In a sense, electrolocation is similar to echolocation. Animals assess the environment by actively sending out signals and monitoring feedback instead of passively using external energy sources such as sunlight. Therefore, electrolocation animals can work in the dark, and they gain this advantage by constantly consuming energy on signal transmission. When processing sensory information, the electrical positioning system must distinguish the feedback related to its signal from the interference sensation caused by other animal signals. This special situation raises many challenging questions for behavioral and neurophysiological research.
Working Principles of Electrolocation
- Model animal
- Working principles
The behavioral output of the electric fish appears in the form of electrical organ discharge, and the stimulus input appears in the form of electrical impulses. The simplicity of the two makes the electric fish an excellent model for neurobehavioral research.
During the active discharge process, the electric fish pass through a special electroreceptor in the tail for a short time discharge, which is distributed on the precursor surface to monitor the local transcutaneous current related to electrical organ discharge (EOD). An object with a different impedance than the surrounding water will distort the electric field, thereby changing the pattern of transcutaneous current intensity, most notably in the surface area closest to the object. By monitoring local changes in electroreceptor activity, they can perceive the surrounding environment to discover any food sources and potential hazards nearby.
Fig.1 Active electrolocation and passive electrolocation in electric fish. (Yilmaz, 2020)
Application of Electrolocation
- Sensory motor behavior analysis
Through the electrolocation imaging of the inductive transverse lobe neurons in the brain under static conditions of the weakly electric fish, it was found that animals can approach new objects spontaneously. The change of sensory motor behavior contributes to the formation of sensory input, thereby generating in-depth information. Motor control can be an active part of sensory learning, so a better understanding of the neurons that guide sensory learning may lead to a better understanding of sensory motor integration, behavior changes, and general learning.
Fig.2 Changes in electrolocation image and motor behavior. (Pedraja, 2020)
- Coronary plaque imaging and analysis
Coronary artery disease is characterized by atherosclerotic plaques growing on the walls of blood vessels, which inhibit blood flow and cause hypoxia in the heart muscle. Preventive assessment focuses on the key size of structural plaque parameters to identify high-risk plaques called thin cap fibroids. The catheter system is constructed based on the principle of active electrolocation of weakly electric fish, and the electrical image evoked by the plaque is projected on the surface of the catheter. Synthetic plaques with critical cap thickness have been detected and localized in porcine coronary arteries, opening a new way for the understanding of human nervous system-related diseases.
Recent studies have shown that behavior-related sensory information can be generated from strong patterns of sensory motor behavior (electric fish use sensory motion to generate electrolocation), and this behavioral control of sensory input may help improve neuronal stimulation detection and coding. The further development of the biomimetic active electrolocation catheter system makes the application of electrolocation technology in the field of neuromedical imaging more extensive. Understanding the specific role of electrical signals in these sensory and neuromodulation processes through chemical methods may have strong guiding significance and therapeutic applications for human neurological diseases.
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- Pedraja, F.; et al. Task-Related Sensorimotor Adjustments Increase the Sensory Range in Electrolocation. Journal of Neuroscience. 2020, 40(5): 1097-1109.
- Yilmaz, S.; Sen, S. Electric fish optimization: a new heuristic algorithm inspired by electrolocation. Neural Computing and Applications. 2020, 32(15): 11543-11578.