Prototype Analysis in Hopfield Networks With Hebbian Learning.

We discuss prototype formation in the Hopfield network. Typically, Hebbian learning with highly correlated states leads to degraded memory performance. We show that this type of learning can lead to prototype formation, where unlearned states emerge as representatives of large correlated subsets of states, alleviating capacity woes.

Contributions by metaplasticity to solving the Catastrophic Forgetting Problem.

Catastrophic forgetting (CF) refers to the sudden and severe loss of prior information in learning systems when acquiring new information. CF has been an Achilles heel of standard artificial neural networks (ANNs) when learning multiple tasks sequentially. The brain, by contrast, has solved this problem during evolution.

Hopfield networks as a model of prototype-based category learning: A method to distinguish trained, spurious, and prototypical attractors.

We present an investigation of the potential use of Hopfield networks to learn neurally plausible, distributed representations of category prototypes. Hopfield networks are dynamical models of autoassociative memory which learn to recreate a set of input states from any given starting state. These networks, however, will almost always learn states which were not presented during training, so called spurious states.

Preschool children's environmental exposures: a case-control epidemiological study of the presence of asthma-like symptoms.

The heterogeneity of asthma and asthma-like symptoms results in difficulty defining, diagnosing, and therefore estimating environmental exposures and associations with wheezing or asthma-like symptoms. Determining the disease burden for young children is particularly challenging. In the study described in this article, counter-matched sampling design was used to select participants from the Woman, Infants, and Children (WIC) program for this nested case-control study (N = 691, with 412 controls).

Neural network modelling and dynamical system theory: are they relevant to study the governing dynamics of association football players?

Recent studies have explored the organization of player movements in team sports using a range of statistical tools. However, the factors that best explain the performance of association football teams remain elusive. Arguably, this is due to the high-dimensional behavioural outputs that illustrate the complex, evolving configurations typical of team games.

Visualizing changes in lower body coordination with different types of foot orthoses using self-organizing maps (SOM).

Human movement involves the coordination of individual segments controlled by the central nervous system and powered by the muscles. However, visualization of this high-dimensional coordination between kinematic and kinetic parameters is challenging. The purposes of this study were (a) to identify differences in lower extremity coordination between different types of foot orthoses using Kohonen self-organizing maps (SOM) and (b) to demonstrate the SOM visualization of high-dimensional coordination in gait.

Artificial neural networks for analyzing inter-limb coordination: the golf chip shot.

Motor control research relies on theories, such as coordination dynamics, adapted from physical sciences to explain the emergence of coordinated movement in biological systems. Historically, many studies of coordination have involved inter-limb coordination of relatively few degrees of freedom. This study looked at the high-dimensional inter-limb coordination used to perform the golf chip shot toward six different target distances.

Beyond the cathedral: building trust to engage the African American community in health promotion and disease prevention.

Effective efforts to eliminate health disparities must be grounded in strong community partnerships and trusting relationships between academic institutions and minority communities. However, there are often barriers to such efforts, including the frequent need to rely on time-limited funding mechanisms that take categorical approaches. This article provides an overview of health promotion and disease prevention projects implemented through the Community Outreach and Information Dissemination Core (COID) of the Center for Minority Health, within the Graduate School of Public Health at the University of Pittsburgh.

Iron overload in the Asian community.

Hereditary hemochromatosis is an iron overload disorder that can lead to the impairment of multiple organs and is caused by mutations in one or more different genes. Type 1 hemochromatosis is the most common form of the disease and results from mutations in the HFE gene. Juvenile hemochromatosis (JH) is the most severe form, usually caused by mutations in hemojuvelin (HJV) or hepcidin (HAMP).

A model of cardinality blindness in inferotemporal cortex.

A classifier is cardinality invariant if it can classify more than one token of a single type at a time. We present a convolutional neural network (CNN) model of inferotemporal cortex (IT) and show that it is cardinality invariant. While the CNN is designed with translation invariance in mind, cardinality invariance is an emergent property.

Memory retention--the synaptic stability versus plasticity dilemma.

Memory maintenance is widely believed to involve long-term retention of the synaptic weights that are set within relevant neural circuits during learning. However, despite recent exciting technical advances, it has not yet proved possible to confirm experimentally this intuitively appealing hypothesis. Artificial neural networks offer an alternative methodology as they permit continuous monitoring of individual connection weights during learning and retention.

A robust method for distinguishing between learned and spurious attractors.

Hopfield/constraint satisfaction type networks can be used to learn (autoassociate) patterns. Random inputs to the network will sometimes converge on states which are learned patterns, and sometimes converge on states which are unlearned/spurious. It would be useful for many reasons to be able to tell whether or not a given state was learned or spurious.