Abstract
The brain is a complex dynamical system which employs specific mechanisms in a self-organized way to stabilize functionally relevant patterns of activity and switch between them, depending on computational demands. We first provide an overview of control mechanisms that involve delayed feedback of activity, plasticity of synaptic coupling strengths and changes of neuronal adaptation properties, and then focus on the latter, summarizing recent results for different spatial levels, obtained through mathematical bottom-up modeling.
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- 1.
Pyramidal cells are the largest class of excitatory neurons in the brain.
- 2.
Note, that an excitatory (inhibitory) neuron can only produce excitatory (inhibitory) synaptic effects at its target neurons.
- 3.
The direction of the flow along the synchronous solution.
- 4.
Synaptic noise is the by far largest-amplitude noise source in neurons of the central nervous system [49].
- 5.
The dynamics of population-averaged activity measures of this kind relate to those of experimentally widely applied neuronal mass signals (such as local field potentials or electroencephalograms [95]) and are thus of major interest.
- 6.
Electrophysiological recordings from neuronal pairs have shown that the connection probability in cortical networks is often very low (see, e.g., [99]). Random connectivity is a simplifying assumption that implies negligible noise correlations as measured in vivo [100]. This is exploited in the mean-field reduction below.
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Acknowledgments
This work was supported by the Deutsche Forschungsgemeinschaft in the framework of Collaborative Research Center 910.
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Ladenbauer, J., Augustin, M., Obermayer, K. (2016). Intrinsic Control Mechanisms of Neuronal Network Dynamics. In: Schöll, E., Klapp, S., Hövel, P. (eds) Control of Self-Organizing Nonlinear Systems. Understanding Complex Systems. Springer, Cham. https://doi.org/10.1007/978-3-319-28028-8_23
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