Indications for optimality of motor cortex

The brain processes time-varying input, but is it 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 [1].
But they may also differ internally, in terms of their degree to
which they produce chaotic activity [2].

We here show that recordings from motor cortex support an operation
close to a transition to chaos [3]. 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 balanced 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 suppresses chaos so that a novel dynamical regime
emerges [2]: memory capacity in this regime is optimal.

Together these findings provide hints towards the operation principles
of cortical networks.


Partially supported by the Helmholtz assocoation, Helmholtz young investigator’s group VH-NG 1028,
RWTH ERS seed fund and the Hans-Herrman Voss Stiftung.


1. Johanna Senk, Karolína Korvasová, Jannis Schuecker, Espen Hagen, Tom Tetzlaff, Markus Diesmann, Moritz Helias (2018)
Conditions for traveling waves in spiking neural networks arXiv:1801.06046 [q-bio.NC]

2. Jannis Schuecker, Sven Goedeke, Moritz Helias (2016)
Optimal sequence memory in driven random networks arXiv:1603.01880 [q-bio.NC]

3. David Dahmen, Sonja Gruen, Markus Diesmann, Moritz Helias (2017)
Two types of criticality in the brain