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Institute of Neuroscience and Medicine

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Population-rate models

  • Relation between microscopic and macroscopic network dynamics
  • Role of cell-type diversity
  • Role of realistic network connectivity

A wealth of knowledge on brain dynamics and function has been obtained by means of population or firing-rate models. Rather than describing the activity of each individual neuron on a microscopic scale, they focus on the average activity, the firing rate, of populations of cells. The main purpose of such models is to reduce the dimensionality and complexity of the microscopic neural-network dynamics to obtain tools which allow mathematical treatment, efficient simulation and intuitive understanding. Despite their success in theoretical neuroscience, the relation between these macroscopic population-rate models and the underlying microscopic dynamics is still poorly understood. In most cases, existing rate models have to be considered purely phenomenological descriptions. Only under simplifying assumptions, they can be derived or extracted from the single-neuron dynamics. Previous studies in this field are mainly restricted to networks of homogeneous subpopulations of simple integrate-and-fire neurons. Our current work is aiming on understanding the effect of the known cell-type diversity in the cortex, in particular the role of various interneuron types, and the effect of a realistic cortical connectivity on the properties of macroscopic population-rate models.

Own project related publications:

Tetzlaff T., Helias M., Einevoll G.T., Diesmann M., Decorrelation of neural-network activity by inhibitory feedback, submitted

Oleynik A., Wyller J., Tetzlaff T., Einevoll G.T. (2011), Stability of bumps in a two-population neural-field model with quasi-power temporal kernels, Nonlinear Analysis: Real World applications (in press), doi:10.1016/j.nonrwa.2011.05.008

Helias M., Deger M., Rotter S., Diesmann M. (2010), Instantaneous Non-Linear Processing by Pulse-Coupled Threshold Units, PLoS Computational Biology 6:e1000929

Nordlie E, Tetzlaff T, Einevoll GT (2010), Rate dynamics of leaky integrate-and-fire neurons with strong synapses, Front. Comput. Neurosci. 4:149, doi:10.3389/fncom.2010.00149

Boucsein C., Tetzlaff T., Meier R., Aertsen A., Naundorf B. (2009), Dynamical response properties of neocortical neuron ensembles: multiplicative versus additive noise, Journal of Neuroscience 29(4):1006-1010

Kriener B., Tetzlaff T., Aertsen A., Diesmann M., Rotter S. (2008), Correlations and population dynamics in cortical networks, Neural Computation 20(9):2185 2226