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Data driven Inference of Models from Embodied Neural Systems In Vertebrate Exper...

Data driven Inference of Models from Embodied Neural Systems In Vertebrate Experiments A major challenge in cognitive neuroscience is to understand how behaviour arises from the dynamical interaction of an organism’s nervous system, its body, and its environment. Understanding embodied neural activity involves the r... ver más

25/05/2022

UOS

213K€

Presupuesto del proyecto: 213K€

Líder del proyecto

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

TRL 4-5

Fecha límite participación Sin fecha límite de participación.

Financiación concedida El organismo H2020 notifico la concesión del proyecto el día 2022-05-25

MSCA-IF-2019: Individual Fellowships

I+D

Cerrada hace 5 años

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No hay información privada compartida para este proyecto. Habla con el coordinador.

1 Participantes

UOS

212.93K€ | Lider

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Duración del proyecto: 25 meses Fecha Inicio: 2020-04-06
Fecha Fin: 2022-05-25

Líder del proyecto

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

TRL 4-5

Presupuesto del proyecto 213K€

Fecha límite de participación Sin fecha límite de participación.

Descripción del proyecto A major challenge in cognitive neuroscience is to understand how behaviour arises from the dynamical interaction of an organism’s nervous system, its body, and its environment. Understanding embodied neural activity involves the resolution of various conceptual, technical and methodological issues in explaining how living organisms self-organize at many levels (from neural bio-chemistry to behaviour and learning). Currently, two important obstacles hinder this endeavour: the difficulties in recording neural activity in behaving animals, and the lack of mathematical tools to characterize the complex brain-body-environment interactions in living organisms. In this project we will address current limits by implementing an interdisciplinary combination of novel animal behaviour neuroimaging setups and large-scale statistical methods, with the goal of recording and modelling whole-brain activity of locomoting vertebrates. We will study fictively swimming larval zebrafish during active behaviour in a pioneering experimental setup, recording neural activity utilizing light-sheet microscopy for calcium imaging in different virtual reality scenarios involving sensorimotor manipulations. In this setup, we will collect data from the distributed neural circuits that integrate sensory signals from the environment (exafferent input) and their own movements (reafferent input), as well as plastic processes of habituation to new sensorimotor contingencies. From this data, we will infer large-scale generative models (i.e. models capable of yielding synthetic data resembling the studied phenomena) of embodied neural circuits by complementing dynamical models and techniques from statistical mechanics with innovative information theoretic and Bayesian inference methods and approximations for very large systems in non-equilibrium and non-stationary conditions.

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