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The diversity of dynamical states in recurrent neural circuits


Experiments suggest a wealth of dynamical states in the brain. These range from asynchronous irregular activity to synchronizations, oscillations, activity waves or avalanches. Understanding the mechanisms that can give rise to such a diversity of network states in the healthy and diseased brain is a challenge both for experimentalists and theoreticians.

Also the implications of these different operating regimes of the network dynamics on the ability to encode, process and transmit information is not well understood. In recurrent network models, the sensitivity of neural dynamics to small perturbations or noise can reveal features that are governing the microscopic phase space organization. Optimal computational performance of neuronal networks was hypothesized to be found close to phase transitions,
where the dynamics exhibits universal behavior that is characterized by strong concerted fluctuations between neurons. The diversity of possible states and state transitions in a high dimensional system such as cortex, however, permits a multitude of hypotheses on the “ground state” of different cortical regions.

In this workshop, we bring together experts working on theories to characterize the different dynamical states of recurrent neural networks and identify synaptic, neuronal, and network properties that shape the collective dynamics. We want to relate dynamical states to features of observed neural activity in different cortical regions, work out possibilities to test theoretical predictions by experiments, and discuss functional implications of the dynamics.