Project description
Pioneering insight into the role of electrical synapses
Electrical synapses were first identified more than six decades ago in the crayfish nervous system. They transmit neuronal signals via ionic currents that pass directly from the cytoplasm of one neuron to the next through intercellular channels. Their importance in the mammalian nervous system has only recently been recognised, and their incorporation into current mathematical models of spiking neural networks based on conventional chemical synapses has been challenging. With the support of the Marie Skłodowska-Curie Actions programme, the NeuralFieldTheoriES project is developing the mathematics required to describe these junctions. Simulations incorporating them for the first time promise to reveal their unique role in the complex spiking patterns that underlie behaviour and cognition.
Objective
A major challenge in statistical physics, nonlinear dynamics and theoretical neuroscience over the last half century has been to understand the self-organizing principles governing the dynamics of large networks of neurons. Physicists and applied mathematicians have proposed simple mean-field descriptions of spatially-extended neural networks in terms of a relevant macroscopic observable, the firing rate. This approach has been particularly successful and so-called Neural Field Models (NFM) have become an extremely popular mathematical tool in neuroscience, physics and applied mathematics. Yet, to date, mean-field theories describe networks with chemical synapses, but it remains a major theoretical challenge to incorporate electrical synaptic interactions in such mathematical descriptions. Recently, a mean-field theory for large networks of spiking neurons has been proposed, which exactly links the dynamics of single neurons with that of two mean-field variables: The firing rate and the mean membrane potential. Remarkably, this theory permits to incorporate electrical interactions, but the mathematical derivation and the analysis of the dynamics of the first NFM is lagging. This project proposes the formal mathematical derivation of such NFM, as well as the thorough analysis of its dynamics and bifurcations. Towards this goal, at the host institution UPF in Barcelona, the ER will apply mean-field methods and nonlinear dynamical systems theory to derive the novel NFM (which we conjecture is of reaction-diffusion type). During a secondment at VU Amsterdam, the ER will be trained to become an expert in numerical analysis of partial differential equations, which will further allow him to perform extensive state-of-the-art computer simulations. The expected results will provide completely novel mechanistic insights on the emergence of complex spatio-temporal patterns of neuronal activity due to the intricate interplay between chemical and electrical synapses.
Fields of science (EuroSciVoc)
CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques. See: The European Science Vocabulary.
CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques. See: The European Science Vocabulary.
- natural sciences mathematics applied mathematics dynamical systems
- natural sciences mathematics pure mathematics mathematical analysis differential equations partial differential equations
- natural sciences biological sciences neurobiology computational neuroscience
- natural sciences mathematics applied mathematics numerical analysis
- natural sciences mathematics applied mathematics mathematical model
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Keywords
Project’s keywords as indicated by the project coordinator. Not to be confused with the EuroSciVoc taxonomy (Fields of science)
Project’s keywords as indicated by the project coordinator. Not to be confused with the EuroSciVoc taxonomy (Fields of science)
Programme(s)
Multi-annual funding programmes that define the EU’s priorities for research and innovation.
Multi-annual funding programmes that define the EU’s priorities for research and innovation.
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H2020-EU.1.3. - EXCELLENT SCIENCE - Marie Skłodowska-Curie Actions
MAIN PROGRAMME
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H2020-EU.1.3.2. - Nurturing excellence by means of cross-border and cross-sector mobility
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Topic(s)
Calls for proposals are divided into topics. A topic defines a specific subject or area for which applicants can submit proposals. The description of a topic comprises its specific scope and the expected impact of the funded project.
Calls for proposals are divided into topics. A topic defines a specific subject or area for which applicants can submit proposals. The description of a topic comprises its specific scope and the expected impact of the funded project.
Funding Scheme
Funding scheme (or “Type of Action”) inside a programme with common features. It specifies: the scope of what is funded; the reimbursement rate; specific evaluation criteria to qualify for funding; and the use of simplified forms of costs like lump sums.
Funding scheme (or “Type of Action”) inside a programme with common features. It specifies: the scope of what is funded; the reimbursement rate; specific evaluation criteria to qualify for funding; and the use of simplified forms of costs like lump sums.
MSCA-IF - Marie Skłodowska-Curie Individual Fellowships (IF)
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Call for proposal
Procedure for inviting applicants to submit project proposals, with the aim of receiving EU funding.
Procedure for inviting applicants to submit project proposals, with the aim of receiving EU funding.
(opens in new window) H2020-MSCA-IF-2020
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Net EU financial contribution. The sum of money that the participant receives, deducted by the EU contribution to its linked third party. It considers the distribution of the EU financial contribution between direct beneficiaries of the project and other types of participants, like third-party participants.
08002 Barcelona
Spain
The total costs incurred by this organisation to participate in the project, including direct and indirect costs. This amount is a subset of the overall project budget.