Biomedical subjects

V R de Sa

Publications and source records attributed to V R de Sa.

3 recordsLinked to original sources

PubMed1998

Category learning through multimodality sensing.

Humans and other animals learn to form complex categories without receiving a target output, or teaching signal, with each input pattern. In contrast, most computer algorithms that emulate such performance assume the brain is provided with the correct output at the neuronal level or require grossly unphysiological methods of information propagation. Natural environments do not contain explicit labeling signals, but they do contain important information in the form of temporal correlations between sensations to different sensory modalities, and humans are affected by this correlational structure (Howells, 1944; McGurk & MacDonald, 1976; MacDonald & McGurk, 1978; Zellner & Kautz, 1990; Durgin & Proffitt, 1996). In this article we describe a simple, unsupervised neural network algorithm that also uses this natural structure. Using only the co-occurring patterns of lip motion and sound signals from a human speaker, the network learns separate visual and auditory speech classifiers that perform comparably to supervised networks.

Algorithms↗

PubMed1998

Cascaded redundancy reduction.

We describe a method for incrementally constructing a hierarchical generative model of an ensemble of binary data vectors. The model is composed of stochastic, binary, logistic units. Hidden units are added to the model one at a time with the goal of minimizing the information required to describe the data vectors using the model. In addition to the top-down generative weights that define the model, there are bottom-up recognition weights that determine the binary states of the hidden units given a data vector. Even though the stochastic generative model can produce each data vector in many ways, the recognition model is forced to pick just one of these ways. The recognition model therefore underestimates the ability of the generative model to predict the data, but this underestimation greatly simplifies the process of searching for the generative and recognition weights of a new hidden unit.

Algorithms↗

PubMed1997

On the nature and scope of featural representations of word meaning.

Behavioral experiments and a connectionist model were used to explore the use of featural representations in the computation of word meaning. The research focused on the role of correlations among features, and differences between speeded and untimed tasks with respect to the use of featural information. The results indicate that featural representations are used in the initial computation of word meaning (as in an attractor network), patterns of feature correlations differ between artifacts and living things, and the degree to which features are intercorrelated plays an important role in the organization of semantic memory. The studies also suggest that it may be possible to predict semantic priming effects from independently motivated featural theories of semantic relatedness. Implications for related behavioral phenomena such as the semantic impairments associated with Alzheimer's disease (AD) are discussed.

Adult↗