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Notes: Abstract  In the last few years, anatomical and physiological studies have provided new insights into the organization of the parieto-frontal network underlying visually guided arm-reaching movements in at least three domains. (1) Network architecture. It has been shown that the different classes of neurons encoding information relevant to reaching are not confined within individual cortical areas, but are common to different areas, which are generally linked by reciprocal association connections. (2) Representation of information. There is evidence suggesting that reach-related populations of neurons do not encode relevant parameters within pure sensory or motor ”reference frames”, but rather combine them within hybrid dimensions. (3) Visuomotor transformation. It has been proposed that the computation of motor commands for reaching occurs as a simultaneous recruitment of discrete populations of neurons sharing similar properties in different cortical areas, rather than as a serial process from vision to movement, engaging different areas at different times. The goal of this paper was to link experimental (neurophysiological and neuroanatomical) and computational aspects within an integrated framework to illustrate how different neuronal populations in the parieto-frontal network operate a collective and distributed computation for reaching. In this framework, all dynamic (tuning, combinatorial, computational) properties of units are determined by their location relative to three main functional axes of the network, the visual-to-somatic, position-direction, and sensory-motor axis. The visual-to-somatic axis is defined by gradients of activity symmetrical to the central sulcus and distributed over both frontal and parietal cortices. At least four sets of reach-related signals (retinal, gaze, arm position/movement direction, muscle output) are represented along this axis. This architecture defines informational domains where neurons combine different inputs. The position-direction axis is identified by the regular distribution of information over large populations of neurons processing both positional and directional signals (concerning the arm, gaze, visual stimuli, etc.) Therefore, the activity of gaze- and arm-related neurons can represent virtual three-dimensional (3D) pathways for gaze shifts or hand movement. Virtual 3D pathways are thus defined by a combination of directional and positional information. The sensory-motor axis is defined by neurons displaying different temporal relationships with the different reach-related signals, such as target presentation, preparation for intended arm movement, onset of movements, etc. These properties reflect the computation performed by local networks, which are formed by two types of processing units: matching and condition units. Matching units relate different neural representations of virtual 3D pathways for gaze or hand, and can predict motor commands and their sensory consequences. Depending on the units involved, different matching operations can be learned in the network, resulting in the acquisition of different visuo-motor transformations, such as those underlying reaching to foveated targets, reaching to extrafoveal targets, and visual tracking of hand movement trajectory. Condition units link these matching operations to reinforcement contingencies and therefore can shape the collective neural recruitment along the three axes of the network. This will result in a progressive match of retinal, gaze, arm, and muscle signals suitable for moving the hand toward the target.

Notes: [Auszug] Complex problem-solving and planning involve the most anterior part of the frontal lobes including the fronto-polar prefrontal cortex (FPPC), which is especially well developed in humans compared with other primates,. The specific role of this region in human cognition, however, is poorly ...

Notes: [Auszug] Visual words that are masked and presented so briefly that they cannot be seen may nevertheless facilitate the subsequent processing of related words, a phenomenon called masked priming,. It has been debated whether masked primes can activate cognitive processes without gaining access to ...

Notes: The amygdala has been shown to respond to many distinct types of affective stimuli, including reward and punishment feedback in animals. In humans, winning and losing situations can be considered as reward and punishment experiences, respectively. In this study, we used functional magnetic resonance imaging (fMRI) to measure regional brain activity when human subjects were given feedback on their performance during a simple response time task in a fictitious competitive tournament. Lexical stimuli were used to convey positive ‘win’ or negative ‘lose’ feedback. The frequency of positive and negative trials was parametrically varied by the experimenters independently from the subjects' actual performance and unbeknownst to them. The results showed that the parametric increase of winning was associated with left amygdala activation whereas the parametric increase of losing was associated with right amygdala activation. These findings provide functional evidence that the human amygdala differentially responds to changes in magnitude of positive or negative reinforcement conveyed by lexical stimuli.

Notes: Abstract. A major issue in cortical physiology and computational neuroscience is understanding the interaction between extrinsic signals from feedforward connections and intracortical signals from lateral connections. We propose here a computational model for motion perception based on the assumption that the local cortical circuits in the medio-temporal area (area MT) implement a Bayesian inference principle. This approach establishes a functional balance between feedforward and lateral, excitatory and inhibitory, inputs. The model reproduces most of the known properties of the neurons in area MT in response to moving stimuli. It accounts for important motion perception phenomena including motion transparency, spatial and temporal integration/segmentation. While integrating several properties of previously proposed models, it makes specific testable predictions concerning, in particular, temporal properties of neurons and the architecture of lateral connections in area MT. In addition, the proposed mechanism is consistent with the known properties of local cortical circuits in area V1. This suggests that Bayesian inference may be a general feature of information processing in cortical neuron populations.