Guy Orban

Bio: Guy Orban is an academic researcher from University of Parma. The author has contributed to research in topics: Visual cortex & Receptive field. The author has an hindex of 93, co-authored 455 publications receiving 26178 citations. Previous affiliations of Guy Orban include Catholic University of Leuven & Université libre de Bruxelles.

Practising orientation identification improves orientation coding in V1 neurons

Abstract: The adult brain shows remarkable plasticity, as demonstrated by the improvement in fine sensorial discriminations after intensive practice. The behavioural aspects of such perceptual learning are well documented, especially in the visual system1,2,3,4,5,6,7,8. Specificity for stimulus attributes clearly implicates an early cortical site, where receptive fields retain fine selectivity for these attributes; however, the neuronal correlates of a simple visual discrimination task remained unidentified. Here we report electrophysiological correlates in the primary visual cortex (V1) of monkeys for learning orientation identification. We link the behavioural improvement in this type of learning to an improved neuronal performance of trained compared to naive neurons. Improved long-term neuronal performance resulted from changes in the characteristics of orientation tuning of individual neurons. More particularly, the slope of the orientation tuning curve that was measured at the trained orientation increased only for the subgroup of trained neurons most likely to code the orientation identified by the monkey. No modifications of the tuning curve were observed for orientations for which the monkey had not been trained. Thus training induces a specific and efficient increase in neuronal sensitivity in V1.

Neuronal Operations in the Visual Cortex

Abstract: 1 The Visual System of Cat and Monkey Compared.- 1.1 The Basic Layout of the Visual System in Cat, Owl Monkey, and Rhesus Monkey.- 1.1.1 The Retina.- 1.1.2 The Optic Chiasm and Optic Tract.- 1.1.3 The Dorsal Lateral Geniculate Nucleus (dLGN).- 1.1.4 Visual Cortex.- 1.1.5 Pulvinar.- 1.1.6 Callosal Connections.- 1.2 Quantitative Aspects of the Retino-Geniculo-Cortical Projections.- 1.2.1 The Overall Numbers of Cells in the Visual Pathway.- 1.2.2 Distribution of Retinal Cell Populations.- 1.2.3 Magnification Factors.- 1.3 Conclusion.- 2 The Visual Cortical Areas of the Cat.- 2.1 Description of the Visual Cortical Areas.- 2.1.1 Area 17: The Prototype of Visual Cortical Areas.- 2.1.2 Areas 18 and 19.- 2.1.3 The Lateral Suprasylvian Areas.- 2.1.4 Areas 20 and 21.- 2.1.5 Additional Visual Areas?.- 2.2 The Levels of Processing in the Visual Cortical System of the Cat.- 2.3 Additional Observations on the Retinotopic Organization in the Primary Complex.- 2.3.1 Variability of the 3 Cortical Maps.- 2.3.2 RF Scatter.- 2.3.3 The 17-18 Border and the Question of the Naso-Temporal Overlap.- 2.3.4 The 18-19 Border and the Question of the Visual Field Islands.- 2.4 Conclusion.- 3 Afferent Projections to Areas 17, 18, 19 of the Cat: Evidence for Parallel Input.- 3.1 The Relay of Retinal Afferents: The Dorsal Lateral Geniculate Nuclear Complex.- 3.2 The Geniculocortical Projection.- 3.3 Functional Streams in the Retino-Geniculocortical Projection.- 3.3.1 Functional Properties of Retinal and Geniculate X, Y, W Cells.- 3.3.2 Correlation with Retinal Morphology.- 3.3.3 Separation of Functional Streams at LGN Level.- 3.3.4 Correlation with LGN Morphological Types.- 3.3.5 Distribution of Functional Streams in dLGN Nuclear Complex.- 3.3.6 Input to Different Areas of Primary Visual Complex.- 3.4 Physiological Identification of the Functional Type of Afferents to Areas 17, 18 and 19.- 3.5 The Termination of Geniculate Afferents in the Visual Cortex.- 3.6 Other Subcortical Afferents: Pulvinar-Lateralis Posterior Complex, Intralaminar Nuclei, Claustrum, and Brainstem.- 3.7 The Ipsilateral Corticocortical Connections.- 3.8 The Connections Through the Corpus Callosum.- 3.9 Conclusion.- 4 Receptive Field Organization in Areas 17, 18 and 19 of the Cat.- 4.1 Twenty Years with the Simple-Complex-Hypercomplex Scheme.- 4.2 Criteria for Classifying Cortical RFs.- 4.2.1 The ON-OFF Overlap or the Parcellation of the RF into Subregions.- 4.2.2 Position Test.- 4.2.3 RF Dimensions.- 4.2.4 End-Stopping or the Hypercomplex Property.- 4.3 The A, B, C, S Scheme.- 4.3.1 Properties and Distribution of Cell Types.- 4.3.2 The S and A Families.- 4.3.3 Responses to Other Stimuli.- 4.4 Correspondence of the A, B, C, S Scheme with Other Classification Schemes.- 4.5 Conclusion.- 5 Parameter Specificity of Visual Cortical Cells and Coding of Visual Parameters.- 5.1 The Tuned Cells as Bandpass Filters: The Multichannel Representation of a Parameter.- 5.2 Are All Tuned Cells Simple (Passive) Bandpass Filters or Are Some of Them Active Filters?.- 5.3 Cells with Thresholds as High-Pass Filters: Single or Multichannel Representation of a Parameter.- 5.4 Conclusion.- 6 Influence of Luminance and Contrast on Cat Visual Cortical Neurons.- 6.1 Contrast-Response Curves Obtained with Sinusoidal Gratings.- 6.2 Contrast-Response Curves Obtained with Slits.- 6.3 The Extreme Contrast Sensitivity at the 18-19 Border.- 6.4 Influence of Contrast and Luminance on Other Response Properties.- 6.5 Conclusion.- 7 Coding of Spatial Parameters by Cat Visual Cortical Neurons: Influence of Stimulus Orientation, Length, Width, and Spatial Frequency.- 7.1 Orientation Tuning of Cortical Cells.- 7.1.1 Definitions and Criteria.- 7.1.2 Quantitative Determinations: Orientation-Response Curves.- 7.1.3 Qualitative Determination: Hand-Plotting.- 7.1.4 Distribution of Preferred Orientations.- 7.1.5 Orientation Columns.- 7.1.6 Conclusion.- 7.2 Influence of Stimulus Length on Cortical Cells.- 7.3 Selectivity of Cortical Neurons for Spatial Frequency and Stimulus Width.- 7.3.1 Selectivity for Spatial Frequency.- 7.3.2 Spatial Frequency and Coding of Stimulus Dimensions.- 7.3.3 Linearity of Cortical Cells.- 7.3.4 The Visual Cortex as a Fourier Analyzer.- 7.3.5 Spatial Frequency: Conclusion.- 7.4 Spatial Parameters: Conclusion.- 8 Coding of Spatio-Temporal Parameters by Cat Visual Cortical Neurons: Influence of Stimulus Velocity Direction and Amplitude of Movement.- 8.1 Influence of Stimulus Velocity.- 8.2 Influence of the Direction of Movement.- 8.3 Influence of Stimulus Movement Amplitude.- 8.4 Conclusion.- 9 Binocular Interactions in Cat Visual Cortical Cells and Coding of Parameters Involved in Static and Dynamic Depth Perception.- 9.1 The Binocularity of Cortical Cells and the Ocular Dominance Scheme.- 9.2 Position Disparity Tuning Curves and the Coding of Static Depth.- 9.3 Orientation Disparity, Another Mechanism for Static Depth Discrimination?.- 9.4 Neuronal Mechanisms Underlying Dynamic Depth Perception (Motion in Depth).- 9.5 Conclusion.- 10 The Output of the Cat Visual Cortex.- 10.1 The Projections of Layer V to the Superior Colliculus, Pons, Pretectum, and Pulvinar-LP Complex.- 10.2 The Projections of Layer VI to the dLGN and the Claustrum.- 10.3 The Commissural Projections.- 10.4 The Associative Corticocortical Projections.- 10.5 Conclusion.- 11 Correlation Between Geniculate Afferents and Visual Cortical Response Properties in the Cat.- 11.1 Electrical Stimulation of the Visual Pathways.- 11.2 The Question of ON or OFF Cell Input to Cortical S Cells.- 11.3 Other Attempts to Identify the LGN Input to Cortical Cells.- 11.4 Conclusion.- 12 Intracortical Mechanisms Underlying Properties of Cat Visual Cortical Cells.- 12.1 The Role of Intracortical Inhibition.- 12.1.1 Orientation Selectivity.- 12.1.2 Direction Selectivity.- 12.1.3 End-Stopping.- 12.1.4 Ocular Dominance.- 12.1.5 Velocity Upper Cut-Off.- 12.1.6 Absence of Response to Two-Dimensional Noise.- 12.2 Properties of the Intracortical Inhibitions.- 12.3 The Structural Counterpart of Inhibitions.- 12.4 Conclusion.- 13 Non-Visual Influences on Cat Visual Cortex.- 13.1 Non-Visual Sensory Inputs to the Visual Cortex.- 13.2 Influence of Eye Movements on Visual Cortical Cells.- 13.3 The Influence of Sleep and Anesthesia.- 14 Response Properties of Monkey Striate Neurons.- 14.1 Retinotopic Organization of Area 17.- 14.2 The Input-Output Relations of Monkey Striate Cortex.- 14.3 Receptive Field Organization and Size.- 14.4 Color Specificity in Monkey Striate Cortex.- 14.5 Influence of Light Intensity and Contrast on Monkey Striate Neurons.- 14.6 Influence of Spatial Parameters.- 14.7 Influence of Spatio-Temporal Parameters.- 14.8 Ocular Dominance Distribution and Depth Sensitivity.- 14.9 Columnar Organization and Functional Architecture of Striate Cortex.- 14.10 Correlation Between Response Properties and Afferent Input.- 14.11 Conclusion.- 15 Conclusion: Signification of Visual Cortical Function in Perception.- 15.1 Operating Principles in Cat Visual Cortex.- 15.1.1 Retinotopic Organization.- 15.1.2 Filtering.- 15.1.3 "Columnar" Organization.- 15.1.4 Distributed Processing in the Primary Complex.- 15.1.5 Changes with Eccentricity.- 15.1.6 Parallel Streams Within each Area.- 15.2 The Cat and Monkey Visual Cortex as a Model: The Question of the Relationship Between Animal Physiology and Human Visual Perception.- 15.3 The Role of the Primary Visual Cortex in Visual Perception: The Significance of Parameter Specificities for Object Recognition.- References.

Parietal Representation of Symbolic and Nonsymbolic Magnitude

Abstract: The close behavioral parallels between the processing of quantitative information conveyed by symbolic and nonsymbolic stimuli led to the hypothesis that there exists a common cerebral representation of quantity (Dehaene, Dehaene-Lambertz, & Cohen, 1998). The neural basis underlying the encoding of number magnitude has been localized to regions in and around the intraparietal sulcus (IPS) by brain-imaging studies. However, it has never been demonstrated that these same regions are also involved in the quantitative processing of nonsymbolic stimuli. Using functional brain imaging, we explicitly tested the hypothesis of a common substrate. Angles, lines, and two-digit numbers were presented pairwise, one to the left and one to the right of the fixation point. In the three comparison tasks, participants (n = 18) pressed the key on the side of the largest quantity. In the three control tasks, they indicated the side on which dimming occurred. A conjunction analysis between the three subtractions (comparison task-control task) revealed a site in left IPS that is specifically responsive when two stimuli have to be compared quantitatively, irrespective of stimulus format. The results confirm the hypothesis that quantity is represented by a common mechanism for both symbolic and nonsymbolic stimuli in IPS. In addition, the interaction between task and type of stimulus identified a region anterior to the conjunction site, not specific for quantitative processing, but reflecting general processes loaded by number processing.

疟原虫var基因转换速率变化导致抗原变异[英]／Paul H, Robert P, Christodoulou Z, et al//Proc Natl Acad Sci U S A

Abstract: 抗原变异可使得多种致病微生物易于逃避宿主免疫应答。表达在感染红细胞表面的恶性疟原虫红细胞表面蛋白1（PfPMP1）与感染红细胞、内皮细胞、树突状细胞以及胎盘的单个或多个受体作用，在黏附及免疫逃避中起关键的作用。每个单倍体基因组var基因家族编码约60种成员，通过启动转录不同的var基因变异体为抗原变异提供了分子基础。

Control of goal-directed and stimulus-driven attention in the brain

Abstract: We review evidence for partially segregated networks of brain areas that carry out different attentional functions. One system, which includes parts of the intraparietal cortex and superior frontal cortex, is involved in preparing and applying goal-directed (top-down) selection for stimuli and responses. This system is also modulated by the detection of stimuli. The other system, which includes the temporoparietal cortex and inferior frontal cortex, and is largely lateralized to the right hemisphere, is not involved in top-down selection. Instead, this system is specialized for the detection of behaviourally relevant stimuli, particularly when they are salient or unexpected. This ventral frontoparietal network works as a 'circuit breaker' for the dorsal system, directing attention to salient events. Both attentional systems interact during normal vision, and both are disrupted in unilateral spatial neglect.

The Brain's Default Network Anatomy, Function, and Relevance to Disease

Abstract: Thirty years of brain imaging research has converged to define the brain’s default network—a novel and only recently appreciated brain system that participates in internal modes of cognition Here we synthesize past observations to provide strong evidence that the default network is a specific, anatomically defined brain system preferentially active when individuals are not focused on the external environment Analysis of connectional anatomy in the monkey supports the presence of an interconnected brain system Providing insight into function, the default network is active when individuals are engaged in internally focused tasks including autobiographical memory retrieval, envisioning the future, and conceiving the perspectives of others Probing the functional anatomy of the network in detail reveals that it is best understood as multiple interacting subsystems The medial temporal lobe subsystem provides information from prior experiences in the form of memories and associations that are the building blocks of mental simulation The medial prefrontal subsystem facilitates the flexible use of this information during the construction of self-relevant mental simulations These two subsystems converge on important nodes of integration including the posterior cingulate cortex The implications of these functional and anatomical observations are discussed in relation to possible adaptive roles of the default network for using past experiences to plan for the future, navigate social interactions, and maximize the utility of moments when we are not otherwise engaged by the external world We conclude by discussing the relevance of the default network for understanding mental disorders including autism, schizophrenia, and Alzheimer’s disease