Biomedical subjects

Paul Frenger

Publications and source records attributed to Paul Frenger.

5 recordsLinked to original sources

PubMed2006

Human hormone function emulator.

This paper describes the addition of simulated hormone action to the author's modular, open-systems, computerized human nervous system function emulator. For this project he revived his 32-year old design for a pulse-integrating artificial neuron with controlled voltage droop and reset, to serve as the principal linear computational element. This neuron sums bursts of quantized presynaptic impulses to produce a simulated altered cell membrane voltage. When coupled with a variable-threshold Schmitt trigger, voltage changes can initiate a mock action potential signal. These altered neural membrane voltages and action potential signals are utilized in the nervous system emulator to qualitatively emulate the level of simulated hormone activity, to react to changes in the electrochemical environment, and to signal initiation of high level behavioral responses in the artificial intelligence system.

Action Potentials↗

PubMed2005

Simplest ever digital / analog neuron: "SEDAN-6".

The author continues with his series on artificial neuron construction. Using the smallest commercially available microcontroller, a 6-pin 8-bit unit, he has created several types of McCulloch-Pitts neurons and logic elements suitable for inclusion into various kinds of artificial neural networks. Additionally, by employing the on-chip analog comparator and simple off-chip stratagems, he has also implemented Hebb neurons and his android emotion emulator. These designs constitute the simplest artificial neurons which can be embodied using a microcontroller, and which are suitable for a wide variety of applications.

Biomimetic Materials↗

PubMed2004

A reduced ambiguity lexical system.

Natural human languages have proven to be sub-optimal in artificial intelligence applications because of their tendency to inexact representation of meaning. The author has devised a technique for converting human language to and from a compact byte-coded intermediate representation, which is processed more easily by computer systems. A specialized lexical engine based on IEEE Standard 1275-1994 was created to embed redundant information invisibly within the byte-coded text stream, to enable use of a variety of alphabets, grammars, and pronunciation rules (including slang and regional dialects). Very large vocabularies in a variety of human languages are supported. These lexical tools are designed to facilitate speech recognition and speech synthesis subsystems, universal translators and machine intelligence systems.

Algorithms↗

PubMed2003

YADCLAN: yet another digitally-controlled linear artificial neuron.

This paper updates the author's 1999 RMBS presentation on digitally controlled linear artificial neuron design. Each neuron is based on a standard operational amplifier having excitatory and inhibitory inputs, variable gain, an amplified linear analog output and an adjustable threshold comparator for digital output. This design employs a 1-wire serial network of digitally controlled potentiometers and resistors whose resistance values are set and read back under microprocessor supervision. This system embodies several unique and useful features, including: enhanced neuronal stability, dynamic reconfigurability and network extensibility. This artificial neuronal is being employed for feature extraction and pattern recognition in an advanced robotic application.

Amplifiers, Electronic↗

PubMed2002

Nanocontroller update: building a better artificial neuron.

Recent progress in microprocessor design has produced sophisticated 8-bit single-chip microcontrollers in small packages. These user-programmable "nanocontrollers", some with as few as eight pins, now include a variety of linear on-chip components. Miniscule complex mixed digital and analog systems are now possible. This paper outlines some of these advances, then describes how using these new microcontroller features to create a better artificial neuron have improved the author's ten-year-old neural network design.

Analog-Digital Conversion↗