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/

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