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FISHEARS

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Enigmatic fish ears solving a sensory biology riddle with bioengineering and Ar...

Enigmatic fish ears solving a sensory biology riddle with bioengineering and Artificial Intelligence One of the predominant riddles of sensory biology is the diversity in fish auditory systems. It is widely accepted that fishes are well adapted to utilising underwater sounds as sensory cues in key life-history events. However, th... ver más

31/05/2025

UNIVERSITY OF BRIS...

272K€

Presupuesto del proyecto: 272K€

Líder del proyecto

UNIVERSITY OF BRISTOL No se ha especificado una descripción o un objeto social para esta compañía.

TRL 4-5

Fecha límite participación Sin fecha límite de participación.

Ver 4 Participantes

Financiación concedida El organismo H2020 notifico la concesión del proyecto el día 2020-03-16

MSCA-IF-2019: Individual Fellowships

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4 Participantes

UNIVERSITY OF BRISTOL

272.08K€ | Lider

UNEXE

N.D. | Participante

THE UNIVERSITY OF AUCKLAND

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Duración del proyecto: 62 meses Fecha Inicio: 2020-03-16
Fecha Fin: 2025-05-31

Líder del proyecto

UNIVERSITY OF BRISTOL No se ha especificado una descripción o un objeto social para esta compañía.

TRL 4-5

Presupuesto del proyecto 272K€

Fecha límite de participación Sin fecha límite de participación.

Descripción del proyecto One of the predominant riddles of sensory biology is the diversity in fish auditory systems. It is widely accepted that fishes are well adapted to utilising underwater sounds as sensory cues in key life-history events. However, the functional significance and the driving force leading to the differences in fish inner ear sizes and structures are unknown. A complex interplay of physical, evolutionary, functional and ecological factors may shape the different elements: a multiscale environment too complicated for human conceptualisation. I propose to address this question by applying novel bioimaging and computational tools to investigate elasmobranch fish ears. Firstly, diffusible iodine-based contrast enhanced computed tomography (diceCT) will be used, co-registered with MRI data, to build 3D high resolution models of the inner ears. Secondly, a Finite Element (FE) model will be created to digitally replicate a fish ear and understand the biomechanics of its structure. Finally, a statistical framework will be developed to incorporate the factors that may shape the hearing system of elasmobranch fishes, including the collected data, together with the available physiological, ecological and biogeographical information on each species, as well as species’ acoustic environmental parameters. A Machine Learning algorithm will be applied to infer patterns and relationships between the factors, to perform both cluster and prediction analyses. Thus, a reliable model will be developed, which can predict the hearing capability of any elasmobranch species based on the ear morphology and the first evidence of the function of fish ear diversity.

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