Description: Optimal Sequence Memory in Driven Random Networks

This title appears in the Scientific Report : 2018

Optimal Sequence Memory in Driven Random Networks

Autonomous, randomly coupled, neural networks display a transition to chaos at a critical coupling strength. Here, we investigate the effect of a time-varying input on the onset of chaos and the resulting consequences for information processing. Dynamic mean-field theory yields the statistics of the...

Personal Name(s):	Schuecker, Jannis (Corresponding author)
	Goedeke, Sven / Helias, Moritz
Contributing Institute:	Computational and Systems Neuroscience; INM-6 JARA-BRAIN; JARA-BRAIN Theoretical Neuroscience; IAS-6
Published in:	Physical review / X, 8 (2018) 4, S. 041029
Imprint:	College Park, Md. APS 2018
DOI:	10.1103/PhysRevX.8.041029
Document Type:	Journal Article
Research Program:	Supercomputing and Modelling for the Human Brain Theory of multi-scale neuronal networks Theory, modelling and simulation
Link:	Get full text Get full text OpenAccess OpenAccess
	Publikationsportal JuSER

Please use the identifier: http://hdl.handle.net/2128/20119 in citations.
Please use the identifier: http://dx.doi.org/10.1103/PhysRevX.8.041029 in citations.

Autonomous, randomly coupled, neural networks display a transition to chaos at a critical coupling strength. Here, we investigate the effect of a time-varying input on the onset of chaos and the resulting consequences for information processing. Dynamic mean-field theory yields the statistics of the activity, the maximum Lyapunov exponent, and the memory capacity of the network. We find an exact condition that determines the transition from stable to chaotic dynamics and the sequential memory capacity in closed form. The input suppresses chaos by a dynamic mechanism, shifting the transition to significantly larger coupling strengths than predicted by local stability analysis. Beyond linear stability, a regime of coexistent locally expansive but nonchaotic dynamics emerges that optimizes the capacity of the network to store sequential input.