title: Collective stability of networks of winner-take-all circuits.
abstract: The neocortex has a remarkably uniform neuronal organization, suggesting that common principles of processing are employed throughout its extent. In particular, the patterns of connectivity observed in the superficial layers of the visual cortex are consistent with the recurrent excitation and inhibitory feedback required for cooperative-competitive circuits such as the soft winner-take-all (WTA). WTA circuits offer interesting computational properties such as selective amplification, signal restoration, and decision making. But these properties depend on the signal gain derived from positive feedback, and so there is a critical trade-off between providing feedback strong enough to support the sophisticated computations while maintaining overall circuit stability. The issue of stability is all the more intriguing when one considers that the WTAs are expected to be densely distributed through the superficial layers and that they are at least partially interconnected. We consider how to reason about stability in very large distributed networks of such circuits. We approach this problem by approximating the regular cortical architecture as many interconnected cooperative-competitive modules. We demonstrate that by properly understanding the behavior of this small computational module, one can reason over the stability and convergence of very large networks composed of these modules. We obtain parameter ranges in which the WTA circuit operates in a high-gain regime, is stable, and can be aggregated arbitrarily to form large, stable networks. We use nonlinear contraction theory to establish conditions for stability in the fully nonlinear case and verify these solutions using numerical simulations. The derived bounds allow modes of operation in which the WTA network is multistable and exhibits state-dependent persistent activities. Our approach is sufficiently general to reason systematically about the stability of any network, biological or technological, composed of networks of small modules that express competition through shared inhibition.
authors.affiliation: Department of Neural Systems and Coding, Max Planck Institute for Brain Research, Frankfurt am Main, Hessen 60528, Germany urut@brain.mpg.de.
authors.forename: Ueli
authors.initials: U
authors.lastname: Rutishauser
authors.forename: Rodney J
authors.initials: RJ
authors.lastname: Douglas
authors.forename: Jean-Jacques
authors.initials: JJ
authors.lastname: Slotine
mesh_terms: D000465:Algorithms; D016014:Linear Models; D016571:Neural Networks, Computer; D009473:Neuronal Plasticity / Q000502:physiology*; D011336:Probability; D013269:Stochastic Processes; D013569:Synapses / Q000502:physiology*