Coherent chaos in a recurrent neural network with structured connectivity

• Published in: PLoS computational biology 2018-12, Vol.14 (12), p.e1006309-e1006309
• Main Authors: Landau, Itamar Daniel, Sompolinsky, Haim
• Format: Article
• Language: English
• Subjects: Biology and Life Sciences; Broken symmetry; Coherence; Computer and Information Sciences; Computer Simulation - statistics & numerical data; Constraints; Dynamics; Eigenvalues; Feedback loops; Fluctuations; Medicine and Health Sciences; Memory; Models, Neurological; Neocortex; Neural networks; Neural Networks, Computer; Neurons; Neurons - physiology; Neurosciences; Nonlinear Dynamics; Orthogonality; Phase transitions; Physical Sciences; Physics; Recurrent neural networks; Research and Analysis Methods; Scaling; Size effects; Standard deviation; Switching theory; Theory
• ISSN: 1553-7358; 1553-734X; 1553-7358
• DOI: 10.1371/journal.pcbi.1006309

We present a simple model for coherent, spatially correlated chaos in a recurrent neural network. Networks of randomly connected neurons exhibit chaotic fluctuations and have been studied as a model for capturing the temporal variability of cortical activity. The dynamics generated by such networks, however, are spatially uncorrelated and do not generate coherent fluctuations, which are commonly observed across spatial scales of the neocortex. In our model we introduce a structured component of connectivity, in addition to random connections, which effectively embeds a feedforward structure via unidirectional coupling between a pair of orthogonal modes. Local fluctuations driven by the random connectivity are summed by an output mode and drive coherent activity along an input mode. The orthogonality between input and output mode preserves chaotic fluctuations by preventing feedback loops. In the regime of weak structured connectivity we apply a perturbative approach to solve the dynamic mean-field equations, showing that in this regime coherent fluctuations are driven passively by the chaos of local residual fluctuations. When we introduce a row balance constraint on the random connectivity, stronger structured connectivity puts the network in a distinct dynamical regime of self-tuned coherent chaos. In this regime the coherent component of the dynamics self-adjusts intermittently to yield periods of slow, highly coherent chaos. The dynamics display longer time-scales and switching-like activity. We show how in this regime the dynamics depend qualitatively on the particular realization of the connectivity matrix: a complex leading eigenvalue can yield coherent oscillatory chaos while a real leading eigenvalue can yield chaos with broken symmetry. The level of coherence grows with increasing strength of structured connectivity until the dynamics are almost entirely constrained to a single spatial mode. We examine the effects of network-size scaling and show that these results are not finite-size effects. Finally, we show that in the regime of weak structured connectivity, coherent chaos emerges also for a generalized structured connectivity with multiple input-output modes.