[Show abstract][Hide abstract] ABSTRACT: High-frequency (e.g., gamma 30 to 50 Hz) coherent neural activity has been postulated to underlie binding of independent neural assemblies and thus integrate processing across distributed neuronal networks to achieve a unified conscious experience. Prior studies suggest that gamma activity may play a role in perceptual mechanisms, but design limitations raise concerns. Thus, controversy exists as to the hypothesis that gamma activity is necessary for perceptual awareness. In addition, controversy exists as to whether the primary sensory cortices are involved directly in the mechanisms of conscious perception or just in processes prior to conscious awareness.
To investigate the relation of gamma coherence and perception.
Digital intracranial electrocorticographic recordings from implanted electrodes were obtained in six patients with intractable epilepsy during a simple somatosensory detection task for near-threshold stimuli applied to the contralateral hand. Signal analyses were then conducted using a quantitative approach that employed two-way Hanning digital bandpass filters to compute running correlations across pairs of channels at various time epochs for each patient and each perception state across multiple bandwidths.
Gamma coherence occurs in the primary somatosensory cortex approximately 150 to 300 milliseconds after contralateral hand stimuli that are perceived, but not for nonperceived stimuli, which did not differ in character/intensity or early somatosensory evoked potentials.
The results are consistent with the possible direct involvement of primary sensory cortex in elemental awareness and with a role for gamma coherence in conscious perception.
[Show abstract][Hide abstract] ABSTRACT: This paper describes and compares several nonlinear
decision-making systems, including multilayer perceptrons, wavelet
neural networks, polynomial neural networks, and fuzzy decision
models. The applicability of these systems is illustrated through
the problem of check authorization from incomplete data. A
benchmark is established in terms of classical linear discriminant
analysis and Bayes quadratic classification, in order to assess the
need for the neuro-fuzzy strategies. An overall improvement of
around 10 percentage points in classification accuracy on an
independent test set is demonstrated for each of the neuro-fuzzy
models over conventional statistical techniques. In addition to
classification accuracy, five performance measures are reported:
accuracy in dollar terms, robustness, parametric efficiency,
training computational expense, and classification balance. Even
though each system performs differently on these measures, any
neuro-fuzzy model is recommended over traditional techniques in
problems such as check authorization, where the improvement in
reliability warrants the added cost of implementation.
Journal of Intelligent and Fuzzy Systems. 01/1998; 6:259-278.
[Show abstract][Hide abstract] ABSTRACT: We describe the application of a multilayer perceptron, polynomial neural networks, and a fuzzy decision model to the problem of check approval from incomplete data. A simple benchmark case was established as a performance metric to compare the various non-linear strategies that were implemented. An overall improvement of at least 10% was obtained in each of these methods. I. Introduction By the turn of the century, almost half of all consumer payments are expected to be made by checks . This trend is making more and more merchants rely on checkguarantee and check-authorization services provided by specialized companies that help them manage the associated increased risk. These companies maintain large databases with information such as customers' names, fraudulent driver's licenses, and Social Security numbers. Stores with connections to an on-line system, can have checks approved or rejected in a matter of seconds. Despite the steady increase in the availability of these services,...
[Show abstract][Hide abstract] ABSTRACT: The difficulties associated with translating a set of scores into
letter-grades are discussed. A novel method for automating this process,
the Fuzzy Grading System, is developed and compared to traditional
practices. Letter-grades are recognized to be fuzzy descriptors of
students' performance. Thus, operations aimed at defining letter-grade
boundaries are naturally carried out in the context of fuzzy set theory
and logic. The Fuzzy Grading System utilizes students' and instructor's
performance measures in order to modify a set of collectively approved,
a priori fuzzy grades, so as to produce a “fair” mark
distribution. The validity of grades is increased by compensating for
factors that are not directly accessible to simpler, traditional grading
IEEE Transactions on Education 06/1995; · 1.22 Impact Factor