Dynamic instabilities from information annihilation in neuronal networks and human motor control

• Applicant: Professor Dr. Klaus Pawelzik
• Subject Area: Cognitive, Systems and Behavioural Neurobiology ; Statistical Physics, Nonlinear Dynamics, Complex Systems, Soft and Fluid Matter, Biological Physics
• Term: from 2015 to 2021
• Project identifier: Deutsche Forschungsgemeinschaft (DFG) - Project number 283571598

Many Complex Adaptive Systems, including neuronal networks, human balancing behaviour, and financial markets, exhibit complex activity, which are characterised by spatio-temporal scaling laws. Interestingly, these systems all feature a dynamic balance of opposing influences. We aim to explain why this balancing does not result in simple equilibria. In particular, we will investigate whether a single general principle, by which criticality emerges from an efficient absorption of information, can account for these observations. We will work on these questions in two subprojects concerning motor control (SP1) and neuronal networks (SP2):Adaptive motor control (SP1): Our previous work shows that Information Annihilation Instability (IAI) can account for criticality in adaptive control. However, the extent to which the corresponding models can explain human motor control in realistic situations is not known. We will perform experiments to test predictions of our previous model, to investigate the range of applicability of our theory, and to perform succeeding modifications and extensions. Also, we will investigate the more general consequences of our theory for adaptive control theory, forward models, and self-organisation of motor-control policies. We expect this subproject to yield new insights into human motor control and to deliver important constraints for the development of biologically realistic neural network models.Neuronal networks (SP2): Many parts of the brain operate close to a state where exciting and inhibiting inputs sent to a neuron are closely balanced. The mechanisms causing this balance and its functional significance are not well understood. Preliminary evidence indicates that specific synaptic adaptations, realizing input balance, induce an increased sensitivity of networks to unexpected inputs and lead to sparse and predictive codes. We further suspect that in recurrent networks the balanced state corresponds to a specific point of operation that is beneficial for coding, signal transmission, and computation. Each of these hypotheses will be investigated by developing and analysing biologically plausible neuronal network models. Furthermore, dynamical consequences and functional benefits of the balance state will be explored for paradigmatic network types as well as for selected closed loop sensor-motor control systems. Using a data-driven approach, we will identify differences and similarities of the two systems as well as their dynamics and develop the respective models within biologically plausible constraints. This will lay ground for the formulation of a general theory of IAI. The identification of unifying, system-independent descriptions will be carried out in parallel in SP1, SP2. and, if approved by the DFG, in close collaboration with the related project on IAI in financial markets (by Dr. Felix Patzelt).

• DFG Programme: Research Grants