A neurodynamical cortical model of visual attention and invariant object recognition.

By Gustavo Deco, E. T. Rolls. 2004

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In Cognitive Neuroscience

Citations in the corpus

Physiologically Inspired Model for the Visual Recognition of Transitive Hand Actions (2013, neutral): “Neuralmechanismssupportingsuchcomputational functionshavebeenextensivelystudiedinthecontextofneural modelsforattention(DecoandRolls,2004;Hamker,2006;Tsot-sos,2011).”
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].”
A Feedback Model of Attention Explains the Diverse Effects of Attention on Neural Firing Rates and Receptive Field Structure (2016, background): “Some models implement attention as a top-down modulation of higher-level areas, as suggested by anatomy and physiology [35,36].”
Neurodynamics of biased competition and cooperation for attention: a model with spiking neurons. (2005, background): “…a variety of tasks and at different cognitive neuroscience experimental measurement levels: single cells (Deco and Lee 2002; Rolls and Deco 2002), fMRI (Corchs and Deco 2002, 2004), psychophysics (Deco and Rolls 2004; Deco et al. 2002), and neuropsychology (Deco and Rolls 2002; Heinke et al. 2002).”
Invariant Visual Object and Face Recognition: Neural and Computational Bases, and a Model, VisNet (2012, methodology): “…properties of objects from the temporo-spatial statistics produced by the normal viewing of objects (Wallis and Rolls, 1997; Rolls and Milward, 2000; Stringer and Rolls, 2000, 2002; Rolls and Stringer, 2001, 2006; Elliffe et al., 2002; Rolls and Deco, 2002; Deco and Rolls, 2004; Rolls, 2008b).”
A Theory of Object Recognition: Computations and Circuits in the Feedforward Path of the Ventral Stream in Primate Visual Cortex (2005, background): “, 2002], a model of invariant object recognition (see [Deco and Rolls, 2004] for a recent review).”
Dynamic Computational Time for Visual Attention (2017, background): “Besides attention, human also tend to dynamically allocate different computational time when processing different images [5, 10].”
A computational theory of hippocampal function, and empirical tests of the theory. (2006, background): “…such as the hippocampus and amygdala, may have a number of different functions (Rolls, 1989a, 1989b, 1989d, 1990a, 1990b, 1992, 2005; Rolls and Deco, 2002) including implementing top-down attention by biased competition (Deco and Rolls, 2003, 2004, 2005a; Deco et al., 2005; Rolls and Deco, 2002).”
The Brain's Router: A Cortical Network Model of Serial Processing in the Primate Brain (2010, background): “As in previous works [31,37,44,174], all neurons receive background Poisson inputs with approximate mean conductances”
Why use a connectivity-based approach to study stroke and recovery of function? (2012, positive): “Preliminary support for this framework comes from proof-of-concept studies in neglect and motor deficits (Deco and Rolls, 2004; Grecucci et al., 2008; Krebs et al., 2009; Park et al., 2011; J. Wang et al., 2010).”
Continuous transformation learning of translation invariant representations (2010, methodology): “…but we have extended elsewhere the model to include top-down backprojections to model top-down attentional effects in visual object recognition (Deco and Rolls 2004, 2005; Rolls 2008b), and have postulated elsewhere that top-down backprojections could be useful to guide learning in…”
Imaging retinotopic maps in the human brain (2010, methodology): “entirely from models of visual recognition (Bar et al., 2001; Deco & Rolls, 2004; DiCarlo & Cox, 2007; Riesenhuber & Poggio, 2000).”