Synchronization-based computation through networks of coupled oscillators - Malagarriga, Daniel; García-Vellisca, Mariano A.; Villa, Alessandro E. P.; Buldú, Javier M.; García-Ojalvo, Jordi; Pons, Antonio J. (Frontiers in Computational Neuroscience, 2015)
The mesoscopic activity of the brain is strongly dynamical, while at the same time exhibiting remarkable computational capabilities. In order to examine how these two features coexist, here we show that the patterns of synchronized oscillations displayed by networks of neural mass models, representing cortical columns, can be used as substrates for Boolean computation. Our results reveal that different logical operations can be implemented by the same neural mass network at different times following the dynamics of the input. The results are reproduced experimentally with electronic circuits of coupled Chua oscillators, showing the robustness of this kind of computation to the intrinsic noise and parameter mismatch of the oscillators responsible for the functioning of the gates. We also show that the information-processing capabilities of coupled oscillations go beyond the simple juxtaposition of logic gates.