Effective influences in neuronal networks : attentional modulation of effective influences underlying flexible processing and how to measure them
|Other Titles:||Effektive Einflüsse in Neuronalen Netzwerken : aufmerksamkeitsbedingte Modulation von effektiven Einflüssen, die flexibler Informationsverarbeitung zugrunde liegen, und wie diese zu quantifizieren sind||Authors:||Harnack, Daniel||Supervisor:||Ernst, Udo A.||1. Expert:||Ernst, Udo A.||2. Expert:||Kreiter, Andreas K.||Abstract:||
Selective routing of information between brain areas is a key prerequisite for flexible adaptive behaviour. It allows to focus on relevant information and to ignore potentially distracting influences. Selective attention is a psychological process which controls this preferential processing of relevant information. The neuronal network structures and dynamics, and the attentional mechanisms by which this routing is enabled are not fully clarified. Based on previous experimental findings and theories, a network model is proposed which reproduces a range of results from the attention literature. It depends on shifting of phase relations between oscillating neuronal populations to modulate the effective influence of synapses. This network model might serve as a generic routing motif throughout the brain. The attentional modifications of activity in this network are investigated experimentally and found to employ two distinct channels to influence processing: facilitation of relevant information and independent suppression of distracting information. These findings are in agreement with the model and previously unreported on the level of neuronal populations. Furthermore, effective influence in dynamical systems is investigated more closely. Due to a lack of a theoretical underpinning for measurements of influence in non-linear dynamical systems such as neuronal networks, often unsuited measures are used for experimental data that can lead to erroneous conclusions. Based on a central theorem in dynamical systems, a novel theory of effective influence is developed. Measures derived from this theory are demonstrated to capture the time dependent effective influence and the asymmetry of influences in model systems and experimental data. This new theory holds the potential to uncover previously concealed interactions in generic non-linear systems studied in a range of disciplines, such as neuroscience, ecology, economy and climatology.
|Keywords:||Neuroscience, Attention, Effective Influence, Causality||Issue Date:||20-Apr-2018||URN:||urn:nbn:de:gbv:46-00106526-14||Institution:||Universität Bremen||Faculty:||FB1 Physik/Elektrotechnik|
|Appears in Collections:||Dissertationen|
checked on Sep 26, 2020
checked on Sep 26, 2020
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