[Periodic ventilation and respiratory system structure: nonquantified simulation by analog computer].

G Matisse; J Lacoste

Home/Archive/PubMed

PubMed · 5522018

[Periodic ventilation and respiratory system structure: nonquantified simulation by analog computer].

G Matisse·J Lacoste

View original source ↗Export metadata ↓

Abstract

The source did not provide an abstract. Follow the original record for more information.

Explore related subjects

Computers, Analog Humans Methods Models, Neurological Models, Theoretical Periodicity Respiration Respiratory Physiological Phenomena Respiratory System Journal Article

Keep this discovery

Explore connections, maps & timelines

BibTeX RIS

G Matisse, J Lacoste. [Periodic ventilation and respiratory system structure: nonquantified simulation by analog computer].. https://pubmed.ncbi.nlm.nih.gov/5522018/

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related citations

PubMed2006

Analog neural network for support vector machine learning.

An analog neural network for support vector machine learning is proposed, based on a partially dual formulation of the quadratic programming problem. It results in a simpler circuit implementation with respect to existing neural solutions for the same application. The effectiveness of the proposed network is shown through some computer simulations concerning benchmark problems.

Computers, Analog↗

PubMed2006

Design and implementation of multipattern generators in analog VLSI.

In recent years, computational biologists have shown through simulation that small neural networks with fixed connectivity are capable of producing multiple output rhythms in response to transient inputs. It is believed that such networks may play a key role in certain biological behaviors such as dynamic gait control. In this paper, we present a novel method for designing continuous-time recurrent neural networks (CTRNNs) that contain multiple embedded limit cycles, and we show that it is possible to switch the networks between these embedded limit cycles with simple transient inputs. We also describe the design and testing of a fully integrated four-neuron CTRNN chip that is used to implement the neural network pattern generators. We provide two example multipattern generators and show that the measured waveforms from the chip agree well with numerical simulations.

Computers, Analog↗

PubMed2004

An analog neural network implementation in fixed time of adjustable-order statistic filters and applications.

In this paper, we show a neural network implementation in fixed time of adjustable order statistic filters, including sorting, and adaptive-order statistic filters. All these networks accept an array of N numbers Xi = S(Xi)M(Xi)2E(Xi) as input (where S(Xi) is the sign of Xi, M(Xi) is the mantissa normalized to m digits, and Ex is the exponent) and employ two kinds of neurons, the linear and the threshold-logic neurons, with only integer weights (most of the weights being just +1 or -1) and integer threshold. Therefore, this will greatly facilitate the actual hardware implementation of the proposed neural networks using currently available very large scale integration technology. An application of using minimum filter in implementing a special neural network model neural network classifier (NNC) is given. With a classification problem of l classes C1, C2,.. ., C1, NNC classifies in fixed time an unknown vector to one class using a minimum-distance classification technique.

Computers, Analog↗