Carregant...
Tipus de document
Treball de fi de grauData de publicació
Llicència de publicació
Si us plau utilitzeu sempre aquest identificador per citar o enllaçar aquest document: https://hdl.handle.net/2445/215845
Damage in simulated neural networks: impact of neuronal aggregation
Títol de la revista
Autors
Director/Tutor
ISSN de la revista
Títol del volum
Recurs relacionat
Resum
Here, we numerically modelled biologically-realistic neuronal networks. We considered neurons that connected to one another on a Euclidean space and used the Izhikevich model to describe their the activity. Inhibitory and excitatory neurons were considered, and were positioned on the Euclidean space in either a homogeneous or aggregated way. Axons emerging from them were modelled as random walkers. Once the network was built, targeted and random damage were applied, and the dynamic response of the network was quantified, measuring the impact of damage using network analysis. Results show that the simulated networks are most resilient when random attack is applied and nodes are arranged on an aggregated way. The change in dynamics exhibits a non-trivial behaviour, as it is heavily dependent not only on the type of damage applied, but also on the way the network is created and the type of neurons that are deleted.
Descripció
Treballs Finals de Grau de Física, Facultat de Física, Universitat de Barcelona, Curs: 2024, Tutor: Jordi Soriano Fradera
Matèries (anglès)
Citació
Col·leccions
Citació
NOVILLO I FONT, Ferran. Damage in simulated neural networks: impact of neuronal aggregation. [consulta: 24 de gener de 2026]. [Disponible a: https://hdl.handle.net/2445/215845]