Oscillations in Natural Neuronal Networks; An Epiphenomenon or a Fundamental Computational Mechanism?

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Abstract
The first part of the paper is devoted to a comparison between the functional architectures of the cerebral cortex and artificial intelligent systems. While the two systems share numerous features, natural systems differ in at least four important aspects: i) the prevalence of recurrent connections, ii) the ability to use the temporal domain for computations, iii) the ability to perform "in memory" computations and iv) the prevalence of analog computations. The second part of the paper focuses on a simulation study that has been designed to answer the long-standing question of whether the oscillatory patterning of neuronal activity, which is a hallmark of natural systems, is an epiphenomenon of recurrent interactions or serves a functional role. To this end, recurrent neuronal networks were simulated to capture essential features of cortical networks, and their performance was tested on standard pattern recognition benchmark tests. In order to control the oscillatory regime of these networks, its nodes were configured as damped harmonic oscillators. By varying the damping factor, the nodes functioned either as leaky integrators or oscillators. It turned out that networks with oscillatory nodes substantially outperformed their non-oscillating counterparts. The reasons for this superior performance and similarities with natural neuronal networks are discussed. It is concluded that the oscillatory patterning of neuronal responses is functionally relevant because it allows the exploitation of the unique dynamics of coupled oscillators for analog computation.
Funding Information
  • DFG, Koselleck-Project (GZ: SI 505/22-1, GZ: SI 505/22-1)
  • Ernst Strüngmann Institute for Neuroscience
  • Ernst Strüngmann Institut gGmbH (ESI)

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