Functional self-reconfiguration processes in neuronal networks
From Luis El Srouji
Here we propose [1-2] that neuronal network activity has two separate components: a collective reference state on top of which information is encoded and distributed in deviations from this reference, similarly to how radio signals broadcast information via frequency or amplitude modulation. In networks, switching between dynamical reference states then enables fast and flexible rerouting of information . In particular, for coupled oscillator networks we analytically show how the physical network structure and the dynamical reference state co-act in order to generate a specific information routing pattern, as quantified by transfer entropy. In modular networks, we find that local changes within a sub-network, e.g. as a result of local processing, are capable of influencing the network’s global reference dynamics and thereby can actively control the network-wide distribution of information. This in turn influences the local processing. Thus, in this loop, the network, while performing computations, is also capable in continuously updating its own function in a dynamic and flexible way . We numerically show that this mechanism for self-organized information processing naturally enables context dependent pattern-recognition in an oscillatory Hopfield network and an analog version of believe propagation.
We are currently exploring learning strategies within this approach  and developing novel data analysis tools combining dimension reduction and dynamic motif detection to identify possible reference dynamics in multi-site electrode recordings of neuronal brain activity.
If time permits, we will also discuss how we are currently using our brain inspired approach to design novel neuromorphic hardware based on energy efficient super-conducting oscillators 
 Kirst, Timme, Battaglia, Nature communications (2016)
 Kirst, Magnasco, Modes, Current Opinion in Systems Biology (2017)
 Zhang, Kirst (in prep)
 Cheng, Kirst*, Vasudevan, IEEE Transactions on Applied Superconductivity (2023, in revision)