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DTSTART;TZID=Europe/Paris:20210415T130000
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DTSTAMP:20260430T093655
CREATED:20210106T205942Z
LAST-MODIFIED:20221114T100847Z
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SUMMARY:Luca Gamberi (King's College London\, UK)
DESCRIPTION:Title: Maximal modularity and the optimal size of parliaments: how big should your parliament be? \nAn important question in representative democracies is how to determine the optimal parliament size of a given country. According to an old conjecture\, known as the cubic root law\, there is a fairly universal power-law relation\, with a 1/3 exponent\, between the size of an elected parliament and the country’s population. Empirical data in modern European countries support such universality but are consistent with a larger exponent. In this seminar\, I will introduce the audience to this intriguing regularity and discuss the current state of social science research on the topic. I will then move on to introducing our model\, which is derived from fundamental complex network theory. We will see how modelling the population of a democratic country as a random network\, drawn from a “clustered” growth model\, enable us to determine an optimal number of constituencies – and thus of representatives – for a given population. In particular\, I will briefly show how the modularity of the population can be calculated analytically and that its functional relation with the number of constituencies is strongly non-monotonic\, exhibiting a maximum that depends on the population size. following a criterion of maximal modularity allows we are able to predict that the number of representatives and show that it naturally scales as a power-law in the size of the population – a finding that is qualitatively confirmed by the empirical analysis of real-world data.  \n\nLink to the seminar here \nPeople outside Université de Namur with a Microsoft Teams account may join. For any questions\, write to riccardo.muolo@unamur.be
URL:https://www.naxys.be/event/luca-gamberi-kings-college-london/
CATEGORIES:NAXYS Seminar
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DTSTART;TZID=Europe/Paris:20210427T130000
DTEND;TZID=Europe/Paris:20210427T140000
DTSTAMP:20260430T093655
CREATED:20210107T090911Z
LAST-MODIFIED:20221114T100830Z
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SUMMARY:Ana Paula Millán Vidal (Amsterdam UMC\, Netherlands)
DESCRIPTION:Title: Brain network models of epilepsy propagation and surgery \nClinical network neuroscience aims to apply the knowledge and tools from network science to study brain network topology in neurological and psychiatric diseases. This idea stems from the description of the brain as a complex network whose underlying structure and dynamics give rise to the emergent behaviour leading to cognition. How is this structure altered in different brain disorders [1]? And how can we apply this information to every-day clinical practice [2]? Here we will discuss the case of epilepsy\, a neurological disorder that affects between 4 and 10 per 1000 people worldwide [3]. There is not one single cause of epilepsy: it often occurs as an associated symptom of an underlying disease\, but many other times it is produced by unknown causes [4]. For patients who do not respond to medication (roughly 1 out of 3 people)\, epilepsy surgery is the treatment of choice if a focal origin can be found. However\, seizure-freedom is currently achieved in only 2/3 of the patients after surgery. Brain networks in patients with epilepsy have been found to deviate significantly from the healthy brain\, even when seizures have a local origin. In order to study how alterations in the underlying network and dynamics can lead and promote seizure propagation\, computational models are used to mimic seizure generation and propagation. These allow us to test in silico different resections before the actual surgery\, with the ultimate goal to improve surgery outcome and reduce side-effects. The choice of the network and dynamical model is the first step on this analysis. Simpler models allow for more computational tractability and theoretical analysis. In particular\, we have modelled seizure propagation as an epidemic spreading process. Fitted with patient specific data\, the model can capture the fundamental aspects of clinically observed seizure propagation\, and can be used to test virtual resections in silico. Combined with optimization algorithms\, smaller or alternative resection strategies\, that are individually targeted for each patient\, can be determined with the ultimate goal to improve surgery outcome. \n[1] C. J. Stam. Nature Reviews Neuroscience 15.10 (2014)\, 683-695.\n[2] L. Douw et al. Network Neuroscience 3.4 (2019)\, 969-993.\n[3] P. N. Banerjee\, D. Filippi\, and W. A. Hauser. Epilepsy Research 85.1 (2009)\, 31-45.\n[4] A. T. Berg et al. Epilepsia 51.4 (2010)\, 676-685.\n\n\nLink to the seminar here \nPeople outside Université de Namur with a Microsoft Teams account may join. For any questions\, write to riccardo.muolo@unamur.be
URL:https://www.naxys.be/event/ana-paula-millan-vidal-amsterdam-universitair-medische-centra/
CATEGORIES:NAXYS Seminar
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