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PubMed1988

A unified framework for connectionist systems.

Pattern classification using connectionist (i.e., neural network) models is viewed within a statistical framework. A connectionist network's subjective beliefs about its statistical environment are derived. This belief structure is the network's "subjective" probability distribution. Stimulus classification is interpreted as computing the "most probable" response for a given stimulus with respect to the subjective probability distribution. Given the subjective probability distribution, learning algorithms can be analyzed and designed using maximum likelihood estimation techniques, and statistical tests can be developed to evaluate and compare network architectures. The framework is applicable to many connectionist networks including those of Hopfield (1982, 1984), Cohen and Grossberg (1983), Anderson et al. (1977), and Rumelhart et al. (1986b).

Animals↗
PubMed2004

Habit learning in Tourette syndrome: a translational neuroscience approach to a developmental psychopathology.

BACKGROUND: The etiology of Tourette syndrome (TS) involves disturbances in the structure and function of the basal ganglia. The basal ganglia mediate habit learning. OBJECTIVE: To study habit learning in persons with TS. DESIGN: Patients with TS were compared with normal controls in performance on a probabilistic classification, or habit-learning task (weather prediction). SETTING: University research institute. PARTICIPANTS: One hundred twenty-three children and adults, 56 with a diagnosis of TS and 67 healthy control subjects. MAIN OUTCOME MEASURES: Habit learning was assessed by the extent of improvement in accuracy of predictions and reaction times over trial blocks during performance of the weather prediction task. Declarative learning was assessed by performance on 3 tasks that required intact declarative memory functioning. RESULTS: Children with TS were impaired at habit learning relative to normal controls (P = .01). This finding was replicated in the independent sample of adults with TS (P = .01). The rate of learning correlated inversely with the severity of tic symptoms across both samples (r = -0.34; P = .01). Thus, impaired learning accompanied more severe symptoms. Measures of declarative memory functioning, in contrast, were normal in the TS groups. CONCLUSIONS: Striatal learning systems are uniquely dysfunctional in both children and adults with TS. The correlation of habit learning with symptom severity suggests that the number and severity of tics are a function of the degree to which the system for habit learning is dysfunctional. Thus, both the deficits in habit learning and the tic symptoms of TS are likely to be consequences of the previously reported anatomical and functional disturbances of the striatum in children and adults who have TS. The existence of a well-developed animal model for this learning system, which permits study of the neural and molecular bases of habit learning, has important implications for the neurobiological study of TS and for the development of new or improved therapeutics for this condition.

Attention Deficit Disorder with Hyperactivity↗
PubMed1998

Spontaneously hypertensive rats: a potential model to identify drugs for treatment of learning disorders.

Spontaneously hypertensive rats (SHR) of 3 to 12 months of age learned and retrieved less information than normotensive Wistar-Kyoto rats (WKY), although no difference was found with animals from 18 and 24 months of age. The combined influence of hypertension and aging had an additive detrimental effect on cognitive functions. Notwithstanding these deficiencies in learning and memory, SHR have seldom been used as a model in the screening of drugs with therapeutic potential for treatment of disorders of cognitive processes. Moreover, the calcium channel blocker nimodipine has beneficial effects on learning in both aged and hypertensive animals and humans. However, no attempt has been made to investigate whether nimodipine can reverse the additive deleterious effects of aging and hypertension in the same subject. We recently reported that deteriorated animals (middle-aged and/or hypertensive) chronically treated with nimodipine (via osmotic minipumps) exhibit higher learning scores. This information indicates that nimodipine can reverse the impairing effects of either aging or hypertension on learning; the presence of the two conditions, however, produces a severe impairment that can be partially reversed by this drug. Therefore, we propose that mature and middle-aged SHR represent a model for the screening of potentially useful drugs in the treatment of learning disorders, probably associated with hypertension and/or aging. Nevertheless, it must be remembered that the SHR is a genetic model and the appearance of neural disturbances could be a parallel genetic phenomenon and not necessarily or exclusively related to hypertension per se.

Aging↗
PubMed2002

The neural basis of human error processing: reinforcement learning, dopamine, and the error-related negativity.

The authors present a unified account of 2 neural systems concerned with the development and expression of adaptive behaviors: a mesencephalic dopamine system for reinforcement learning and a "generic" error-processing system associated with the anterior cingulate cortex. The existence of the error-processing system has been inferred from the error-related negativity (ERN), a component of the event-related brain potential elicited when human participants commit errors in reaction-time tasks. The authors propose that the ERN is generated when a negative reinforcement learning signal is conveyed to the anterior cingulate cortex via the mesencephalic dopamine system and that this signal is used by the anterior cingulate cortex to modify performance on the task at hand. They provide support for this proposal using both computational modeling and psychophysiological experimentation.

Analysis of Variance↗
PubMed1994

Deviation in cerebral excitability: possible clinical implications.

Schizophrenia, a chemical signaling disorder in the brain, is also a deteriorating neurological disorder. The deficit in cerebral excitability, and associated reduced synaptic density, imply a risk of cortical breakdown of circuitry accompanied by an insufficient fill-in mechanism, and persistent silent spots, but no total loss of function, only dysfunction. This is subjectively experienced as deficiencies of cognition, perception and sensorimotor phenomena depending upon localization and connections of the disconnected circuitry. Considering the adversity inherent in this neural network, both the fast Hebbian pre-post form of learning and the slow pre-modulatory coincidence form of learning are probably impaired. The use of Feed Back Loops which usually govern our behaviour might also be impaired. In addition, we have to consider the daily problem of insufficient drive and motivation. Manic depressive psychosis, a chemical signaling disorder in the brain, is a true functional psychosis. The raised excitatory drive and raised synaptic density imply raised risk of uncoupling of circadian rhythms via the direct glutamatergic input to the suprachiasmatic nucleus of hypothalamus (SCN). This episodic brain stem dysfunction illustrates how a deficit in inhibition renders the brain unstable. The requirements of the fast Hebbian form of learning should easily be met, and neither should the slow forms of learning present a problem in networks characterized by excessive density.(ABSTRACT TRUNCATED AT 250 WORDS)

Cerebral Cortex↗
PubMed2004

New training strategies for constructive neural networks with application to regression problems.

Regression problem is an important application area for neural networks (NNs). Among a large number of existing NN architectures, the feedforward NN (FNN) paradigm is one of the most widely used structures. Although one-hidden-layer feedforward neural networks (OHL-FNNs) have simple structures, they possess interesting representational and learning capabilities. In this paper, we are interested particularly in incremental constructive training of OHL-FNNs. In the proposed incremental constructive training schemes for an OHL-FNN, input-side training and output-side training may be separated in order to reduce the training time. A new technique is proposed to scale the error signal during the constructive learning process to improve the input-side training efficiency and to obtain better generalization performance. Two pruning methods for removing the input-side redundant connections have also been applied. Numerical simulations demonstrate the potential and advantages of the proposed strategies when compared to other existing techniques in the literature.

Algorithms↗
PubMed2004

Entropy-based kernel mixture modeling for topographic map formation.

A new information-theoretic learning algorithm for kernel-based topographic map formation is introduced. In the one-dimensional case, the algorithm is aimed at uniformizing the cumulative distribution of the kernel mixture densities by maximizing its differential entropy. A nonparametric differential entropy estimator is used on which normalized gradient ascent is performed. Both differentiable and nondifferentiable kernels are in principle supported, such as Gaussian and rectangular (on/off) kernels. The relation is shown with joint entropy maximization of the kernel outputs. The learning algorithm's performance is assessed and compared with the theoretically optimal performance. A fixed-point rule is derived for the case of heterogeneous kernel mixtures. Finally, an extension of the algorithm to the multidimensional case is suggested.

Algorithms↗
PubMed1992

A movement pattern generator model using artificial neural networks.

Artificial neural networks (ANN's) allow a new approach to biological modeling. The main applications of ANN's have been geared towards the modeling of the association and learning mechanisms of the brain; only a few researchers have explored them for motor control. The fact that ANN's are based on biological systems indicates their potential application for a biological act such as locomotion. Towards this goal, we have developed a "movement pattern generator," using an ANN for generating periodic movement trajectories. This model is based on the concept of "central pattern generators." Jordan's sequential network, which is capable of learning sequences of patterns, was modified and used to generate several bipedal trajectories (or gaits), coded in task space, at different frequencies. The network model successfully learned all of the trajectories presented to it. The model has many attractive properties such as limit cycle behavior, generalization of trajectories and frequencies, phase maintenance, and fault tolerance. The movement pattern generator model is potentially applicable for improved understanding of animal locomotion and for use in legged robots and rehabilitation medicine.

Animals↗
PubMed1986

Young children's understanding of random phenomena.

2 experiments on the development of the understanding of random phenomena are reported. Of interest was whether children understand the characteristic uncertainty in the physical nature of random phenomena as well as the unpredictability of outcomes. Children were asked, for both a random and a determined phenomenon, whether they knew what its next outcome would be and why. In Experiment 1, 4-, 5-, and 7-year-olds correctly differentiated their responses to the question of outcome predictability; the 2 older groups also mentioned appropriate characteristics of the random mechanism in explaining why they did not know what its outcome would be. Although 3-year-olds did not differentiate the random and determined phenomena, neither did they treat both phenomena as predictable. This latter result is inconsistent with Piaget and Inhelder's characterization of an early stage of development. Experiment 2 was designed to control for the possibility that children in Experiment 1 learned how to respond on the basis of pretest experience with the 2 different phenomena. 5- and 7-year-olds performed at a comparable level to the same-aged children in Experiment 1. Results suggest an earlier understanding of random phenomena than previously has been reported and support results in the literature indicating an early understanding of causality.

Child↗
PubMed1997

Effects of habenular lesions upon two-way active avoidance conditioning in rats.

To evaluate if habenular nuclei lesions improve, impair, or have no effects on two-way active avoidance acquisition and/or retention, rats in a Lesion group were subjected to bilateral electrolytical lesions of this complex, while control rats were sham-operated (Sham group). Once recovered from the stereotaxic procedures, rats were submitted to 5 training sessions (10 trials each, one session per day) of two-way active avoidance conditioning. Ten days after the last training session, another session was administered in order to test the long-term retention of the task. Results indicated that habenular lesions did not affect the overall performance of the rats during either the acquisition sessions or the retention session of two-way active avoidance. We suggest that habenular lesions can affect the acquisition of several learning tasks, probably through their role in modulating stress responses and/or arousal states. The nature of these effects (whether facilitative, detrimental, or neutral) might depend on the interaction between several factors such as the kind of task, the specific conditioning procedures (which may generate different stress levels), and the specific area destroyed by the lesion.

Animals↗
PubMed2002

A computational account of altered error processing in older age: dopamine and the error-related negativity.

When participants commit errors or receive feedback signaling that they have made an error, a negative brain potential is elicited. According to Holroyd and Coles's (in press) neurocomputational model of error processing, this error-related negativity (ERN) is elicited when the brain first detects that the consequences of an action are worse than expected. To study age-related changes in error processing, we obtained performance and ERN measures of younger and high-functioning older adults. Experiment 1 demonstrated reduced ERN amplitudes in older adults in the context of otherwise intact brain potentials. This result could not be attributed to uncertainty about the required response in older adults. Experiment 2 revealed impaired performance and reduced response- and feedback-related ERNs of older adults in a probabilistic learning task. These age changes could be simulated by manipulation of a single parameter of the neurocomputational model, this manipulation corresponding to weakened phasic activity of the mesencephalic dopamine system.

Adolescent↗
PubMed2004

From blind signal extraction to blind instantaneous signal separation: criteria, algorithms, and stability.

This paper reports a study on the problem of the blind simultaneous extraction of specific groups of independent components from a linear mixture. This paper first presents a general overview and unification of several information theoretic criteria for the extraction of a single independent component. Then, our contribution fills the theoretical gap that exists between extraction and separation by presenting tools that extend these criteria to allow the simultaneous blind extraction of subsets with an arbitrary number of independent components. In addition, we analyze a family of learning algorithms based on Stiefel manifolds and the natural gradient ascent, present the nonlinear optimal activations (score) functions, and provide new or extended local stability conditions. Finally, we illustrate the performance and features of the proposed approach by computer-simulation experiments.

Algorithms↗
PubMed1998

Influences of prior knowledge on selective weighting of category members.

Three experiments addressed how prior theories affect categorization, comparing the influence of theory-congruent versus theory-incongruent category members. Subjects observed descriptions of persons, some congruent with prior knowledge and some incongruent, then made transfer judgments. In Experiment 1, subjects were given a relatively long time to study each description, whereas in Experiment 2 study time was manipulated between subjects. In Experiment 3, learning was self-paced by each subject. It was found that, with enough study time, prior knowledge had 2 distinct influences. First, prior knowledge provided an initial representation, subsequently revised in light of new observations. Second, incongruent observations had more impact than congruent observations on categorization. In comparison, when study time was more limited, revision proceeded in a Bayesian manner, in that congruent and incongruent observations had equal impacts.

Adult↗
PubMed2006

Strategies in probabilistic categorization: results from a new way of analyzing performance.

The "Weather Prediction" task is a widely used task for investigating probabilistic category learning, in which various cues are probabilistically (but not perfectly) predictive of class membership. This means that a given combination of cues sometimes belongs to one class and sometimes to another. Prior studies showed that subjects can improve their performance with training, and that there is considerable individual variation in the strategies subjects use to approach this task. Here, we discuss a recently introduced analysis of probabilistic categorization, which attempts to identify the strategy followed by a participant. Monte Carlo simulations show that the analysis can, indeed, reliably identify such a strategy if it is used, and can identify switches from one strategy to another. Analysis of data from normal young adults shows that the fitted strategy can predict subsequent responses. Moreover, learning is shown to be highly nonlinear in probabilistic categorization. Analysis of performance of patients with dense memory impairments due to hippocampal damage shows that although these patients can change strategies, they are as likely to fall back to an inferior strategy as to move to more optimal ones.

Adult↗
PubMed2004

Probabilistic sequential independent components analysis.

Under-complete models, which derive lower dimensional representations of input data, are valuable in domains in which the number of input dimensions is very large, such as data consisting of a temporal sequence of images. This paper presents the under-complete product of experts (UPoE), where each expert models a one-dimensional projection of the data. Maximum-likelihood learning rules for this model constitute a tractable and exact algorithm for learning under-complete independent components. The learning rules for this model coincide with approximate learning rules proposed earlier for under-complete independent component analysis (UICA) models. This paper also derives an efficient sequential learning algorithm from this model and discusses its relationship to sequential independent component analysis (ICA), projection pursuit density estimation, and feature induction algorithms for additive random field models. This paper demonstrates the efficacy of these novel algorithms on high-dimensional continuous datasets.

Algorithms↗
PubMed1994

[Discrete dynamic systems: the effect of perceptual structuring on composition and transfer of knowledge about operating sequences].

This paper reports two experiments in which we explored the impact of perceptual grouping of elements on the organization and use of knowledge about how to operate a device. Experiment 1 explored the effects of different perceptual display regions on the creation of chunks when sequences of inputs had to be reproduced. The effects of regions were not homogeneous, but rather their influence depended on interactions between different modalities and learning conditions. Experiment 2 investigated the influence of grouping-induced composition of knowledge elements on the transfer of sequential knowledge. Two different learning criteria were used in the acquisition phase to manipulate the degree of composition of knowledge elements. In the transfer phase, subjects could transfer (1) the whole sequence of one region, (2) two partial sequences of adjacent regions, or (3) single components. It was found that regional invariance and immediate succession of components were both important for transfer performance. These results suggests that the temporal order of regions is important for the organization and use of sequential knowledge, and not the grouping of elements by itself.

Adult↗
PubMedDate not supplied

Self-organizing maps with recursive neighborhood adaptation.

Self-organizing maps (SOMs) are widely used in several fields of application, from neurobiology to multivariate data analysis. In that context, this paper presents variants of the classic SOM algorithm. With respect to the traditional SOM, the modifications regard the core of the algorithm, (the learning rule), but do not alter the two main tasks it performs, i.e. vector quantization combined with topology preservation. After an intuitive justification based on geometrical considerations, three new rules are defined in addition to the original one. They develop interesting properties such as recursive neighborhood adaptation and non-radial neighborhood adaptation. In order to assess the relative performances and speeds of convergence, the four rules are used to train several maps and the results are compared according to several error measures (quantization error and topology preservation criterions).

Algorithms↗
PubMed2004

Prefrontal cortex and decision making in a mixed-strategy game.

In a multi-agent environment, where the outcomes of one's actions change dynamically because they are related to the behavior of other beings, it becomes difficult to make an optimal decision about how to act. Although game theory provides normative solutions for decision making in groups, how such decision-making strategies are altered by experience is poorly understood. These adaptive processes might resemble reinforcement learning algorithms, which provide a general framework for finding optimal strategies in a dynamic environment. Here we investigated the role of prefrontal cortex (PFC) in dynamic decision making in monkeys. As in reinforcement learning, the animal's choice during a competitive game was biased by its choice and reward history, as well as by the strategies of its opponent. Furthermore, neurons in the dorsolateral prefrontal cortex (DLPFC) encoded the animal's past decisions and payoffs, as well as the conjunction between the two, providing signals necessary to update the estimates of expected reward. Thus, PFC might have a key role in optimizing decision-making strategies.

Animals↗