Generating neural systems that are both flexible and stable is a nontrivial challenge and requires a prolonged period of development when multiple mechanisms are coordinated in a hierarchy of levels and timescales to establish a rich repertoire of computations. Studying this process is of fundamental importance for the understanding of normal brain function and the prevention, detection and treatment of brain disorders and intellectual disabilities. There are many transient mechanisms that operate during the timescale of development shaping neural networks structures in a unique way to prepare the brain for adult computations. In this project the ESR will study the role of the subplate, a transient cortical structure that disappears shortly after birth, in providing a scaffold for self-organization of connectivity between the thalamus (a relay station) and the cortex. The project will be based on published experimental data from visual and barrel cortex in rodents, but we will also collaborate with experimental colleagues at the University of Mainz.
The student will develop biologically realistic network models to understand how connectivity strength and spread between the thalamus, subplate and cortex can be changed during development. For this, we will use using biologically plausible Hebbian learning rules for tuning synaptic weights, including spike-timing-dependent plasticity (STDP) that relies on spike interactions based on pairs and triplets of spikes. A second aspect of the project addressed by the ESR will focus on the role of inhibition. Even though inhibition is absent in the cortex during the relevant stage of development, it is present in the subplate, which matures earlier. Can subplate activity patterns drive functional feedforward connectivity into the cortex? How does it depend on the connectivity profiles, both feedforward and intra-subplate? This project will help us identify changing network structures during development that help establish mature network connectivity and computations.
Kanold PO, Luhmann HJ. (2010). Annu Rev Neurosci. 2010;33:23-48
1. To build a biophysically realistic model of network self-organization using a transient scaffold structure.
2. To determine the learning rule motifs that enable the emergence of robust and precise connectivity.
3. To determine the role of inhibition in the maturation of network connectivity during development.
1. Fleur Zeldenrust, Radboud University, M11-13. Biophysical network modeling.
2. MetaCell Ltd., M29-31: analyze experimental results using the MetaCell tools.
Enrolment in Doctoral degree(s): You will be enrolled at the Graduate School of Life Sciences at the Technical University of Munich.