Connectivity
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Prof Tansu Celikel
The Department of Neurophysiology, Donders Centre for Neuroscience is looking for a PhD candidate to discover targets and pathways in molecular networks. You will investigate transcriptomic and proteomic networks in neurons, and how these networks relate to experience-dependent plasticity, i.e. the changes in neuronal and network structure upon sensory input. This includes developing statistical methods for molecular target identification, and comparison of connectivity in molecular networks to connectivity in cellular networks. For more information see: https://www.ru.nl/werken-bij/vacature/details-vacature/?recid=1129025 For more information about SmartNets: https://www.smartnets-etn.eu/
Dr Panayiota Poirazi
The successful applicant will build a simulation model of the DG-CA3-CA1 hippocampal network and use it to assess the role of dendritic nonlinearities on both the memory capacity and the structure of the resulting networks, following contextual and spatial learning. Specifically, the successful applicant will build simplified integrate-and-fire circuits of the DG, CA3 and CA1 hippocampal sub-regions, interconnected in one network and equipped with known plasticity rules such as LTP/LTD, homeostatic plasticity and plasticity of neuronal excitability. The basic biophysical properties of all models will be validated against existing experimental data provided by the literature, ongoing collaborations (e.g. Attila Losonczy, Columbia University) and partners of this ETN (secondment Fleur Zeldenrust and Tansu Celikel). The hippocampal circuit model will be trained with two integrative tasks: contextual learning and spatial learning. The properties of the resulting engram (e.g. neuronal population size during recall, somatic and dendritic excitability, dendritic spiking probability, synaptic weight changes, spatial distribution of modified synapses, sparsity, place cell number, stability, sensitivity etc) will be assessed. By systematically manipulating dendritic biophysics (i.e. ionic and synaptic conductances) so as to generate linear, sublinear and supralinear dendrites, as well as the various synaptic/intrinsic plasticity rules, we will assess the effect of these manipulations on a) learning capacity, b) the emergent connectivity of the network post-learning, c) engram size/connectivity/recall stability etc. For more information see: https://www.smartnets-etn.eu/role-of-dendritic-nonlinearities-in-hippocampal-network-properties-after-contextual-and-spatial-learning/
Dr Panayiota Poirazi
The successful applicant will build a simulation model of the rodent visual cortex and use it to assess the role of dendritic nonlinearities on the connectivity and activity properties of the resulting memory engrams. Selected model predictions will be tested in headfixed behaving animals performing a visual discrimination task. For more information see: https://www.smartnets-etn.eu/role-of-dendritic-nonlinearities-in-v1-network-properties-after-visual-learning/
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