DIMENSIVE · Data-driven Inference of Models from Embodied Neural Systems In Vertebrate Experiments
Horizon 2020 — Marie Skłodowska-Curie Actions
- Duration
- 2020-05-12 → 2022-05-25
- EU contribution
- €212,934
- Participants
- 1
- Scheme
- MSCA-IF-EF-ST
Lines connect the coordinator with its partners.
Results in brief
Data-driven Inference of Models from Embodied Neural Systems In Vertebrate Experiments
A crucial challenge in modern neuroscience is the development of methodological tools for incorporating behaviour into the study of brain and mind (Krakauer et al., 2017; Pessoa et al., 2022). Neural recordings usually generate large-scale, non-stationary data, obscuring our understanding of how organisms extract, generate and leverage valuable information from interactions with their environment. Accurately explaining the role of neural activity in information processing involves resolving various conceptual, technical and methodological issues at many levels of organization (from neural biochemistry to behaviour and learning). The state-of-the-art view is that the activity of neural populations organizes dynamically with behaviour in a complex, ongoing interaction, being steered but not deterministically controlled by sensory input (Harris, 2005). This complex interplay between brain, body and environment is an open problem in academic fields from neuroscience to cognitive robotics. Even simple behaviours show complex interdependencies that involve many levels of organization and non-stationary dynamics, and current efforts are still too limited in providing an integrative view. Thus, there is a pressing demand for mathematical, physical and computational theories to disentangle and explain the complex dynamics of nonequilibrium neural computation (Grün, 2009). This MSCA aimed to develop and apply mathematical tools and experimental setups to study large neural systems during animal behaviour, i.e., in out-of-equilibrium conditions driven by sensorimotor interaction.
Data: CORDIS, © European Union
Project objective
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.
Original text from CORDIS.
Participants
- THE UNIVERSITY OF SUSSEX · BrightonCoordinatorUnited Kingdom
Links
Data: CORDIS, © European Union