts of a regulatory feedback loop model are being shared. During cooperative and collaborative learning, not only self-regulation but also the…

Cognitive Developmental Level Gender, and the Development of Learned Helplessness on Mathematical Calculation and Reasoning Tasks.

ERIC Educational Resources Information Center

Monaco, Nanci M.; Gentile, J. Ronald

1987-01-01

This study was designed to test whether a learned helplessness treatment would decrease performance on mathematical tasks and to extend learned helplessness findings to include the cognitive development dimension. Results showed no differential advantages to either sex in resisting effects of learned helplessness or in benefiting from strategy…

Differentially Private Empirical Risk Minimization

PubMed Central

Chaudhuri, Kamalika; Monteleoni, Claire; Sarwate, Anand D.

2011-01-01

Privacy-preserving machine learning algorithms are crucial for the increasingly common setting in which personal data, such as medical or financial records, are analyzed. We provide general techniques to produce privacy-preserving approximations of classifiers learned via (regularized) empirical risk minimization (ERM). These algorithms are private under the ε-differential privacy definition due to Dwork et al. (2006). First we apply the output perturbation ideas of Dwork et al. (2006), to ERM classification. Then we propose a new method, objective perturbation, for privacy-preserving machine learning algorithm design. This method entails perturbing the objective function before optimizing over classifiers. If the loss and regularizer satisfy certain convexity and differentiability criteria, we prove theoretical results showing that our algorithms preserve privacy, and provide generalization bounds for linear and nonlinear kernels. We further present a privacy-preserving technique for tuning the parameters in general machine learning algorithms, thereby providing end-to-end privacy guarantees for the training process. We apply these results to produce privacy-preserving analogues of regularized logistic regression and support vector machines. We obtain encouraging results from evaluating their performance on real demographic and benchmark data sets. Our results show that both theoretically and empirically, objective perturbation is superior to the previous state-of-the-art, output perturbation, in managing the inherent tradeoff between privacy and learning performance. PMID:21892342

EdREC: Design and Development of Adaptive Platform for Scaling-up Flipped Mastery Learning

DOE Office of Scientific and Technical Information (OSTI.GOV)

Gautam, Thakur

EdREC is an adaptive learning and management platform designed to enhance the adoption of differential classroom and mastery flipped learning in K-12 school system. The platform is an innovative approach to teaching and learning that addresses education needs of each student separately by providing customized education plans and adaptive learning methodologies that tunes to the students abilities as well as giving students freedom to learn in their own way. On one side, EdREC provides innovative ways to help students learn; on the other side, it reduces educators' workload and empowers them to understand their students better. EdREC comes with amore » state-of-the-art computer algorithm package that enables educators to store and retrieve their students' information and augment their abilities to individualize student attention, get real-time feedback about student education progress, and provide corrective actions. The platform provides approaches to design and develop a differential classroom concept that frees much needed time by the teachers to focus more on the students at the individual level and to increase communication and collaboration opportunities among them.« less

Embedding of Authentic Assessment in Work-Integrated Learning Curriculum

ERIC Educational Resources Information Center

Bosco, Anna Maria; Ferns, Sonia

2014-01-01

Contemporary perspectives of higher education endorse a work integrated learning (WIL) approach to curriculum content, delivery and assessment. It is agreed that authenticity in learning relates to real-world experience, however, differentiating and strategically linking WIL provision and facilitation to assessment tasks and collation of authentic…

Agreement among Classroom Observers of Children's Stylistic Learning Behaviors.

ERIC Educational Resources Information Center

Buchanan, Helen Hamlet; McDermott, Paul A.; Schaefer, Barbara A.

1998-01-01

Investigates the interobserver agreement of the Learning Behavior Scale (LBS) by educators (n=16) observing students in special-education classes (n=72). No significant observer effect was found. Moreover, the LBS produced comparable levels of differential learning styles for assessments of individual children. (Author/MKA)

The School Dropout: Implications for Counselors.

ERIC Educational Resources Information Center

Gadwa, Karol; Griggs, Shirley A.

1985-01-01

Assessed learning style of secondary students, categorized as dropout (N=345), alternative (N=214), or traditional students (N=213) using the Learning Style Inventory (LSI). The groups were differentiated on 17 of 23 variables, with dropouts being motivated, peer and teacher oriented, easily bored, preferring evening for learning, preferring…

A Four-Tier Differentiation Model: Engage All Students in the Learning Process

ERIC Educational Resources Information Center

Herrelko, Janet M.

2013-01-01

This study details the creation of a four-tiered format designed to help preservice teachers write differentiated lesson plans. A short history of lesson plan differentiation models is described and how the four-tier approach was developed through collaboration with classroom teachers and university faculty. The unifying element for the format…

11 Practical Ways to Guide Teachers toward Differentiation (and an Evaluation Tool)

ERIC Educational Resources Information Center

Chapman, Carolyn; King, Rita

2005-01-01

Differentiated instruction adapts learning to the students' unique differences. It focuses on the diverse needs of the individual learners. It is stated, that the first step in developing a differentiated instructional program is to provide an introduction to the philosophy and an overview of the benefits for learners. After the introductory…

Teachers Use of a Differentiated Curriculum for Gifted Students

ERIC Educational Resources Information Center

Marotta-Garcia, Christina

2011-01-01

Teachers have the responsibility to educate a diverse group of students in heterogeneous classes. One way in which teachers meet this challenge is to differentiate the curriculum to meet the needs, interests, and abilities of each student. One particular group of students in need of a differentiated curriculum to maximize learning potential is the…

Oscillations and chaos in neural networks: an exactly solvable model.

PubMed Central

Wang, L P; Pichler, E E; Ross, J

1990-01-01

We consider a randomly diluted higher-order network with noise, consisting of McCulloch-Pitts neurons that interact by Hebbian-type connections. For this model, exact dynamical equations are derived and solved for both parallel and random sequential updating algorithms. For parallel dynamics, we find a rich spectrum of different behaviors including static retrieving and oscillatory and chaotic phenomena in different parts of the parameter space. The bifurcation parameters include first- and second-order neuronal interaction coefficients and a rescaled noise level, which represents the combined effects of the random synaptic dilution, interference between stored patterns, and additional background noise. We show that a marked difference in terms of the occurrence of oscillations or chaos exists between neural networks with parallel and random sequential dynamics. Images PMID:2251287

Using Technology to Facilitate Differentiated High School Science Instruction

NASA Astrophysics Data System (ADS)

Maeng, Jennifer L.

2017-10-01

This qualitative investigation explored the beliefs and practices of one secondary science teacher, Diane, who differentiated instruction and studied how technology facilitated her differentiation. Diane was selected based on the results of a previous study, in which data indicated that Diane understood how to design and implement proactively planned, flexible, engaging instructional activities in response to students' learning needs better than the other study participants. Data for the present study included 3 h of semi-structured interview responses, 37.5 h of observations of science instruction, and other artifacts such as instructional materials. This variety of data allowed for triangulation of the evidence. Data were analyzed using a constant comparative approach. Results indicated that technology played an integral role in Diane's planning and implementation of differentiated science lessons. The technology-enhanced differentiated lessons employed by Diane typically attended to students' different learning profiles or interest through modification of process or product. This study provides practical strategies for science teachers beginning to differentiate instruction, and recommendations for science teacher educators and school and district administrators. Future research should explore student outcomes, supports for effective formative assessment, and technology-enhanced readiness differentiation among secondary science teachers.

Cardiac Concomitants of Feedback and Prediction Error Processing in Reinforcement Learning.

PubMed

Kastner, Lucas; Kube, Jana; Villringer, Arno; Neumann, Jane

2017-01-01

Successful learning hinges on the evaluation of positive and negative feedback. We assessed differential learning from reward and punishment in a monetary reinforcement learning paradigm, together with cardiac concomitants of positive and negative feedback processing. On the behavioral level, learning from reward resulted in more advantageous behavior than learning from punishment, suggesting a differential impact of reward and punishment on successful feedback-based learning. On the autonomic level, learning and feedback processing were closely mirrored by phasic cardiac responses on a trial-by-trial basis: (1) Negative feedback was accompanied by faster and prolonged heart rate deceleration compared to positive feedback. (2) Cardiac responses shifted from feedback presentation at the beginning of learning to stimulus presentation later on. (3) Most importantly, the strength of phasic cardiac responses to the presentation of feedback correlated with the strength of prediction error signals that alert the learner to the necessity for behavioral adaptation. Considering participants' weight status and gender revealed obesity-related deficits in learning to avoid negative consequences and less consistent behavioral adaptation in women compared to men. In sum, our results provide strong new evidence for the notion that during learning phasic cardiac responses reflect an internal value and feedback monitoring system that is sensitive to the violation of performance-based expectations. Moreover, inter-individual differences in weight status and gender may affect both behavioral and autonomic responses in reinforcement-based learning.

Cardiac Concomitants of Feedback and Prediction Error Processing in Reinforcement Learning

PubMed Central

Kastner, Lucas; Kube, Jana; Villringer, Arno; Neumann, Jane

2017-01-01

Successful learning hinges on the evaluation of positive and negative feedback. We assessed differential learning from reward and punishment in a monetary reinforcement learning paradigm, together with cardiac concomitants of positive and negative feedback processing. On the behavioral level, learning from reward resulted in more advantageous behavior than learning from punishment, suggesting a differential impact of reward and punishment on successful feedback-based learning. On the autonomic level, learning and feedback processing were closely mirrored by phasic cardiac responses on a trial-by-trial basis: (1) Negative feedback was accompanied by faster and prolonged heart rate deceleration compared to positive feedback. (2) Cardiac responses shifted from feedback presentation at the beginning of learning to stimulus presentation later on. (3) Most importantly, the strength of phasic cardiac responses to the presentation of feedback correlated with the strength of prediction error signals that alert the learner to the necessity for behavioral adaptation. Considering participants' weight status and gender revealed obesity-related deficits in learning to avoid negative consequences and less consistent behavioral adaptation in women compared to men. In sum, our results provide strong new evidence for the notion that during learning phasic cardiac responses reflect an internal value and feedback monitoring system that is sensitive to the violation of performance-based expectations. Moreover, inter-individual differences in weight status and gender may affect both behavioral and autonomic responses in reinforcement-based learning. PMID:29163004

Let's Celebrate Personalization: But Not Too Fast

ERIC Educational Resources Information Center

Tomlinson, Carol Ann

2017-01-01

The concept of personalization in learning appeals to many K-12 teachers and students weary of regimented, one-size-fits-all instruction. The in-vogue term personalization is used to refer to many different learning strategies and structures--from personal learning plans to greater student voice. Differentiation expert Carol Ann Tomlinson is…

Blackstone Valley Prep Mayoral Academy: BVP High School

ERIC Educational Resources Information Center

EDUCAUSE, 2015

2015-01-01

This Rhode Island charter high school serves an intentionally diverse population of students from two urban and two suburban communities. The blended learning model is tailored by grade level and emphasizes differentiation, deeper learning in a community, and assessment. The two-page grantee profiles from Next Generation Learning Challenges (NGLC)…

Applications of Andragogy in Multi-Disciplined Teaching and Learning

ERIC Educational Resources Information Center

Chan, Sang

2010-01-01

Arguments regarding the distinction between child and adult learning have existed for decades. Pedagogy has a long tradition of providing educational guidance in which there is little differentiation between child and adult education. The two groups of learners are assumed to learn under the same philosophy. Conversely, andragogy, advanced by…

Think, Jane, Think. See Jane Think. Go, Jane... Metacognition and Learning in the Library

ERIC Educational Resources Information Center

Jaeger, Paige

2007-01-01

Buzzwords are as prolific in educational circles as bunny rabbits are in spring. Over the last 10 years everyone has heard the buzz of multiculturism, multiple intelligences, learning modalities, essential questions, cultural literacy, media literacy, differentiated instruction, learning by design, curriculum alignment, curriculum mapping,…

Successful Learning Strategies for the Emerging Adolescent.

ERIC Educational Resources Information Center

Butler, Kathleen A.

1986-01-01

Focusing on individual learning styles as a major component of intentional teaching, this discussion points out advantages for teachers who provide options based on students' learning styles in the classroom. Particular attention is given to the model of Style Differentiated Instruction (SDI) which developed from the research on styles, higher…

Learning Goals of AACSB-Accredited Undergraduate Business Programs: Predictors of Conformity versus Differentiation

ERIC Educational Resources Information Center

Brink, Kyle E.; Palmer, Timothy B.; Costigan, Robert D.

2014-01-01

Learning goals are central to assurance of learning. Yet little is known about what goals are used by business programs or how they are established. On the one hand, business schools are encouraged to develop their own unique learning goals. However, business schools also face pressures that would encourage conformity by adopting goals used by…

Neural network models for spatial data mining, map production, and cortical direction selectivity

NASA Astrophysics Data System (ADS)

Parsons, Olga

A family of ARTMAP neural networks for incremental supervised learning has been developed over the last decade. The Sensor Exploitation Group of MIT Lincoln Laboratory (LL) has incorporated an early version of this network as the recognition engine of a hierarchical system for fusion and data mining of multiple registered geospatial images. The LL system has been successfully fielded, but it is limited to target vs. non-target identifications and does not produce whole maps. This dissertation expands the capabilities of the LL system so that it learns to identify arbitrarily many target classes at once and can thus produce a whole map. This new spatial data mining system is designed particularly to cope with the highly skewed class distributions of typical mapping problems. Specification of a consistent procedure and a benchmark testbed has permitted the evaluation of candidate recognition networks as well as pre- and post-processing and feature extraction options. The resulting default ARTMAP network and mapping methodology set a standard for a variety of related mapping problems and application domains. The second part of the dissertation investigates the development of cortical direction selectivity. The possible role of visual experience and oculomotor behavior in the maturation of cells in the primary visual cortex is studied. The responses of neurons in the thalamus and cortex of the cat are modeled when natural scenes are scanned by several types of eye movements. Inspired by the Hebbian-like synaptic plasticity, which is based upon correlations between cell activations, the second-order statistical structure of thalamo-cortical activity is examined. In the simulations, patterns of neural activity that lead to a correct refinement of cell responses are observed during visual fixation, when small ocular movements occur, but are not observed in the presence of large saccades. Simulations also replicate experiments in which kittens are reared under stroboscopic illumination. The abnormal fixational eye movements of these cats may account for the puzzling finding of a specific loss of cortical direction selectivity but preservation of orientation selectivity. This work indicates that the oculomotor behavior of visual fixation may play an important role in the refinement of cell response selectivity.

Integrated Analysis of Alzheimer's Disease and Schizophrenia Dataset Revealed Different Expression Pattern in Learning and Memory.

PubMed

Li, Wen-Xing; Dai, Shao-Xing; Liu, Jia-Qian; Wang, Qian; Li, Gong-Hua; Huang, Jing-Fei

2016-01-01

Alzheimer's disease (AD) and schizophrenia (SZ) are both accompanied by impaired learning and memory functions. This study aims to explore the expression profiles of learning or memory genes between AD and SZ. We downloaded 10 AD and 10 SZ datasets from GEO-NCBI for integrated analysis. These datasets were processed using RMA algorithm and a global renormalization for all studies. Then Empirical Bayes algorithm was used to find the differentially expressed genes between patients and controls. The results showed that most of the differentially expressed genes were related to AD whereas the gene expression profile was little affected in the SZ. Furthermore, in the aspects of the number of differentially expressed genes, the fold change and the brain region, there was a great difference in the expression of learning or memory related genes between AD and SZ. In AD, the CALB1, GABRA5, and TAC1 were significantly downregulated in whole brain, frontal lobe, temporal lobe, and hippocampus. However, in SZ, only two genes CRHBP and CX3CR1 were downregulated in hippocampus, and other brain regions were not affected. The effect of these genes on learning or memory impairment has been widely studied. It was suggested that these genes may play a crucial role in AD or SZ pathogenesis. The different gene expression patterns between AD and SZ on learning and memory functions in different brain regions revealed in our study may help to understand the different mechanism between two diseases.

Machine learning-based quantitative texture analysis of CT images of small renal masses: Differentiation of angiomyolipoma without visible fat from renal cell carcinoma.

PubMed

Feng, Zhichao; Rong, Pengfei; Cao, Peng; Zhou, Qingyu; Zhu, Wenwei; Yan, Zhimin; Liu, Qianyun; Wang, Wei

2018-04-01

To evaluate the diagnostic performance of machine-learning based quantitative texture analysis of CT images to differentiate small (≤ 4 cm) angiomyolipoma without visible fat (AMLwvf) from renal cell carcinoma (RCC). This single-institutional retrospective study included 58 patients with pathologically proven small renal mass (17 in AMLwvf and 41 in RCC groups). Texture features were extracted from the largest possible tumorous regions of interest (ROIs) by manual segmentation in preoperative three-phase CT images. Interobserver reliability and the Mann-Whitney U test were applied to select features preliminarily. Then support vector machine with recursive feature elimination (SVM-RFE) and synthetic minority oversampling technique (SMOTE) were adopted to establish discriminative classifiers, and the performance of classifiers was assessed. Of the 42 extracted features, 16 candidate features showed significant intergroup differences (P < 0.05) and had good interobserver agreement. An optimal feature subset including 11 features was further selected by the SVM-RFE method. The SVM-RFE+SMOTE classifier achieved the best performance in discriminating between small AMLwvf and RCC, with the highest accuracy, sensitivity, specificity and AUC of 93.9 %, 87.8 %, 100 % and 0.955, respectively. Machine learning analysis of CT texture features can facilitate the accurate differentiation of small AMLwvf from RCC. • Although conventional CT is useful for diagnosis of SRMs, it has limitations. • Machine-learning based CT texture analysis facilitate differentiation of small AMLwvf from RCC. • The highest accuracy of SVM-RFE+SMOTE classifier reached 93.9 %. • Texture analysis combined with machine-learning methods might spare unnecessary surgery for AMLwvf.

Gene expression profiling in the hippocampus of learned helpless and nonhelpless rats.

PubMed

Kohen, R; Kirov, S; Navaja, G P; Happe, H Kevin; Hamblin, M W; Snoddy, J R; Neumaier, J F; Petty, F

2005-01-01

In the learned helplessness (LH) animal model of depression, failure to attempt escape from avoidable environmental stress, LH, indicates behavioral despair, whereas nonhelpless (NH) behavior reflects behavioral resilience to the effects of environmental stress. Comparing hippocampal gene expression with large-scale oligonucleotide microarrays, we found that stress-resilient (NH) rats, although behaviorally indistinguishable from controls, showed a distinct gene expression profile compared to LH, sham stressed, and naïve control animals. Genes that were confirmed as differentially expressed in the NH group by quantitative PCR strongly correlated in their levels of expression across all four animal groups. Differential expression could not be confirmed at the protein level. We identified several shared degenerate sequence motifs in the 3' untranslated region (3'UTR) of differentially expressed genes that could be a factor in this tight correlation of expression levels among differentially expressed genes.

Support Vector Machines for Differential Prediction

PubMed Central

Kuusisto, Finn; Santos Costa, Vitor; Nassif, Houssam; Burnside, Elizabeth; Page, David; Shavlik, Jude

2015-01-01

Machine learning is continually being applied to a growing set of fields, including the social sciences, business, and medicine. Some fields present problems that are not easily addressed using standard machine learning approaches and, in particular, there is growing interest in differential prediction. In this type of task we are interested in producing a classifier that specifically characterizes a subgroup of interest by maximizing the difference in predictive performance for some outcome between subgroups in a population. We discuss adapting maximum margin classifiers for differential prediction. We first introduce multiple approaches that do not affect the key properties of maximum margin classifiers, but which also do not directly attempt to optimize a standard measure of differential prediction. We next propose a model that directly optimizes a standard measure in this field, the uplift measure. We evaluate our models on real data from two medical applications and show excellent results. PMID:26158123

Support Vector Machines for Differential Prediction.

PubMed

Kuusisto, Finn; Santos Costa, Vitor; Nassif, Houssam; Burnside, Elizabeth; Page, David; Shavlik, Jude

Machine learning is continually being applied to a growing set of fields, including the social sciences, business, and medicine. Some fields present problems that are not easily addressed using standard machine learning approaches and, in particular, there is growing interest in differential prediction . In this type of task we are interested in producing a classifier that specifically characterizes a subgroup of interest by maximizing the difference in predictive performance for some outcome between subgroups in a population. We discuss adapting maximum margin classifiers for differential prediction. We first introduce multiple approaches that do not affect the key properties of maximum margin classifiers, but which also do not directly attempt to optimize a standard measure of differential prediction. We next propose a model that directly optimizes a standard measure in this field, the uplift measure. We evaluate our models on real data from two medical applications and show excellent results.

Group Dynamics and Individual Roles: A Differentiated Approach to Social-Emotional Learning

ERIC Educational Resources Information Center

Dugas, Daryl

2017-01-01

Differentiated instruction is a set of strategies to help teachers meet each child where he or she is in order to improve students' engagement, lead them to do their best work, and maximize their success. This article describes a differentiated classroom management approach based in group dynamics which focuses on the development of group norms…

Testing Response-Stimulus Equivalence Relations Using Differential Responses as a Sample

ERIC Educational Resources Information Center

Shimizu, Hirofumi

2006-01-01

This study tested the notion that an equivalence relation may include a response when differential responses are paired with stimuli presented during training. Eight normal adults learned three kinds of computer mouse movements as differential response topographies (R1, R2, and R3). Next, in matching-to-sample training, one of the response…

A Comparative Study between Teachers' Self-Efficacy of Differentiated Instruction and Frequency Differentiated Instruction is Implemented

ERIC Educational Resources Information Center

Garrett, Stacie

2017-01-01

Differentiated instruction (DI) is a curriculum framework that focuses on the individual student. Students achieve because teachers develop lessons to the students' readiness levels, interests, and learning styles. Students in early childhood through college have shown increased achievement when DI is implemented. The purpose of this quantitative,…

Value-Differentiation and Self-Esteem among Majority and Immigrant Youth

ERIC Educational Resources Information Center

Daniel, Ella; Boehnke, Klaus; Knafo-Noam, Ariel

2016-01-01

As they inhabit complex social worlds, adolescents often learn competing values, resulting in value-differentiation, within-individual variability in value importance across contexts. But what are the implications of value-differentiation across age groups and cultures? A study of 4007 adolescents aged 11 to 18 (M = 14.41, SD = 2.16), of three…

Integrating Multiple Intelligences and Learning Styles on Solving Problems, Achievement in, and Attitudes towards Math in Six Graders with Learning Disabilities in Cooperative Groups

ERIC Educational Resources Information Center

Eissa, Mourad Ali; Mostafa, Amaal Ahmed

2013-01-01

This study investigated the effect of using differentiated instruction by integrating multiple intelligences and learning styles on solving problems, achievement in, and attitudes towards math in six graders with learning disabilities in cooperative groups. A total of 60 students identified with LD were invited to participate. The sample was…

Rapid processing of chemosensor transients in a neuromorphic implementation of the insect macroglomerular complex

PubMed Central

Pearce, Timothy C.; Karout, Salah; Rácz, Zoltán; Capurro, Alberto; Gardner, Julian W.; Cole, Marina

2012-01-01

We present a biologically-constrained neuromorphic spiking model of the insect antennal lobe macroglomerular complex that encodes concentration ratios of chemical components existing within a blend, implemented using a set of programmable logic neuronal modeling cores. Depending upon the level of inhibition and symmetry in its inhibitory connections, the model exhibits two dynamical regimes: fixed point attractor (winner-takes-all type), and limit cycle attractor (winnerless competition type) dynamics. We show that, when driven by chemosensor input in real-time, the dynamical trajectories of the model's projection neuron population activity accurately encode the concentration ratios of binary odor mixtures in both dynamical regimes. By deploying spike timing-dependent plasticity in a subset of the synapses in the model, we demonstrate that a Hebbian-like associative learning rule is able to organize weights into a stable configuration after exposure to a randomized training set comprising a variety of input ratios. Examining the resulting local interneuron weights in the model shows that each inhibitory neuron competes to represent possible ratios across the population, forming a ratiometric representation via mutual inhibition. After training the resulting dynamical trajectories of the projection neuron population activity show amplification and better separation in their response to inputs of different ratios. Finally, we demonstrate that by using limit cycle attractor dynamics, it is possible to recover and classify blend ratio information from the early transient phases of chemosensor responses in real-time more rapidly and accurately compared to a nearest-neighbor classifier applied to the normalized chemosensor data. Our results demonstrate the potential of biologically-constrained neuromorphic spiking models in achieving rapid and efficient classification of early phase chemosensor array transients with execution times well beyond biological timescales. PMID:23874265

Ising formulation of associative memory models and quantum annealing recall

NASA Astrophysics Data System (ADS)

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.

Validating Machine Learning Algorithms for Twitter Data Against Established Measures of Suicidality.

PubMed

Braithwaite, Scott R; Giraud-Carrier, Christophe; West, Josh; Barnes, Michael D; Hanson, Carl Lee

2016-05-16

One of the leading causes of death in the United States (US) is suicide and new methods of assessment are needed to track its risk in real time. Our objective is to validate the use of machine learning algorithms for Twitter data against empirically validated measures of suicidality in the US population. Using a machine learning algorithm, the Twitter feeds of 135 Mechanical Turk (MTurk) participants were compared with validated, self-report measures of suicide risk. Our findings show that people who are at high suicidal risk can be easily differentiated from those who are not by machine learning algorithms, which accurately identify the clinically significant suicidal rate in 92% of cases (sensitivity: 53%, specificity: 97%, positive predictive value: 75%, negative predictive value: 93%). Machine learning algorithms are efficient in differentiating people who are at a suicidal risk from those who are not. Evidence for suicidality can be measured in nonclinical populations using social media data.

Validating Machine Learning Algorithms for Twitter Data Against Established Measures of Suicidality

PubMed Central

2016-01-01

Background One of the leading causes of death in the United States (US) is suicide and new methods of assessment are needed to track its risk in real time. Objective Our objective is to validate the use of machine learning algorithms for Twitter data against empirically validated measures of suicidality in the US population. Methods Using a machine learning algorithm, the Twitter feeds of 135 Mechanical Turk (MTurk) participants were compared with validated, self-report measures of suicide risk. Results Our findings show that people who are at high suicidal risk can be easily differentiated from those who are not by machine learning algorithms, which accurately identify the clinically significant suicidal rate in 92% of cases (sensitivity: 53%, specificity: 97%, positive predictive value: 75%, negative predictive value: 93%). Conclusions Machine learning algorithms are efficient in differentiating people who are at a suicidal risk from those who are not. Evidence for suicidality can be measured in nonclinical populations using social media data. PMID:27185366

Naming and verbal learning in adults with Alzheimer's disease, mild cognitive impairment and in healthy aging, with low educational levels.

PubMed

Hübner, Lilian Cristine; Loureiro, Fernanda; Tessaro, Bruna; Siqueira, Ellen Cristina Gerner; Jerônimo, Gislaine Machado; Gomes, Irênio; Schilling, Lucas Porcello

2018-02-01

Language assessment seems to be an effective tool to differentiate healthy and cognitively impaired aging groups. This article discusses the impact of educational level on a naming task, on a verbal learning with semantic cues task and on the MMSE in healthy aging adults at three educational levels (very low, low and high) as well as comparing two clinical groups of very low (0-3 years) and low education (4-7 years) patients with Alzheimer's disease (AD) and mild cognitive impairment (MCI) with healthy controls. The participants comprised 101 healthy controls, 17 patients with MCI and 19 with AD. Comparisons between the healthy groups showed an education effect on the MMSE, but not on naming and verbal learning. However, the clinical groups were differentiated in both the naming and verbal learning assessment. The results support the assumption that the verbal learning with semantic cues task is a valid tool to diagnose MCI and AD patients, with no influence from education.

Socioeconomic Contributions of Adult Learning to Community: A Social Capital Perspective. CRLRA Discussion Paper.

ERIC Educational Resources Information Center

Balatti, Jo; Falk, Ian

The socioeconomic contributions of adult learning to community were examined from a social capital perspective. The concepts of human capital and social capital were differentiated, and the relationship between learning, human capital, and social capital was explored. The relevance of social capital in describing the wider benefits of adult…

When Two Paradigms Meet: Does Evaluative Learning Extinguish in Differential Fear Conditioning?

ERIC Educational Resources Information Center

Blechert, Jens; Michael, Tanja; Williams, S. Lloyd; Purkis, Helena M.; Wilhelm, Frank H.

2008-01-01

Contemporary theories of Pavlovian conditioning propose a distinction between signal learning (SL), in which a conditioned stimulus (CS) becomes a predictor for a biologically significant unconditioned stimulus (US), and evaluative learning (EL), in which the valence of the US is transferred to the CS. This distinction is based largely on the…

Impact of Computer Animations in Cognitive Learning: Differentiation

ERIC Educational Resources Information Center

Altiparmak, Kemal

2014-01-01

In mathematic courses, construction of some concepts by the students in a meaningful way may be complicated. In such circumstances, to embody the concepts application of the required technologies may reinforce learning process. Onset of learning process over daily life events of the student's environment may lure their attention and may…

Racial Discourse in Mathematics and Its Impact on Student Learning, Identity, and Participation

ERIC Educational Resources Information Center

Shah, Niral

2013-01-01

Discussions of race in educational research have focused primarily on performance gaps and differential access to advanced coursework. Thus, very little is known about how race mediates the learning process, particularly with respect to classroom participation and student identity formation. This dissertation examines mathematics learning as a…

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

ERIC Educational Resources Information Center

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…

Storytelling: Learning to Read as Social and Cultural Processes

ERIC Educational Resources Information Center

Bloome, David; Kim, Minjeong

2016-01-01

The argument here is that learning to read for young people in school is not a monolithic process but, rather, consists of multiple and differentiated pathways involving the acquisition of diverse reading practices and cultural ideologies embedded in a broad range of social and cultural contexts. Such a view of learning to read entails…

Implicit and Explicit Learning Mechanisms Meet in Monkey Prefrontal Cortex.

PubMed

Chafee, Matthew V; Crowe, David A

2017-10-11

In this issue, Loonis et al. (2017) provide the first description of unique synchrony patterns differentiating implicit and explicit forms of learning in monkey prefrontal networks. Their results have broad implications for how prefrontal networks integrate the two learning mechanisms to control behavior. Copyright © 2017 Elsevier Inc. All rights reserved.

Cultures of Learning in Effective High Schools

ERIC Educational Resources Information Center

Tichnor-Wagner, Ariel; Harrison, Christopher; Cohen-Vogel, Lora

2016-01-01

Purpose: Research indicates that a culture of learning is a key factor in building high schools that foster academic achievement in all students. Yet less is known about which elements of a culture of learning differentiate schools with higher levels of academic performance. To fill this gap, this comparative case study examined the cultures of…

Differential Recruitment of Distinct Amygdalar Nuclei across Appetitive Associative Learning

ERIC Educational Resources Information Center

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…

Transformative Learning Challenges in a Context of Trauma and Fear: An Educator's Story

ERIC Educational Resources Information Center

John, Vaughn M.

2016-01-01

After more than three decades of development, transformative learning theory is currently a major theory of adult learning. It has also attracted substantial critique, leading to further development, application and differentiation. Recent contributions to this vast scholarship show a quest for a more unified theory. This article examines…

Investigating Students' Ideas about Buoyancy and the Influence of Haptic Feedback

ERIC Educational Resources Information Center

Minogue, James; Borland, David

2016-01-01

While haptics (simulated touch) represents a potential breakthrough technology for science teaching and learning, there is relatively little research into its differential impact in the context of teaching and learning. This paper describes the testing of a haptically enhanced simulation (HES) for learning about buoyancy. Despite a lifetime of…

Impact of Multimedia on Students' Perceptions of the Learning Environment in Mathematics Classrooms

ERIC Educational Resources Information Center

Chipangura, Addwell; Aldridge, Jill

2017-01-01

We investigated (1) whether the learning environment perceptions of students in classes frequently exposed to multimedia differed from those of students in classes that were not, (2) whether exposure to multimedia was differentially effective for males and females and (3) relationships between students' perceptions of the learning environment and…

Selective attention: psi performance in children with learning disabilities.

PubMed

Garcia, Vera Lúcia; Pereira, Liliane Desgualdo; Fukuda, Yotaka

2007-01-01

Selective attention is essential for learning how to write and read. The objective of this study was to examine the process of selective auditory attention in children with learning disabilities. Group I included forty subjects aged between 9 years and six months and 10 years and eleven months, who had a low risk of altered hearing, language and learning development. Group II included 20 subjects aged between 9 years and five months and 11 years and ten months, who presented learning disabilities. A prospective study was done using the Pediatric Speech Intelligibility Test (PSI). Right ear PSI with an ipsilateral competing message at speech/noise ratios of 0 and -10 was sufficient to differentiate Group I and Group II. Special attention should be given to the performance of Group II on the first tested ear, which may substantiate important signs of improvements in performance and rehabilitation. The PSI - MCI of the right ear at speech/noise ratios of 0 and -10 was appropriate to differentiate Groups I and II. There was an association with the group that presented learning disabilities: this group showed problems in selective attention.

Mesenchymal Stem Cells – Sources and Clinical Applications

PubMed Central

Klingemann, Hans; Matzilevich, David; Marchand, James

2008-01-01

Summary Although mesenchymal stem cells (MSC) from different tissue sources share many characteristics and generally fulfill accepted criteria for MSC (plastic adherence, certain surface marker expression, and ability to differentiate into mesenchymal tissues), we are increasingly learning that they can be distinguished at the level of cytokine production and gene expression profiles. Their ability to differentiate into different tissues including endodermal and ectodermal lineages, also varies according to tissue origin. Importantly, MSC from fetal sources can undergo more cell divisions before they reach senescence than MSC from adult tissue such as bone marrow or adipose tissue. As we learn more about the differentiation and plasticity of MSC from different sources, health care providers in the future will use them tailored to different medical indications. PMID:21512642

Promoting children's learning and development in conflict-affected countries: Testing change process in the Democratic Republic of the Congo.

PubMed

Aber, J Lawrence; Tubbs, Carly; Torrente, Catalina; Halpin, Peter F; Johnston, Brian; Starkey, Leighann; Shivshanker, Anjuli; Annan, Jeannie; Seidman, Edward; Wolf, Sharon

2017-02-01

Improving children's learning and development in conflict-affected countries is critically important for breaking the intergenerational transmission of violence and poverty. Yet there is currently a stunning lack of rigorous evidence as to whether and how programs to improve learning and development in conflict-affected countries actually work to bolster children's academic learning and socioemotional development. This study tests a theory of change derived from the fields of developmental psychopathology and social ecology about how a school-based universal socioemotional learning program, the International Rescue Committee's Learning to Read in a Healing Classroom (LRHC), impacts children's learning and development. The study was implemented in three conflict-affected provinces of the Democratic Republic of the Congo and employed a cluster-randomized waitlist control design to estimate impact. Using multilevel structural equation modeling techniques, we found support for the central pathways in the LRHC theory of change. Specifically, we found that LRHC differentially impacted dimensions of the quality of the school and classroom environment at the end of the first year of the intervention, and that in turn these dimensions of quality were differentially associated with child academic and socioemotional outcomes. Future implications and directions are discussed.

QML-AiNet: An immune network approach to learning qualitative differential equation models

PubMed Central

Pang, Wei; Coghill, George M.

2015-01-01

In this paper, we explore the application of Opt-AiNet, an immune network approach for search and optimisation problems, to learning qualitative models in the form of qualitative differential equations. The Opt-AiNet algorithm is adapted to qualitative model learning problems, resulting in the proposed system QML-AiNet. The potential of QML-AiNet to address the scalability and multimodal search space issues of qualitative model learning has been investigated. More importantly, to further improve the efficiency of QML-AiNet, we also modify the mutation operator according to the features of discrete qualitative model space. Experimental results show that the performance of QML-AiNet is comparable to QML-CLONALG, a QML system using the clonal selection algorithm (CLONALG). More importantly, QML-AiNet with the modified mutation operator can significantly improve the scalability of QML and is much more efficient than QML-CLONALG. PMID:25648212

Language experience differentiates prefrontal and subcortical activation of the cognitive control network in novel word learning

PubMed Central

King, Kelly E.; Hernandez, Arturo E.

2012-01-01

The purpose of this study was to examine the cognitive control mechanisms in adult English speaking monolinguals compared to early sequential Spanish-English bilinguals during the initial stages of novel word learning. Functional magnetic resonance imaging during a lexico-semantic task after only two hours of exposure to novel German vocabulary flashcards showed that monolinguals activated a broader set of cortical control regions associated with higher-level cognitive processes, including the supplementary motor area (SMA), anterior cingulate (ACC), and dorsolateral prefrontal cortex (DLPFC), as well as the caudate, implicated in cognitive control of language. However, bilinguals recruited a more localized subcortical network that included the putamen, associated more with motor control of language. These results suggest that experience managing multiple languages may differentiate the learning strategy and subsequent neural mechanisms of cognitive control used by bilinguals compared to monolinguals in the early stages of novel word learning. PMID:23194816

Learning-Related Shifts in Generalization Gradients for Complex Sounds

PubMed Central

Wisniewski, Matthew G.; Church, Barbara A.; Mercado, Eduardo

2010-01-01

Learning to discriminate stimuli can alter how one distinguishes related stimuli. For instance, training an individual to differentiate between two stimuli along a single dimension can alter how that individual generalizes learned responses. In this study, we examined the persistence of shifts in generalization gradients after training with sounds. University students were trained to differentiate two sounds that varied along a complex acoustic dimension. Students subsequently were tested on their ability to recognize a sound they experienced during training when it was presented among several novel sounds varying along this same dimension. Peak shift was observed in Experiment 1 when generalization tests immediately followed training, and in Experiment 2 when tests were delayed by 24 hours. These findings further support the universality of generalization processes across species, modalities, and levels of stimulus complexity. They also raise new questions about the mechanisms underlying learning-related shifts in generalization gradients. PMID:19815929

QML-AiNet: An immune network approach to learning qualitative differential equation models.

PubMed

Pang, Wei; Coghill, George M

2015-02-01

In this paper, we explore the application of Opt-AiNet, an immune network approach for search and optimisation problems, to learning qualitative models in the form of qualitative differential equations. The Opt-AiNet algorithm is adapted to qualitative model learning problems, resulting in the proposed system QML-AiNet. The potential of QML-AiNet to address the scalability and multimodal search space issues of qualitative model learning has been investigated. More importantly, to further improve the efficiency of QML-AiNet, we also modify the mutation operator according to the features of discrete qualitative model space. Experimental results show that the performance of QML-AiNet is comparable to QML-CLONALG, a QML system using the clonal selection algorithm (CLONALG). More importantly, QML-AiNet with the modified mutation operator can significantly improve the scalability of QML and is much more efficient than QML-CLONALG.

Learning partial differential equations via data discovery and sparse optimization

NASA Astrophysics Data System (ADS)

Schaeffer, Hayden

2017-01-01

We investigate the problem of learning an evolution equation directly from some given data. This work develops a learning algorithm to identify the terms in the underlying partial differential equations and to approximate the coefficients of the terms only using data. The algorithm uses sparse optimization in order to perform feature selection and parameter estimation. The features are data driven in the sense that they are constructed using nonlinear algebraic equations on the spatial derivatives of the data. Several numerical experiments show the proposed method's robustness to data noise and size, its ability to capture the true features of the data, and its capability of performing additional analytics. Examples include shock equations, pattern formation, fluid flow and turbulence, and oscillatory convection.

Learning partial differential equations via data discovery and sparse optimization.

PubMed

Schaeffer, Hayden

2017-01-01

We investigate the problem of learning an evolution equation directly from some given data. This work develops a learning algorithm to identify the terms in the underlying partial differential equations and to approximate the coefficients of the terms only using data. The algorithm uses sparse optimization in order to perform feature selection and parameter estimation. The features are data driven in the sense that they are constructed using nonlinear algebraic equations on the spatial derivatives of the data. Several numerical experiments show the proposed method's robustness to data noise and size, its ability to capture the true features of the data, and its capability of performing additional analytics. Examples include shock equations, pattern formation, fluid flow and turbulence, and oscillatory convection.

Learning partial differential equations via data discovery and sparse optimization

PubMed Central

2017-01-01

We investigate the problem of learning an evolution equation directly from some given data. This work develops a learning algorithm to identify the terms in the underlying partial differential equations and to approximate the coefficients of the terms only using data. The algorithm uses sparse optimization in order to perform feature selection and parameter estimation. The features are data driven in the sense that they are constructed using nonlinear algebraic equations on the spatial derivatives of the data. Several numerical experiments show the proposed method's robustness to data noise and size, its ability to capture the true features of the data, and its capability of performing additional analytics. Examples include shock equations, pattern formation, fluid flow and turbulence, and oscillatory convection. PMID:28265183

Neural network error correction for solving coupled ordinary differential equations

NASA Technical Reports Server (NTRS)

Shelton, R. O.; Darsey, J. A.; Sumpter, B. G.; Noid, D. W.

1992-01-01

A neural network is presented to learn errors generated by a numerical algorithm for solving coupled nonlinear differential equations. The method is based on using a neural network to correctly learn the error generated by, for example, Runge-Kutta on a model molecular dynamics (MD) problem. The neural network programs used in this study were developed by NASA. Comparisons are made for training the neural network using backpropagation and a new method which was found to converge with fewer iterations. The neural net programs, the MD model and the calculations are discussed.

Bloom's taxonomy of cognitive learning objectives.

PubMed

Adams, Nancy E

2015-07-01

Information professionals who train or instruct others can use Bloom's taxonomy to write learning objectives that describe the skills and abilities that they desire their learners to master and demonstrate. Bloom's taxonomy differentiates between cognitive skill levels and calls attention to learning objectives that require higher levels of cognitive skills and, therefore, lead to deeper learning and transfer of knowledge and skills to a greater variety of tasks and contexts.

A comparative evaluation of supervised and unsupervised representation learning approaches for anaplastic medulloblastoma differentiation

NASA Astrophysics Data System (ADS)

Cruz-Roa, Angel; Arevalo, John; Basavanhally, Ajay; Madabhushi, Anant; González, Fabio

2015-01-01

Learning data representations directly from the data itself is an approach that has shown great success in different pattern recognition problems, outperforming state-of-the-art feature extraction schemes for different tasks in computer vision, speech recognition and natural language processing. Representation learning applies unsupervised and supervised machine learning methods to large amounts of data to find building-blocks that better represent the information in it. Digitized histopathology images represents a very good testbed for representation learning since it involves large amounts of high complex, visual data. This paper presents a comparative evaluation of different supervised and unsupervised representation learning architectures to specifically address open questions on what type of learning architectures (deep or shallow), type of learning (unsupervised or supervised) is optimal. In this paper we limit ourselves to addressing these questions in the context of distinguishing between anaplastic and non-anaplastic medulloblastomas from routine haematoxylin and eosin stained images. The unsupervised approaches evaluated were sparse autoencoders and topographic reconstruct independent component analysis, and the supervised approach was convolutional neural networks. Experimental results show that shallow architectures with more neurons are better than deeper architectures without taking into account local space invariances and that topographic constraints provide useful invariant features in scale and rotations for efficient tumor differentiation.

Unique and shared validity of the "Wechsler logical memory test", the "California verbal learning test", and the "verbal learning and memory test" in patients with epilepsy.

PubMed

Helmstaedter, Christoph; Wietzke, Jennifer; Lutz, Martin T

2009-12-01

This study was set-up to evaluate the construct validity of three verbal memory tests in epilepsy patients. Sixty-one consecutively evaluated patients with temporal lobe epilepsy (TLE) or extra-temporal epilepsy (E-TLE) underwent testing with the verbal learning and memory test (VLMT, the German equivalent of the Rey auditory verbal learning test, RAVLT); the California verbal learning test (CVLT); the logical memory and digit span subtests of the Wechsler memory scale, revised (WMS-R); and testing of intelligence, attention, speech and executive functions. Factor analysis of the memory tests resulted in test-specific rather than test over-spanning factors. Parameters of the CVLT and WMS-R, and to a much lesser degree of the VLMT, were highly correlated with attention, language function and vocabulary. Delayed recall measures of logical memory and the VLMT differentiated TLE from E-TLE. Learning and memory scores off all three tests differentiated mesial temporal sclerosis from other pathologies. A lateralization of the epilepsy was possible only for a subsample of 15 patients with mesial TLE. Although the three tests provide overlapping indicators for a temporal lobe epilepsy or a mesial pathology, they can hardly be taken in exchange. The tests have different demands on semantic processing and memory organization, and they appear differentially sensitive to performance in non-memory domains. The tests capability to lateralize appears to be poor. The findings encourage the further discussion of the dependency of memory outcomes on test selection.

Improved pedagogy for linear differential equations by reconsidering how we measure the size of solutions

NASA Astrophysics Data System (ADS)

Tisdell, Christopher C.

2017-11-01

For over 50 years, the learning of teaching of a priori bounds on solutions to linear differential equations has involved a Euclidean approach to measuring the size of a solution. While the Euclidean approach to a priori bounds on solutions is somewhat manageable in the learning and teaching of the proofs involving second-order, linear problems with constant co-efficients, we believe it is not pedagogically optimal. Moreover, the Euclidean method becomes pedagogically unwieldy in the proofs involving higher-order cases. The purpose of this work is to propose a simpler pedagogical approach to establish a priori bounds on solutions by considering a different way of measuring the size of a solution to linear problems, which we refer to as the Uber size. The Uber form enables a simplification of pedagogy from the literature and the ideas are accessible to learners who have an understanding of the Fundamental Theorem of Calculus and the exponential function, both usually seen in a first course in calculus. We believe that this work will be of mathematical and pedagogical interest to those who are learning and teaching in the area of differential equations or in any of the numerous disciplines where linear differential equations are used.

Learning, Differentiation and Strategic Action in Secondary Education: Analyses from the "Identity and Learning Programme"

ERIC Educational Resources Information Center

Pollard, Andrew; Filer, Ann

2007-01-01

This paper reports on the social factors influencing the learning of two cohorts of school students and their experience of compulsory secondary education in a city in southern England - the secondary schooling phase of a 12-year, longitudinal ethnographic study that also tracked the same children's experiences through primary schooling. We embed…

The Effects of Self-Explanation and Reading Questions and Answers on Learning Computer Programming Language

ERIC Educational Resources Information Center

Lee, Nancy

2013-01-01

The current study explored the differential effects of two learning strategies, self-explanation and reading questions and answers, on students' test performance in the computer programming language JavaScript. Students' perceptions toward the two strategies as to their effectiveness in learning JavaScript was also explored by examining students'…

"What Is the Usefulness of Your Schoolwork?": The Differential Effects of Intrinsic and Extrinsic Goal Framing on Optimal Learning

ERIC Educational Resources Information Center

Vansteenkiste, Maarten; Soenens, Bart; Verstuyf, Joke; Lens, Willy

2009-01-01

Various motivational frameworks converge to suggest that highlighting the relevance of a learning activity yields benefits for students' learning and performance. Herein, we review a set of studies grounded in self-determination theory's distinction between intrinsic and extrinsic goals, which show that the beneficial effect of a learning…

Diverse Family Types and Out-of-School Learning Time of Young School-Age Children

ERIC Educational Resources Information Center

Ono, Hiromi; Sanders, James

2010-01-01

Sources of differentials in out-of-school learning time between children in first marriage biological parent families and children in six nontraditional family types are identified. Analyses of time diaries reveal that children in four of the six nontraditional family types spend fewer minutes learning than do children in first marriage biological…

Nature of Teacher-Students' Interaction in Electronic Learning and Traditional Courses of Higher Education--A Review

ERIC Educational Resources Information Center

Malik, Sufiana Khatoon; Khurshed, Fauzia

2011-01-01

Present paper explores differential teacher-student interaction in electronic learning (el) and in face to face traditional learning (tl) courses at higher education. After thorough study literature available and getting information from university teachers teaching el and tl courses about the nature of teacher-students interaction in both modes…

A Knowledge Engineering Approach to Developing Educational Computer Games for Improving Students' Differentiating Knowledge

ERIC Educational Resources Information Center

Hwang, Gwo-Jen; Sung, Han-Yu; Hung, Chun-Ming; Yang, Li-Hsueh; Huang, Iwen

2013-01-01

Educational computer games have been recognized as being a promising approach for motivating students to learn. Nevertheless, previous studies have shown that without proper learning strategies or supportive models, the learning achievement of students might not be as good as expected. In this study, a knowledge engineering approach is proposed…

The Creation of Task-Based Differentiated Learning Materials for Students with Learning Difficulties and/or Disabilities.

ERIC Educational Resources Information Center

Barker, Trevor; Jones, Sara; Britton, Carol; Messer, David

This paper describes Horizon, a European-funded project designed to increase employment opportunities for students with disabilities or learning difficulties. The project established a working cafe/restaurant (Cafe Horizon) in East London staffed by students. Part of the project involved the creation of multimedia units linked directly to Level 1…

Support Required for Primary and Secondary Students with Communication Disorders and/or Other Learning Needs

ERIC Educational Resources Information Center

McLeod, Sharynne; McKinnon, David H.

2010-01-01

Prioritization of school students with additional learning needs is a reality due to a finite resource base. Limited evidence exists regarding teachers' prioritization of primary and secondary school students with additional learning needs. The aim of the present article was to differentiate teachers' perceptions of the level of support required…

Blending toward Competency. Early Patterns of Blended Learning and Competency-Based Education in New Hampshire

ERIC Educational Resources Information Center

Freeland, Julia

2014-01-01

As the education field strives to differentiate and personalize learning to cater to each student, two related movements are gaining attention: competency-based education and blended learning. In competency-based models, students advance on the basis of mastery, rather than according to the traditional methods of counting progress in terms of time…

Tuning Primary Learning Style for Children with Secondary Behavioral Patterns

ERIC Educational Resources Information Center

Mosharraf, Maedeh

2016-01-01

Personalization is one of the most expected features in the current educational systems. User modeling is supposed to be the first stage of this process, which may incorporate learning style as an important part of the model. Learning style, which is a non-stable characteristic in the case of children, differentiates students in learning…

The Relevance of the Nature of Learned Associations for the Differentiation of Human Memory Systems

ERIC Educational Resources Information Center

Rose, Michael; Haider, Hilde; Weiller, Cornelius; Buchel, Christian

2004-01-01

In a previous functional magnetic resonance imaging (fMRI) study we demonstrated an involvement of the medial temporal lobe (MTL) during an implicit learning task. We concluded that the MTL was engaged because of the complex contingencies that were implicitly learned. In addition, the basal ganglia demonstrated effects of a paralleled…

Judgments of Self-Perceived Academic Competence and Their Differential Impact on Students' Achievement Motivation, Learning Approach, and Academic Performance

ERIC Educational Resources Information Center

Ferla, Johan; Valcke, Martin; Schuyten, Gilberte

2010-01-01

Using path analysis, the present study focuses on the development of a model describing the impact of four judgments of self-perceived academic competence on higher education students' achievement goals, learning approach, and academic performance. Results demonstrate that academic self-efficacy, self-efficacy for self-regulated learning, academic…

Improving Understanding in Ordinary Differential Equations through Writing in a Dynamical Environment

ERIC Educational Resources Information Center

Habre, Samer

2012-01-01

Research on writing in mathematics has shown that students learn more effectively in an environment that promotes this skill and that writing is most beneficial when it is directed at the learning aspect. Writing, however, necessitates proficiency on the part of the students that may not have been developed at earlier learning stages. Research has…

The Differential Impact of Observational Learning and Practice-Based Learning on the Development of Oral Presentation Skills in Higher Education

ERIC Educational Resources Information Center

De Grez, Luc; Valcke, Martin; Roozen, Irene

2014-01-01

The present study focuses on the design and evaluation of an innovative instructional approach to developing oral presentation skills. The intervention builds on the observational learning theoretical perspective. This perspective is contrasted with the traditional training and practice approach. Two sequencing approaches--learners starting with…

Concept Development in Learning Physics: The Case of Electric Current and Voltage Revisited

ERIC Educational Resources Information Center

Koponen, Ismo T.; Huttunen, Laura

2013-01-01

In learning conceptual knowledge in physics, a common problem is the development and differentiation of concepts in the learning process. An important part of this development process is the re-organisation or re-structuring process in which students' conceptual knowledge and concepts change. This study proposes a new view of concept…

Theory for the alignment of cortical feature maps during development.

PubMed

Bressloff, Paul C; Oster, Andrew M

2010-08-01

We present a developmental model of ocular dominance column formation that takes into account the existence of an array of intrinsically specified cytochrome oxidase blobs. We assume that there is some molecular substrate for the blobs early in development, which generates a spatially periodic modulation of experience-dependent plasticity. We determine the effects of such a modulation on a competitive Hebbian mechanism for the modification of the feedforward afferents from the left and right eyes. We show how alternating left and right eye dominated columns can develop, in which the blobs are aligned with the centers of the ocular dominance columns and receive a greater density of feedforward connections, thus becoming defined extrinsically. More generally, our results suggest that the presence of periodically distributed anatomical markers early in development could provide a mechanism for the alignment of cortical feature maps.

Differential learning and memory performance in OEF/OIF veterans for verbal and visual material.

PubMed

Sozda, Christopher N; Muir, James J; Springer, Utaka S; Partovi, Diana; Cole, Michael A

2014-05-01

Memory complaints are particularly salient among veterans who experience combat-related mild traumatic brain injuries and/or trauma exposure, and represent a primary barrier to successful societal reintegration and everyday functioning. Anecdotally within clinical practice, verbal learning and memory performance frequently appears differentially reduced versus visual learning and memory scores. We sought to empirically investigate the robustness of a verbal versus visual learning and memory discrepancy and to explore potential mechanisms for a verbal/visual performance split. Participants consisted of 103 veterans with reported history of mild traumatic brain injuries returning home from U.S. military Operations Enduring Freedom and Iraqi Freedom referred for outpatient neuropsychological evaluation. Findings indicate that visual learning and memory abilities were largely intact while verbal learning and memory performance was significantly reduced in comparison, residing at approximately 1.1 SD below the mean for verbal learning and approximately 1.4 SD below the mean for verbal memory. This difference was not observed in verbal versus visual fluency performance, nor was it associated with estimated premorbid verbal abilities or traumatic brain injury history. In our sample, symptoms of depression, but not posttraumatic stress disorder, were significantly associated with reduced composite verbal learning and memory performance. Verbal learning and memory performance may benefit from targeted treatment of depressive symptomatology. Also, because visual learning and memory functions may remain intact, these might be emphasized when applying neurocognitive rehabilitation interventions to compensate for observed verbal learning and memory difficulties.

Influence of contingency awareness on neural, electrodermal and evaluative responses during fear conditioning

PubMed Central

Merz, Christian J.; Klucken, Tim; Schweckendiek, Jan; Vaitl, Dieter; Wolf, Oliver T.; Stark, Rudolf

2011-01-01

In an fMRI study, effects of contingency awareness on conditioned responses were assessed in three groups comprising 118 subjects. A differential fear-conditioning paradigm with visual conditioned stimuli, an electrical unconditioned stimulus and two distractors was applied. The instructed aware group was informed about the contingencies, whereas the distractors prevented contingency detection in the unaware group. The third group (learned aware) was not informed about the contingencies, but learned them despite the distractors. Main effects of contingency awareness on conditioned responses emerged in several brain structures. Post hoc tests revealed differential dorsal anterior cingulate, insula and ventral striatum responses in aware conditioning only, whereas the amygdala was activated independent of contingency awareness. Differential responses of the hippocampus were specifically observed in learned aware subjects, indicating a role in the development of contingency awareness. The orbitofrontal cortex showed varying response patterns: lateral structures showed higher responses in instructed aware than unaware subjects, the opposite was true for medial parts. Conditioned subjective and electrodermal responses emerged only in the two aware groups. These results confirm the independence of conditioned amygdala responses from contingency awareness and indicate specific neural circuits for different aspects of fear acquisition in unaware, learned aware and instructed aware subjects. PMID:20693389

Do Processing Patterns of Strengths and Weaknesses Predict Differential Treatment Response?

ERIC Educational Resources Information Center

Miciak, Jeremy; Williams, Jacob L.; Taylor, W. Pat; Cirino, Paul T.; Fletcher, Jack M.; Vaughn, Sharon

2016-01-01

No previous empirical study has investigated whether the learning disabilities (LD) identification decisions of proposed methods to operationalize processing strengths and weaknesses approaches for LD identification are associated with differential treatment response. We investigated whether the identification decisions of the…

A Proposed System for Differentiating Elementary Mathematics for Exceptionally Able Students.

ERIC Educational Resources Information Center

Sirr, Palma M.

1984-01-01

A pilot mathematics project for one exceptionally able elementary student expanded to include other students and schools. Project activities included a needs assessment and development of a learning center approach and materials to differentiate the core elementary mathematics curriculum. (CL)