Neurodynamics of biased competition and cooperation for attention: a model with spiking neurons.

By Gustavo Deco, E. T. Rolls. 2005

249 citations recorded by the source corpus.

Subfields: Cognitive Neuroscience, Electrical and Electronic Engineering

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Cognitive Neuroscience

In Electrical and Electronic Engineering

Citations in the corpus

Deep learning in neural networks: An overview (2014, neutral): “…proposed and analyzed (e.g., Amit & Brunel, 1997; Bohte, Kok, & La Poutre, 2002; Brea, Senn, & Pfister, 2013; Brette et al., 2007; Brunel, 2000; Deco & Rolls, 2005; Gerstner & Kistler, 2002; Gerstner & van Hemmen, 1992; Hoerzer, Legenstein, & Maass, 2014; Izhikevich et al., 2003; Kasabov,…”
Visual attention: The past 25 years (2011, methodology): “In most neural implementations of the biased competition model, nodes within each processing stage compete by inhibiting the output activity generated by neighboring nodes (e.g., Corchs & Deco, 2002; Deco & Rolls, 2005).”
Neuromorphic Electronic Circuits for Building Autonomous Cognitive Systems (2014, background): “…can be integrated to form multi-chip spiking recurrent and WinnerTake-All neural networks, which in turn have been proposed as neural models for explaining pattern recognition [24], [25], working memory [9], [26], decision making [27], [28] and state-dependent computation [29], [30] in the brain.”
The Normalization Model of Attention (2009, methodology): “The model is also related to a number of more detailed models that have been proposed as biophysically plausible implementations of biased competition or the feature-similarity gain principle (Ardid et al., 2007; Deco and Rolls, 2005; Hamker and Zirnsak, 2006; Spratling and Johnson, 2004; Tiesinga e”
The Noisy Brain: Stochastic Dynamics as a Principle of Brain Function (2010, unknown): “The details of the mathematical formulation are summarized in previous publications ( Brunel and Wang 2001 , Deco and Rolls 2005b , Deco and Rolls 2006 ) , and are provided in Section 2 .”
A computational theory of hippocampal function, and empirical tests of the theory. (2006, background): “Another type of sequence memory uses synaptic adaptation to effectively encode the order of the items in a sequence (Deco and Rolls, 2005c).”
Reconciling Predictive Coding and Biased Competition Models of Cortical Function (2008, methodology): “The simplest mechanism, which is used in many previous models of biased competition (e.g., Corchs and Deco, 2002; Deco et al., 2002; Deco and Rolls, 2005; Hahnloser et al., 2002; Phaf et al., 1990; Usher and Niebur, 1996), is to have the top-down bias add to the node activations.”
The orbitofrontal cortex and beyond: from affect to decision-making. (2008, methodology): “This is computationally appropriate in top-down biased competition models of attention (Deco and Rolls, 2005a; Rolls, 2008e; Rolls and Deco, 2002).”
Invariant Visual Object and Face Recognition: Neural and Computational Bases, and a Model, VisNet (2012, background): “The computational mechanisms of attention, including top-down biased competition, are described elsewhere (Rolls and Deco, 2002; Deco and Rolls, 2005b; Rolls, 2008b).”
The Dynamic Brain: From Spiking Neurons to Neural Masses and Cortical Fields (2008, methodology): “The mean-field approach has been applied to biased-competition and cooperation networks and has been used to model single neuronal responses, fMRI activation patterns, psychophysical measurements, effects of pharmacological agents, and effects of local cortical lesions [6,24–33].”