English: The presentation by Tilo Schwalger, from the Institut für Mathematik, Technische Universität Berlin, is part of the Pathways to the 2023 IHP thematic project Random Processes in the Brain.

In this seminar, Schwalger talks how mesoscopic neuronal population dynamics deals with emergent neural activity and computations at a coarse-grained spatial scale at which fluctuations due to a finite number of neurons should not be neglected. A prime example is metastable dynamics in cortical and hippocampal circuits, in which fluctuations likely play a critical role. In this lecture, I will discuss recent advances and current challenges for mean-field descriptions of computations and metastable dynamics at the mesoscopic scale. Firstly, I will discuss fundamental differences between external noise and intrinsic "finite-size noise" in population models, and their distinct impact on metastable dynamics. Is it possible to infer the type of metastability and noise from mesoscopic population data? Secondly, I will address the question of how to treat single-neuron dynamics (e.g. refractory mechanisms, adaptation) and synaptic dynamics (e.g. short-term depression) at the level of mesoscopic populations. Is it possible to derive (low-dimensional) bottom-up mesoscopic models that link back to the microscopic properties of spiking neural networks? And thirdly, I will address the fundamental problem of heterogeneity in biological neural networks. An important source of heterogeneity is non-homogeneous network structure. The synaptic connectivity of any neural network that performs computations is structured, e.g. as a result of learning. How can mesoscopic mean-field theories, which so far assumed homogeneous (unstructured) connectivity, be generalized to heterogeneous, structured connectivity?

You can learn more about the Pathways to the 2023 IHP thematic project Random Processes in the Brain on this link: https://rpbihp.numec.prp.usp.br/index.php/Pathways_to_the_2023_IHP_thematic_program_Random_Processes_in_the_Brain