Transient Dynamics for Neural Processing

By M. Rabinovich, Ramón Huerta, Gilles Laurent. Science, 2008

451 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Cognitive Neuroscience

View Paper: https://www.semanticscholar.org/paper/bde9ab741e1ca8e8ded4bea9422b72fa6fc3e910

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Citations in the corpus

SORN: A Self-Organizing Recurrent Neural Network (2009, unknown): “Reservoir networks do not require classical attractor states and are compatible with the view that cortical computation is based on transient dynamics (Mazor and Laurent, 2005; Durstewitz and Deco, 2008; Rabinovich et al., 2008).”
Information processing using a single dynamical node as complex system (2011, background): “Th e main inspiration underlying RC is the insight that the brain processes information generating patterns of transient neuronal activity excited by input sensory signals 13 .”
Functional connectivity dynamics: Modeling the switching behavior of the resting state (2015, background): “Thesetrajectorieseithercon-verged back towards the ﬁ xed point from which they originated or escaped towards different attractors, thereby giving rise potentially to structured dynamic itinerancy (Bressler and Kelso, 2001; Rabinovich et al., 2008).”
ROBUST TIMING AND MOTOR PATTERNS BY TAMING CHAOS IN RECURRENT NEURAL NETWORKS (2012, background): “However, more recent theoretical and experimental work has emphasized the importance of dynamic patterns of activity in neural computations (Buonomano and Maass, 2009; Durstewitz and Deco, 2008; Perdikis et al., 2011; Rabinovich et al., 2008; Stopfer et al., 2003).”
Transient phenomena in ecology (2018, background): “Another important aspect will be the exchange of ideas with other areas of biology such as neuroscience, where transients are considered important and have been studied with both data and models (20, 21).”
Computation Through Neural Population Dynamics (2020, background): “The study also serves as yet another example of how different regions of state space can subserve different computational roles (Churchland et al. 2012; Hennequin et al. 2014; Mante et al. 2013; Murakami & Mainen 2015; Rabinovich et al. 2001, 2008; Raposo et al. 2014).”
Memory without Feedback in a Neural Network (2009, background): “…of sequence processing that propagate activity through a sequence of metastable states (Hopﬁeld, 1982; Kleinfeld, 1986; Sompolinsky and Kanter, 1986; Rabinovich et al., 2008) and, when accomplished through an intrinsic bursting mechanism, has been suggested to underlie the robustness of temporal…”