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Biomedical subjects

R M Neal

Publications and source records attributed to R M Neal.

8 recordsLinked to original sources
PubMed1997

Factor analysis using delta-rule wake-sleep learning.

We describe a linear network that models correlations between real-valued visible variables using one or more real-valued hidden variables-a factor analysis model. This model can be seen as a linear version of the Helmholtz machine, and its parameters can be learned using the wake-sleep method, in which learning of the primary generative model is assisted by a recognition model, whose role is to fill in the values of hidden variables based on the values of visible variables. The generative and recognition models are jointly learned in wake and sleep phases, using just the delta rule. This learning procedure is comparable in simplicity to Hebbian learning, which produces a somewhat different representation of correlations in terms of principal components. We argue that the simplicity of wake-sleep learning makes factor analysis a plausible alternative to Hebbian learning as a model of activity-dependent cortical plasticity.

Factor Analysis, Statistical↗
PubMed1995

The "wake-sleep" algorithm for unsupervised neural networks.

An unsupervised learning algorithm for a multilayer network of stochastic neurons is described. Bottom-up "recognition" connections convert the input into representations in successive hidden layers, and top-down "generative" connections reconstruct the representation in one layer from the representation in the layer above. In the "wake" phase, neurons are driven by recognition connections, and generative connections are adapted to increase the probability that they would reconstruct the correct activity vector in the layer below. In the "sleep" phase, neurons are driven by generative connections, and recognition connections are adapted to increase the probability that they would produce the correct activity vector in the layer above.

Algorithms↗
PubMed1995

The Helmholtz machine.

Discovering the structure inherent in a set of patterns is a fundamental aim of statistical inference or learning. One fruitful approach is to build a parameterized stochastic generative model, independent draws from which are likely to produce the patterns. For all but the simplest generative models, each pattern can be generated in exponentially many ways. It is thus intractable to adjust the parameters to maximize the probability of the observed patterns. We describe a way of finessing this combinatorial explosion by maximizing an easily computed lower bound on the probability of the observations. Our method can be viewed as a form of hierarchical self-supervised learning that may relate to the function of bottom-up and top-down cortical processing pathways.

Algorithms↗
PubMed1994

Screening for anxiety and depression in elderly medical outpatients.

In a study of 45 consecutive new outpatients at geriatric medicine clinics, 17.8% were diagnosed as depressed and 2.2% as anxious using the Geriatric Mental Status Schedule. Of two screening instruments, the Geriatric Depression Scale (GDS), in either 30-item or 15-item version, performed well and the depression sub-scale of Goldberg and Bridges' screening questionnaire for depression and anxiety in medical settings was adequate. The anxiety sub-scale of the latter was poor. Detection by geriatricians of depression and anxiety disorders was poor. It is recommended that a short screening instrument for the detection of depression, such as the GDS, be incorporated into the clinic setting. As yet there is no satisfactory screening questionnaire for detecting anxiety disorders.

Aged↗
PubMed1977

Time as a factor in atmospheric sampling.

Differences in results of simultaneous air monitoring of ozone with three different methods in the field are described. The argument is advanced that the differences are due largely to sampling turbulent atmospheres with instruments utilizing different sampling intervals and time constants. It is concluded that when sampling natural turbulent atmospheres, a stable mean value can only be achieved through integrated sampling periods of greater than ten minutes.

Air↗
PubMedDate not supplied

Liquid chromatographic determination of sulfamethazine in feeds.

A reverse-phase liquid chromatographic method for the assay of sulfamethazine (SMZ) in feeds is described. Feed samples are extracted with 50% methanol solution, centrifuged, filtered, and diluted when necessary, and chromatographed on a C-18 column. Samples are eluted with a mobile phase of 20% methanol and 80% of a solution containing acetic acid and tetramethylammonium chloride. The average recovery from spiked samples was 97.2% with a coefficient of variation of 1.2%. Linearity was very good (correlation coefficient 0.9997). Within-day and between-day coefficients of variation averaged 1.3 and 2.6%, respectively. The results for samples assayed by this method compared closely with the results from the same extracts assayed by the AOAC colorimetric method.

Animal Feed↗