Transient Cognitive Dynamics, Metastability, and Decision Making

By Mikhail I. Rabinovich, Ramón Huerta, Pablo Varona, Valentin S. Afraimovich. PLoS Comput. Biol., 2008

352 citations recorded by the source corpus.

The idea that cognitive activity can be understood using nonlinear dynamics has been intensively discussed at length for the last 15 years. One of the popular points of view is that metastable states play a key role in the execution of cognitive functions. Experimental and modeling studies suggest that most of these functions are the result of transient activity of large-scale brain networks in the presence of noise. Such transients may consist of a sequential switching between different metastable cognitive states. The main problem faced when using dynamical theory to describe transient cognitive processes is the fundamental contradiction between reproducibility and flexibility of transient behavior. In this paper, we propose a theoretical description of transient cognitive dynamics based on the interaction of functionally dependent metastable cognitive states. The mathematical image of such transient activity is a stable heteroclinic channel, i.e., a set of trajectories in the vicinity of a heteroclinic skeleton that consists of saddles and unstable separatrices that connect their surroundings. We suggest a basic mathematical model, a strongly dissipative dynamical system, and formulate the conditions for the robustness and reproducibility of cognitive transients that satisfy the competing requirements for stability and flexibility. Based on this approach, we describe here an effective solution for the problem of sequential decision making, represented as a fixed time game: a player takes sequential actions in a changing noisy environment so as to maximize a cumulative reward. As we predict and verify in computer simulations, noise plays an important role in optimizing the gain.

Subfields: Cognitive Neuroscience, Economics and Econometrics, Statistical and Nonlinear Physics

View Paper: https://www.semanticscholar.org/paper/2235899f3a7cfa096e0fb4d5ae14f99110b53bbd

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Citations in the corpus

Controlling Recurrent Neural Networks by Conceptors (2014, background): “This idea can be traced back at least to the notion of cell assemblies formulated by Hebb [45], reached a first culmination in the formal analysis of associative memories [80, 48, 4], and has since then diversified into a range of increasingly complex models of interacting (partial) neural attractor”
A neurodynamical model for working memory (2011, background): “Departing from the classical notion of attractors, a number of alternative “attractor-like” metastability phenomena have been considered that may arise in high-dimensional nonlinear dynamics: saddle point dynamics (Rabinovich et al., 2008); attractor relics (or attractor ruins) where classical attra”
Criticality in the brain: A synthesis of neurobiology, models and cognition. (2017, background): “…is that of so-called winnerless competition (Melbourne et al., 1989; Rabinovich et al., 2001) that has been proposed to underlie cognitive tasks such as animal gait (Golubitsky et al., 1999), perceptual rivalry (Ashwin and Lavric, 2010), and sequential decision-making (Rabinovich et al., 2008).”
The Metastable Brain (2014, neutral): “This is observed both during intensive information exchange with the environment (e.g., Hoshi and Tamura, 1997; Rabinovich et al., 2008) and when the brain operates more on the side of a closed system at rest (Gong et al., 2003; Kaplan et al., 2005; Honey et al., 2007; Ito et al., 2007).”
Neural syntax: cell assemblies, synapsembles and readers (2010, neutral): “…the evolution of the assembly sequences (i.e., the uniquely different neural sentences) can be described by a transition rule where the future sequence is probabilistically defined by the previous ordering of assemblies (Jin, 2009; Rabinovich et al., 2008a, 2008b; Sakata and Brainard, 2006).”
The anticipating brain is not a scientist: the free-energy principle from an ecological-enactive perspective (2016, background): “…self-organizing (living) systems such as synergetics (Tschacher and Haken2007), global brain dynamics (Freeman1987, 2000),metastability (Kelso2012; Rabinovich et al. 2008), autopoiesis (Maturana and Varela 1980; but see Kirchhoff (2016)) and ecological psychology (Gibson 1979).5 Not all of this…”
Opening the Black Box: Low-Dimensional Dynamics in High-Dimensional Recurrent Neural Networks (2013, result): “…therefore initially their implementation was unknown, our method and results are compatible with studies attempting to understand biological neural circuits, for example (Huerta & Rabinovich, 2004; Machens, Romo, & Brody, 2005; Rabinovich et al., 2008; Liu & Buonomano, 2009; Ponzi & Wickens, 2010).”
Resting‐state networks show dynamic functional connectivity in awake humans and anesthetized macaques (2013, background): “…changing microstates—a cycling of varying network topologies through the brain’s functional repertoire allowing for a plastic and flexible framework necessary for ongoing cognitive processes [Friston, 2000; Kelso, 1995; Rabinovich et al., 2008; Tognoli and Kelso, 2009; Sporns, 2010, p. 172].”