Spatial learning through active electroreception in Gnathonemus petersii

Jung, Sarah Nicola; Künzel, Silke; Engelmann, Jacob

doi:10.1016/j.anbehav.2019.06.029

Cited by 7 publications

(3 citation statements)

References 43 publications

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“…Pedraja et al., 2021 ) and spatial navigation (e.g. Jung et al., 2019 ; Schumacher, von der Emde, et al., 2017 ). The importance of the active electric sense to everyday life in these animals, the amount of documentation on how their active electric sense works, and their locomotor patterns ( more on this later ) makes G. petersii an ideal candidate for understanding how these systems coordinate activity, and enable them to behave adaptively.…”

Section: Gnathonemus Petersii Is An Excellent Model For Stud...mentioning

confidence: 99%

Mormyrid fish as models for investigating sensory‐motor integration: A behavioural perspective

2023

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Animals possess senses which gather information from their environment. They can tune into important aspects of this information and decide on the most appropriate response, requiring coordination of their sensory and motor systems. This interaction is bidirectional. Animals can actively shape their perception with selfdriven motion, altering sensory flow to maximise the environmental information they are able to extract. Mormyrid fish are excellent candidates for studying sensory-motor interactions, because they possess a unique sensory system (the active electric sense) and exhibit notable behaviours that seem to be associated with electrosensing. This review will take a behavioural approach to unpicking this relationship, using active electrolocation as an example where body movements and sensing capabilities are highly related and can be assessed in tandem. Active electrolocation is the process where individuals will generate and detect lowvoltage electric fields to locate and recognise nearby objects. We will focus on research in the mormyrid Gnathonemus petersii (G. petersii), given the extensive study of this species, particularly its object recognition abilities. By studying object detection and recognition, we can assess the potential benefits of self-driven movements to enhance selection of biologically relevant information. Finally, these findings are highly relevant to understanding the involvement of movement in shaping the sensory experience of animals that use other sensory modalities. Understanding the overlap between sensory and motor systems will give insight into how different species have become adapted to their environments.

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Section: Gnathonemus Petersii Is An Excellent Model For Stud...mentioning

confidence: 99%

Mormyrid fish as models for investigating sensory‐motor integration: A behavioural perspective

2023

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“…With respect to electrolocation, it is known that weakly electric fish electrically locate objects in the dark and even discriminate between objects relying on various object features (von der Emde, 1990 , 1999 ; von der Emde and Bleckmann, 1998 ; Schwarz and von der Emde, 2000 ). Furthermore, it has been investigated that electric fish also use their electric sense for spatial navigation (Jun et al, 2016 ; Jung et al, 2019 ). The role of EODs in electrocommunication has also been studied although these studies until recently were constrained to lab conditions (Moller, 1970 ; Walz et al, 2013 ).…”

Section: Introductionmentioning

confidence: 99%

The Use of Supervised Learning Models in Studying Agonistic Behavior and Communication in Weakly Electric Fish

Pedraja

Herzog

Engelmann

et al. 2021

Front. Behav. Neurosci.

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Despite considerable advances, studying electrocommunication of weakly electric fish, particularly in pulse-type species, is challenging as very short signal epochs at variable intervals from a few hertz up to more than 100 Hz need to be assigned to individuals. In this study, we show that supervised learning approaches offer a promising tool to automate or semiautomate the workflow, and thereby allowing the analysis of much longer episodes of behavior in a reasonable amount of time. We provide a detailed workflow mainly based on open resource software. We demonstrate the usefulness by applying the approach to the analysis of dyadic interactions of Gnathonemus petersii. Coupling of the proposed methods with a boundary element modeling approach, we are thereby able to model the information gained and provided during agonistic encounters. The data indicate that the passive electrosensory input, in particular, provides sufficient information to localize a contender during the pre-contest phase, fish did not use or rely on the theoretically also available sensory information of the contest outcome-determining size difference between contenders before engaging in agonistic behavior.

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“…Conductive objects, such as prey, induce a localized increase of the electric field, thereby evoking an increase of tuberous receptor spike rate. Active electrosensing by mormyrid fish is important for navigation and prey capture, as well as for perceptual [3] and spatial learning [4].…”

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confidence: 99%

Neural Networks: How a Multi-Layer Network Learns to Disentangle Exogenous from Self-Generated Signals

Maler

2020

Current Biology

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about MEK-ERK docking, it is entirely plausible to think that there is a mix of processive and distributive phosphorylation, depending on the probability of catalysis versus the probability of undocking. Mutants that change the former but not the latter would be expected to increase both the processivity and the overall rate of ERK activation. It will be interesting to see if other activated MEK mutants follow this pattern.

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Spatial learning through active electroreception in Gnathonemus petersii

Cited by 7 publications

References 43 publications

Mormyrid fish as models for investigating sensory‐motor integration: A behavioural perspective

Mormyrid fish as models for investigating sensory‐motor integration: A behavioural perspective

The Use of Supervised Learning Models in Studying Agonistic Behavior and Communication in Weakly Electric Fish

Neural Networks: How a Multi-Layer Network Learns to Disentangle Exogenous from Self-Generated Signals

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