Artificial Intelligence

41.

Recognizing complex patterns.

How the brain recognizes complex patterns in the environment is a central, but little understood question in neuroscience. The problem is of great significance for a host of applications such as biometric-based access control, autonomous robots and ...
Pawan Sinha (Nat Neurosci, 200211)
recognizing-complex-patterns.asp

42.

An intelligent biological information management system.

MOTIVATION: As biomedical researchers are amassing a plethora of information in a variety of forms resulting from the advancements in biomedical research, there is a critical need for innovative information management and knowledge discovery tools to ...
Mathew Palakal, Snehasis Mukhopadhyay, Javed Mostafa, Rajeev Raje, Mathias NCho, Santosh Mishra (Bioinformatics, 200210)
intelligent-biological-information-management-system.asp

43.

Manual and automated measurement of the whole thalamus and mediodorsal nucleus using magnetic resonance imaging.

The thalamus is an important relay structure in the brain that may be relevant to a variety of brain diseases. It is divided into multiple subnuclei with different cortical connections. The medial dorsal (MD) nucleus is particularly important because it ...
Ruth Spinks, Vincent A Magnotta, Nancy C Andreasen, Karen C Albright, Steven Ziebell, Peggy Nopoulos, Martin Cassell (Neuroimage, 200210)
manual-automated-measurement-whole-thalamus-mediodorsal-nucleus.asp

44.

Disruptive visions.

Numerous advanced technologies, both medical and nonmedical, are emerging faster than their social, behavioral, political, moral, and ethical implications can be understood. Some of these technologies will fundamentally challenge the practice of surgery: ...
R M Satava (Surg Endosc, 200210)
disruptive-visions.asp

45.

Linear spatial integration for single-trial detection in encephalography.

Conventional analysis of electroencephalography (EEG) and magnetoencephalography (MEG) often relies on averaging over multiple trials to extract statistically relevant differences between two or more experimental conditions. In this article we ...
Lucas Parra, Chris Alvino, Akaysha Tang, Barak Pearlmutter, Nick Yeung, Allen Osman, Paul Sajdal (Neuroimage, 200209)
linear-spatial-integration-single-trial-detection-encephalography.asp

46.

On the emergence of rules in neural networks.

A simple associationist neural network learns to factor abstract rules (i.e., grammars) from sequences of arbitrary input symbols by inventing abstract representations that accommodate unseen symbol sets as well as unseen but similar grammars. The neural ...
Stephen José Hanson, Michiro Negishi (Neural Comput, 200209)
emergence-rules-neural-networks.asp

47.

Learning nonregular languages: a comparison of simple recurrent networks and LSTM.

In response to Rodriguezs recent article (2001), we compare the performance of simple recurrent nets and long short-term memory recurrent nets on context-free and context-sensitive ...
J Schmidhuber, F Gers, D Eck (Neural Comput, 200209)
learning-nonregular-languages-comparison-simple-recurrent-networks.asp

48.

A discrete speech recognition system for dermatology: 8 years of daily experience in a medical dermatology office.

A discrete speech recognition system was customized to recognize approximately 9,000 phrases and terms commonly used in dermatology, and has been used on a daily basis since 1984 to produce as many as 65 consult and follow up letters daily. The systems ...
Kevin C Smith (Semin Cutan Med Surg, 200209)
discrete-speech-recognition-system-dermatology-years-daily.asp

49.

Concept mapping: a tool to bridge the disciplinary divide.

The American College of Womens Health Physicians has been exploring an on-line educational tool-concept mapping-to facilitate the development of an interdisciplinary and woman-centered womens health curriculum, and to implement The Womens Health Care ...
Eileen Hoffman, Justina Trott, Katherine Patterson Neely (Am J Obstet Gynecol, 200209)
concept-mapping-tool-bridge-disciplinary-divide.asp

50.

Preintegration lateral inhibition enhances unsupervised learning.

A large and influential class of neural network architectures uses postintegration lateral inhibition as a mechanism for competition. We argue that these algorithms are computationally deficient in that they fail to generate, or learn, appropriate ...
M W Spratling, M H Johnson (Neural Comput, 200209)
preintegration-lateral-inhibition-enhances-unsupervised-learning.asp