Computational neuroscience of vision

By E. T. Rolls, Gustavo Deco. 2002

545 citations recorded by the source corpus.

Subfields: Cognitive Neuroscience, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/3a2b6d523914aba26a46f6f5d5a1f976304793c9

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Citations in the corpus

The Representation of Information About Taste and Odor in the Orbitofrontal Cortex (2010, methodology): “The information theoretic approach and its application to the analysis of neuronal data are described in detail elsewhere ( Rolls and Treves 1998 ; Rolls and Deco 2002 ; Rolls 2008a ) .”
Neural correlates of rapid reversal learning in a simple model of human social interaction (2003, background): “…neurophysiological studies in nonhuman primates (Bruce et al., 1981; Desimone and Gross, 1979; Hasselmo et al., 1989a, 1989b; Perrett et al., 1982; Rolls and Deco, 2002), and from human lesion (Adolphs et al., 1994; Bodamer, 1947; Sergent and Villemure, 1989) and imaging studies (Haxby et al.,…”
Invariant Object Recognition with Slow Feature Analysis (2008, background): “VisNet is the most prominent example of hierarchical feed-forward neural network models of the primate ventral visual system [1].”
Continuous transformation learning of translation invariant representations (2010, methodology): “…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 hierarchical networks (Rolls and Treves 1998; Rolls and Deco 2002).”
A neurodynamical cortical model of visual attention and invariant object recognition. (2004, methodology): “…2002; Wallis & Rolls, 1997), with attentional phenomena such as objectcued and space-cued search which can be implemented in a system with backprojections and a dorsal as well as a ventral stream of cortical processing (Corchs & Deco, 2002; Deco & Lee, 2002; Deco & Zihl, 2001; Rolls & Deco, 2002).”
TRoPICALS: a computational embodied neuroscience model of compatibility effects. (2010, background): “In fact the information processed by the two pathways is partially integrated not only in the late frontal stages of sensorimotor information flow, but also through connections existing between early visual stages (Rolls & Deco, 2002) and in the PC (Gallese, Craighero, Fadiga, & Fogassi, 1999).”
Invariant Visual Object and Face Recognition: Neural and Computational Bases, and a Model, VisNet (2012, methodology): “FEATURE HIERARCHIES AND 2D VIEW-BASED OBJECT RECOGNITION Another approach, and one that is much closer to what appears to be present in the primate ventral visual system (Wurtz and Kandel, 2000a; Rolls and Deco, 2002; Rolls, 2008b), is a feature hierarchy system (see Figure 9).”
The Dynamic Brain: From Spiking Neurons to Neural Masses and Cortical Fields (2008, background): “This means that the spatiotemporal spike patterns produced by neural circuits convey information among neurons; this is the microscopic level on which the brain’s representations and computations rest [6].”
Multiclass Object Recognition with Sparse, Localized Features (2006, background): “In biological vision this attentional search appears to be driven by a complex range of saliency measures [22].”
1 On what it means to see , and what we can do about it (2008, unknown): “…by isocortical structures that consist of the primary visual areas in the occipital lobe and the high-level areas in the temporal and parietal lobes (Rolls and Deco, 2001), and the frontal lobe, the visual functions of which include exerting contextual influence on the interpretation of the…”
A computational theory of hippocampal function, and empirical tests of the theory. (2006, methodology): “…are brought together in a single network, for it is only when both types of information are in the same network that spatial information can be retrieved from object information, and vice versa, which is a fundamental property of episodic memory (Rolls and Deco, 2002; Rolls and Treves, 1998).”
Towards Scalable Representations of Object Categories: Learning a Hierarchy of Parts (2007, background): “Parts and their higher level combinations should be learned in an unsupervised manner (at least in the first stages of the hierarchy) in order to avoid hand-labelling of massive image data as well as to capture the regularities within the visual data as effectively and compactly as possible [3, 17, ”
STDP-based spiking deep neural networks for object recognition (2016, background): “Taken together, our results suggest that the combination of STDP with latency coding may be a key to understanding the way that the primate visual system learns, its remarkable processing speed and its low energy consumption.”
Neurodynamics of biased competition and cooperation for attention: a model with spiking neurons. (2005, methodology): “…visual attention in 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…”
The mechanisms for pattern completion and pattern separation in the hippocampus (2013, background): “Treves, 1998; Rolls and Deco, 2002, 2010; Rolls, 2010a) including Memory, Attention, and Decision-Making (Rolls, 2008)].”
A High-Throughput Screening Approach to Discovering Good Forms of Biologically Inspired Visual Representation (2009, background): “contrast gain control mechanisms in cortical area V1, and elsewhere [1, 2])”
Intersensory binding across space and time: A tutorial review (2013, unknown): “This model fits nicely with related biological mechanisms (Ben-Yishai, Bar-Or & Sompolinsky, 1995; Ghazanfar & Schroeder, 2006; Georgopoulos, Taira & Lukashin, 1993; Martuzzi, Murray, Michel, Thiran, Maeder, Clarke & Meuli, 2007; Rolls & Deco, 2002; Schroeder & Foxe, 2005).”
The orbitofrontal cortex and beyond: from affect to decision-making. (2008, methodology): “How might this rapid stimulus-reinforcer association learning and reversal be implemented at the neuronal and neuronal network level? One mechanism could be implemented by Hebbian modification of synapses conveying visual input onto taste-responsive neurons, implementing a pattern association networ”
A Theory of Object Recognition: Computations and Circuits in the Feedforward Path of the Ventral Stream in Primate Visual Cortex (2005, methodology): “We plan to simulate learning of the wiring at the level of C units in later work using the “trace” rule (see [Földiák, 1991; Rolls and Deco, 2002] for a plausibility proof).”
Computational models of schizophrenia and dopamine modulation in the prefrontal cortex (2008, background): “The operation and properties of attractor networks are described in Box 1 and more fully elsewher”