Respiratory pumping seizure: a newly discovered spontaneous stereotyped behavior pattern in the opisthobranch mollusc Aplysia californica

1990 ◽  
Vol 166 (5) ◽  
Author(s):  
JamesE. Kanz ◽  
WilliamD. Quast
1993 ◽  
Vol 174 (1) ◽  
pp. 375-380
Author(s):  
M. Martinez-Padron ◽  
K. Lukowiak

Periodic spontaneous gill movements (SGMs) are one of the most obvious of the 21 general action patterns described in the ethogram of Aplysia californica (Leonard and Lukowiak, 1986). SGMs are thought to be a prime component of Aplysia's respiratory cycle (Koester et al. 1974; Byrne and Koester, 1978) and in the intact animal the frequency of SGMs can be modified by changes in the partial pressures of CO2 and O2 of the sea water (Croll, 1985; Levy et al. 1989).


1985 ◽  
Vol 117 (1) ◽  
pp. 15-27
Author(s):  
ROGER P. CROLL

The frequency of respiratory pumping in Aplysia increases when CO2 is bubbled through the bathing sea water. Air, O2 and N2 do not have this effect. The sensitivity to CO2 may be mediated by receptors which are sensitive to pH changes within the range encountered during hypercapnia. In addition to the frequency change during hypercapnia, increases in the rate of pumping occur after titration to low pH with hydrochloric, acetic, nitric and sulphuric acids, thus indicating sensitivity to changes in the concentration of hydrogen ions and not to any specific anions. High pH and large deviations from normal in the tonicity of the sea water are ineffective in influencing the rate of pumping. The locus of pH sensitivity resides primarily within the mantle cavity. Lesions of the osphradium indicate that this chemosensory organ mediates a large degree of sensory control over respiratory pumping.


1999 ◽  
Vol 202 (17) ◽  
pp. 2371-2383
Author(s):  
R.S. Hewes

The ventromedial cells (VM cells) of the moth Manduca sexta belong to a peptide hormone signaling hierarchy that directs an episodic and stereotyped behavior pattern, ecdysis. The VM cells respond to declining ecdysteroid titers at the end of the final larval molt with a transcription-dependent decrease in spike threshold and the abrupt release of the previously stockpiled neuropeptide, eclosion hormone (EH). This report describes whole-cell patch-clamp recordings of acutely isolated VM cell somata made to identify membrane currents that may underlie the increase in VM cell excitability during EH release and that may contribute to abrupt peptide release. There were at least three voltage- and time-dependent conductances in the VM cells. The inward current was carried exclusively by a voltage-dependent inward Ca(2+) current (I(Ca)), and the outward currents were a combination of a Ca(2+)-dependent outward K(+) current (I(K(Ca))) and a transient, voltage-dependent outward K(+) current, the A current (I(A)). In current-clamp recordings, the currents present in the acutely isolated somata were sufficient to generate membrane properties similar to those observed in the VM cells in situ. This study represents the first step toward characterization of the mechanisms underlying the abrupt release of EH stores from the VM cells preceding ecdysis.


2020 ◽  
Vol 39 (6) ◽  
pp. 8927-8935
Author(s):  
Bing Zheng ◽  
Dawei Yun ◽  
Yan Liang

Under the impact of COVID-19, research on behavior recognition are highly needed. In this paper, we combine the algorithm of self-adaptive coder and recurrent neural network to realize the research of behavior pattern recognition. At present, most of the research of human behavior recognition is focused on the video data, which is based on the video number. At the same time, due to the complexity of video image data, it is easy to violate personal privacy. With the rapid development of Internet of things technology, it has attracted the attention of a large number of experts and scholars. Researchers have tried to use many machine learning methods, such as random forest, support vector machine and other shallow learning methods, which perform well in the laboratory environment, but there is still a long way to go from practical application. In this paper, a recursive neural network algorithm based on long and short term memory (LSTM) is proposed to realize the recognition of behavior patterns, so as to improve the accuracy of human activity behavior recognition.


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