Submitted by Deivis Angeli (deivisangeli@gmail.com) on 2018-06-06T06:09:47Z No. of bitstreams: 1 DynamicCoordinationWithNetworkExternalities.pdf: 394741 bytes, checksum: e157b70d33e77a4676224e1c72a1e34d (MD5) / Approved for entry into archive by Katia Menezes de Souza (katia.menezes@fgv.br) on 2018-06-06T12:05:15Z (GMT) No. of bitstreams: 1 DynamicCoordinationWithNetworkExternalities.pdf: 394741 bytes, checksum: e157b70d33e77a4676224e1c72a1e34d (MD5) / Approved for entry into archive by Suzane Guimarães (suzane.guimaraes@fgv.br) on 2018-06-06T12:50:08Z (GMT) No. of bitstreams: 1 DynamicCoordinationWithNetworkExternalities.pdf: 394741 bytes, checksum: e157b70d33e77a4676224e1c72a1e34d (MD5) / Made available in DSpace on 2018-06-06T12:50:08Z (GMT). No. of bitstreams: 1 DynamicCoordinationWithNetworkExternalities.pdf: 394741 bytes, checksum: e157b70d33e77a4676224e1c72a1e34d (MD5) Previous issue date: 2018-05-09 / I analyze a dynamic coordination model under quasi-hyperbolic discounting. The main result is that present bias can induce a society to coordinate efficiently. When considering a transition from network A to B, higher present bias induces individuals to ask for higher relative quality of B, which is also what the central planner dictates, but for different reasons. Present biased agents overvalue relative quality, as positive externalities coming from more individuals in network B are more distant in the future, because transitioning takes time. The planner's motives are the negative externalities inflicted on agents 'stuck' in A during a transition. / É analizado um modelo de coordenação dinâmica sob desconto quasi-hiperbólico. O resultado principal é que a presença de miopia temporal pode induzir uma sociedade a coordenar eficientemente. Considerando a transição de uma rede A para uma rede B, maior miopia temporal induz indivíduos a serem mais exigentes em termos de qualidade intrínseca relativa da rede B, o que é a mesma coisa que um planejador central ditaria, mas por razões diferentes. Indivíduos com miopia temporal valorizam excessivamente a qualidade relativa, pois as externalidades recebidas por haver muitos indivíduos na rede B estão em um futuro distante e as transições não são imediatas. Os motivos da sugestão do planejador central são as externalidades negativas infligidas em agentes 'presos' na rede A durante uma transição.

Adaptability and flexibility are some of the most important human characteristics. Learning based on new experiences enables adaptation by changing the structural connectivity of the brain through plasticity mechanisms. But the human brain can also adapt to new tasks and situations in a matter of milliseconds by dynamic coordination of functional activation. To understand how this flexibility can be achieved in the computations performed by neural networks, we have to understand how the relatively fixed structural backbone interacts with the functional dynamics. In this thesis, I will analyze these interactions between the structural network connectivity and functional activations and their dynamic interactions on different levels of abstraction and spatial and temporal scales. One of the big questions in neuroscience is how functional interactions in the brain can adapt instantly to different tasks while the brain structure remains almost static. To improve our knowledge of the neural mechanisms involved, I will first analyze how dynamics in functional brain activations can be simulated based on the structural brain connectivity obtained with diffusion tensor imaging. In particular, I will show that a dynamic model of functional connectivity in the human cortex is more predictive of empirically measured functional connectivity than a stationary model of functional dynamics. More specifically, the simulations of a coupled oscillator model predict 54\% of the variance in the empirically measured EEG functional connectivity. Hypotheses of temporal coding have been proposed for the computational role of these dynamic oscillatory interactions on fast timescales. These oscillatory interactions play a role in the dynamic coordination between brain areas as well as between cortical columns or individual cells. Here I will extend neural network models, which learn unsupervised from statistics of natural stimuli, with phase variables that allow temporal coding in distributed representations. The analysis shows that synchronization of these phase variables provides a useful mechanism for binding of activated neurons, contextual coding, and figure ground segregation. Importantly, these results could also provide new insights for improvements of deep learning methods for machine learning tasks. The dynamic coordination in neural networks has also large influences on behavior and cognition. In a behavioral experiment, we analyzed multisensory integration between a native and an augmented sense. The participants were blindfolded and had to estimate their rotation angle based on their native vestibular input and the augmented information. Our results show that subjects alternate in the use between these modalities, indicating that subjects dynamically coordinate the information transfer of the involved brain regions. Dynamic coordination is also highly relevant for the consolidation and retrieval of associative memories. In this regard, I investigated the beneficial effects of sleep for memory consolidation in an electroencephalography (EEG) study. Importantly, the results demonstrate that sleep leads to reduced event-related theta and gamma power in the cortical EEG during the retrieval of associative memories, which could indicate the consolidation of information from hippocampal to neocortical networks. This highlights that cognitive flexibility comprises both dynamic organization on fast timescales and structural changes on slow timescales. Overall, the computational and empirical experiments demonstrate how the brain evolved to a system that can flexibly adapt to any situation in a matter of milliseconds. This flexibility in information processing is enabled by an effective interplay between the structure of the neural network, the functional activations, and the dynamic interactions on fast time scales.