MPIDS Colloquium: Indications for optimality of motor cortex
14:15 - 15:15
Prof. Moritz Helias
Research Center Jülich, Germany
Max-Planck-Institut für Dynamik und Selbstorganisation (MPIDS)
Prandtl Lecture Hall
The brain processes time-varying input, but it is not known how it achieves its high computational performance. Indeed, neuronal networks can show a rich set of dynamical states. Such states can differ in the measureable spatio-temporal patterns of activity . But they may also differ internally, in terms of their degree to which they produce chaotic activity .
We here show that recordings from motor cortex support an operation close to a transition to chaos . We identify this computationally beneficial regime by combining finite-size mean-field theory with massively parallel spike recordings. The theoretical predictions resolve the puzzle how a bal-anced state can be compatible with widely distributed correlations and long-time dynamics.
We then investigate how such networks process input by quantifying how time-varying input sup-presses chaos so that a novel dynamical regime emerges : memory capacity in this regime is op-timal.
Together these findings provide hints towards the operation principles of cortical networks.
Partially supported by the Helmholtz association, Helmholtz young investigator's group VH-NG 1028, RWTH ERS seed fund and the Hans Hermann Voss Stiftung.
 Johanna Senk, Karolína Korvasová, Jannis Schuecker, Espen Hagen, Tom Tetzlaff, Markus Diesmann, Moritz He-lias (2018), “Conditions for traveling waves in spiking neural networks”, arXiv:1801.06046 [q-bio.NC]
 Jannis Schuecker, Sven Goedeke, Moritz Helias (2016), “Optimal sequence memory in driven random networks”, arXiv:1603.01880 [q-bio.NC]
 David Dahmen, Markus Diesmann, Moritz Helias (2016), “Distributions of covariances as a window into the opera-tional regime of neuronal networks”, arXiv:1605.04153 [cond-mat.dis-nn]