Felix Effenberger, Pedro Carvalho, Igor Dubinin, Wolf Singer

Biological neuronal networks have the propensity to oscillate. However, it is unclear whether these oscillations are a mere byproduct of neuronal interactions or serve computational purposes. Therefore, we implemented hallmark features of the cerebral cortex in recurrent neuronal networks (RNNs) simulated in silico and examined their performance on common pattern recognition tasks after training with a gradient-based learning rule. We find that by configuring network nodes as damped harmonic oscillators (DHOs), performance is substantially improved over non-oscillating architectures and that the introduction of heterogeneous nodes, conduction delays, and network modularity further improved performance. We furthermore provide a proof of concept of how unsupervised Hebbian learning can work in such networks. Analyses of network activity illustrate how the nonlinear dynamics of coupled DHOs drive performance, and provide plausible a posteriori explanations for a number of physiological phenomena whose function so far has been elusive.

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