Summary
Network structures arise in many applications like for biological tissues, neuron systems, and nanoelectronic devices. Neuronal network structures are inspiring novel neuromorphic computer architectures, overcoming physical scaling limits in traditional hardware. The project NEUROMORPH focuses on the interplay of emerging structures in biological neuron systems and electronic circuit models. The problems we address are formulated in terms of nonlinear partial differential systems, including stochastic and nonlocal terms. Examples include transport through ion channels, chemotaxis-fluid systems, mean-field network models, and memristor networks.
The aims of this mathematics-oriented project are to explore the structure of the multiscale systems, prove their well-posedness, and devise structure-preserving numerical methods. Mathematical challenges are coming from the cross-diffusion character, the coupling of different types of equations (partially diffusive, stochastic, algebraic), the nonstandard degeneracies of the equations, and the hierarchy of scales, ranging from the molecular to the cellular to the network level.
To achieve these goals, we develop new tools by combining variants of the boundedness-by-entropy method, compensated compactness, stability theory, and stochastic analysis. We build on the expertise of the PI on semiconductor device modeling, theory of cross-diffusion systems, numerical analysis, and recent work on stochastic differential equations. Concepts from thermodynamics, cell biology, and electrical engineering will be condensed into innovative mathematical theories for cross-diffusion systems and multiscale models.
The project culminates in the simulation of small bio-inspired neuromorphic circuits, where memristor devices model the behavior of synapses or ion channels and mimic neuronal connectivity. The combination of bio-physical and device-circuit models is expected to make a vital progress for the design of neuromorphic structures.
The aims of this mathematics-oriented project are to explore the structure of the multiscale systems, prove their well-posedness, and devise structure-preserving numerical methods. Mathematical challenges are coming from the cross-diffusion character, the coupling of different types of equations (partially diffusive, stochastic, algebraic), the nonstandard degeneracies of the equations, and the hierarchy of scales, ranging from the molecular to the cellular to the network level.
To achieve these goals, we develop new tools by combining variants of the boundedness-by-entropy method, compensated compactness, stability theory, and stochastic analysis. We build on the expertise of the PI on semiconductor device modeling, theory of cross-diffusion systems, numerical analysis, and recent work on stochastic differential equations. Concepts from thermodynamics, cell biology, and electrical engineering will be condensed into innovative mathematical theories for cross-diffusion systems and multiscale models.
The project culminates in the simulation of small bio-inspired neuromorphic circuits, where memristor devices model the behavior of synapses or ion channels and mimic neuronal connectivity. The combination of bio-physical and device-circuit models is expected to make a vital progress for the design of neuromorphic structures.
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| Web resources: | https://cordis.europa.eu/project/id/101018153 |
| Start date: | 01-10-2021 |
| End date: | 30-09-2026 |
| Total budget - Public funding: | 1 945 713,00 Euro - 1 945 713,00 Euro |
Cordis data
Original description
Network structures arise in many applications like for biological tissues, neuron systems, and nanoelectronic devices. Neuronal network structures are inspiring novel neuromorphic computer architectures, overcoming physical scaling limits in traditional hardware. The project NEUROMORPH focuses on the interplay of emerging structures in biological neuron systems and electronic circuit models. The problems we address are formulated in terms of nonlinear partial differential systems, including stochastic and nonlocal terms. Examples include transport through ion channels, chemotaxis-fluid systems, mean-field network models, and memristor networks.The aims of this mathematics-oriented project are to explore the structure of the multiscale systems, prove their well-posedness, and devise structure-preserving numerical methods. Mathematical challenges are coming from the cross-diffusion character, the coupling of different types of equations (partially diffusive, stochastic, algebraic), the nonstandard degeneracies of the equations, and the hierarchy of scales, ranging from the molecular to the cellular to the network level.
To achieve these goals, we develop new tools by combining variants of the boundedness-by-entropy method, compensated compactness, stability theory, and stochastic analysis. We build on the expertise of the PI on semiconductor device modeling, theory of cross-diffusion systems, numerical analysis, and recent work on stochastic differential equations. Concepts from thermodynamics, cell biology, and electrical engineering will be condensed into innovative mathematical theories for cross-diffusion systems and multiscale models.
The project culminates in the simulation of small bio-inspired neuromorphic circuits, where memristor devices model the behavior of synapses or ion channels and mimic neuronal connectivity. The combination of bio-physical and device-circuit models is expected to make a vital progress for the design of neuromorphic structures.
Status
SIGNEDCall topic
ERC-2020-ADGUpdate Date
27-04-2024
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