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Biomedical subjects

G Tesauro

Publications and source records attributed to G Tesauro.

10 recordsLinked to original sources
PubMed2001

The changing face of cancer survivorship.

OBJECTIVES: To provide a review of who is surviving cancer diagnosed within the last 20 years and key areas for research development related to cancer survivorship. DATA SOURCES: Articles, studies, and Surveillance, Epidemiology and End Results statistics. CONCLUSIONS: The information we have on today's survivors must be periodically revisited and revised to equip cancer patients with the knowledge and tools they need to master the new realities of their survivorship. IMPLICATIONS FOR NURSING PRACTICE: With the increasing numbers of individuals being cured of or living long periods of time following a diagnosis of cancer, oncology nurses who work with cancer survivors must maintain their knowledge of the issues and practices critical to the well-being of the patient.

Adult↗
PubMed1988

A plausible neural circuit for classical conditioning without synaptic plasticity.

The cellular bases of learning are currently under active investigation by both experimental and theoretical means. In this paper, a simple neuronal wiring diagram is proposed that can reproduce both simple and higher-order behavioral paradigms seen in invertebrate classical conditioning experiments. Learning in this model does not take place by modification of synaptic strength values. Instead, the model uses a layer of interneurons with modifiable thresholds for spike initiation, as suggested by the plasticity mechanisms thought to operate in Hermissenda [Alkon, D. L. (1983) Sci. Am. 249, 70-84]. The model therefore has an advantage in plausibility compared with more standard models using Hebb synapses or their functional equivalents, which have not yet been demonstrated in any invertebrate organism.

Animals↗
PubMed1986

Simple neural models of classical conditioning.

A systematic study of the necessary and sufficient ingredients of a successful model of classical conditioning is presented. Models are constructed along the lines proposed by Gelperin, Hopfield, and Tank, who showed that many conditioning phenomena could be reproduced in a model using non-trivial distributed representations of the sensory stimuli. The additional phenomena of extinction and blocking are found to be obtainable by generalizing the Hebbian learning algorithm, rather than by additional complications in the hardware. The most successful algorithms have a minimal number of adjustable parameters, and require only local-time information about the level of postsynaptic activity. The proper behavior of these algorithms is verified by both simple analytic arguments and by direct numerical simulation. Certain detailed assumptions concerning the distributed sensory representations are also found to have a surprising degree of importance.

Algorithms↗