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Abstract Eugenol, the major active ingredient of clove oil, is widely used for anesthesia in fish. Yet virtually nothing is known about its effects on CNS functions, and thus about potential interference with neurophysiological experimentation. To address this issue, we employed a neuro-behavioral assay recently developed for testing of water-soluble anesthetic agents. The unique feature of thisin-vivotool is that it utilizes a readily accessible behavior, the electric organ discharge (EOD), as a proxy of the neural activity generated by a brainstem oscillator, the pacemaker nucleus, in the weakly electric fishApteronotus leptorhynchus. A deep state of anesthesia, as assessed by the cessation of locomotor activity, was induced within less than 3 min at concentrations of 30–60 µL/L eugenol. This change in locomotor activity was paralleled by a dose-dependent, pronounced decrease in EOD frequency. After removal of the fish from the anesthetic solution, the frequency returned to baseline levels within 30 min. Eugenol also led to a significant increase in the rate of ‘chirps,’ specific amplitude/frequency modulations of the EOD, during the 30 min after the fish’s exposure to the anesthetic. At 60 µL/L, eugenol induced a collapse of the EOD amplitude after about 3.5 min in half of the fish tested. The results of our study indicate strong effects of eugenol on CNS functions. We hypothesize that these effects are mediated by the established pharmacological activity of eugenol to block the generation of action potentials and to reduce the excitability of neurons; as well as to potentiate GABAA-receptor responses.
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Supervised learning algorithm for analysis of communication signals in the weakly electric fish Apteronotus leptorhynchus
Abstract Signal analysis plays a preeminent role in neuroethological research. Traditionally, signal identification has been based on pre-defined signal (sub-)types, thus being subject to the investigator’s bias. To address this deficiency, we have developed a supervised learning algorithm for the detection of subtypes of chirps—frequency/amplitude modulations of the electric organ discharge that are generated predominantly during electric interactions of individuals of the weakly electric fishApteronotus leptorhynchus. This machine learning paradigm can learn, from a ‘ground truth’ data set, a function that assigns proper outputs (here: time instances of chirps and associated chirp types) to inputs (here: time-series frequency and amplitude data). By employing this artificial intelligence approach, we have validated previous classifications of chirps into different types and shown that further differentiation into subtypes is possible. This demonstration of its superiority compared to traditional methods might serve as proof-of-principle of the suitability of the supervised machine learning paradigm for a broad range of signals to be analyzed in neuroethology.
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- Award ID(s):
- 1946910
- PAR ID:
- 10528094
- Publisher / Repository:
- Springer Nature
- Date Published:
- Journal Name:
- Journal of Comparative Physiology A
- Volume:
- 210
- Issue:
- 3
- ISSN:
- 0340-7594
- Page Range / eLocation ID:
- 443 to 458
- Subject(s) / Keyword(s):
- Signal analysis Artificial intelligence Supervised learning Chirping behavior Weakly electric fish Apteronotus leptorhynchus
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1007/s00359-023-01664-4
