Biomedical subjects

E Guigon

Publications and source records attributed to E Guigon.

10 recordsLinked to original sources

PubMed2001

Recoding arm position to learn visuomotor transformations.

There is strong experimental evidence that guiding the arm toward a visual target involves an initial vectorial transformation from direction in visual space to direction in motor space. Constraints on this transformation are imposed (i) by the neural codes for incoming information: the desired movement direction is thought to be signalled by populations of broadly tuned neurons and arm position by populations of monotonically tuned neurons; and (ii) by the properties of outgoing information: the actual movement direction results from the collective action of broadly tuned neurons whose preferred directions rotate with the position of the arm. A neural network model is presented that computes the visuomotor mapping, given these constraints. Appropriate operations are learned by the network in an unsupervised fashion through repeated action- perception cycles by recoding the arm-related proprioceptive information. The resulting solution has two interesting properties: (i) the required transformation is executed accurately over a large part of the reaching space, although few positions are actually learned; and (ii) properties of single neurons and populations in the network closely resemble those of neurons and populations in parietal and motor cortical regions. This model thus suggests a realistic scenario for the calculation of coordinate transformations and initial motor command for arm reaching movements.

Arm↗

PubMed1999

Parieto-frontal coding of reaching: an integrated framework.

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.

Animals↗

PubMed1997

Bistable behaviour in a neocortical neurone model.

Intracellular recordings have shown that neocortical pyramidal neurones have an intrinsic capacity for regenerative firing. The cellular mechanism of this firing was investigated by computer simulations of a model neurone endowed with standard action potential and persistent sodium (gNaP) conductances. The firing mode of the neurone was determined as a function of leakage and NaP maximal conductances (gl and gNaP). The neurone had two stable states of activity (bistable) over wide range of gl and gNaP, one at the resting potential and the other in a regenerative firing mode, that could be triggered by a transient input. This model points to a cellular mechanism that may contribute to the generation and maintenance of long-lasting sustained neuronal discharges in the cerebral cortex.

Action Potentials↗

PubMed1997

Visuomotor transformations for reaching to memorized targets: a PET study.

Positron emission tomography (PET) was used to identify cortical and subcortical regions involved in the control of reaching to visual targets. Regional cerebral blood flow (rCBF) was measured in eight healthy subjects using H2(15)O PET during the performance of three different tasks. All tasks required central fixation while a 400-ms target was flashed every 5 s at a random location around a virtual circle centered on the fixation target. Additional instructions differed according to the task: (i) visual detection of the target without overt responses; (ii) immediate pointing to the most recent target in the sequence, and (iii) pointing to the previous target in the sequence. By design, the two motor tasks differed in the cognitive processing required. In each trial of immediate pointing, the spatial location of only the most recent target needed to be processed. In each trial of pointing to the previous, instead, while the most recent target was stored in memory for the movement of the next trial, the previous target had to be retrieved from memory to direct the current movement. Limb trajectories were comparable between the two motor tasks in terms of most spatiotemporal parameters examined. Significant rCBF increases were identified using analysis of covariance and t statistics. Compared with visual detection there was activation of primary sensorimotor cortex, ventrolateral precentral gyrus, inferior frontal gyrus in the opercular region, supramarginal gyrus, and middle occipital gyrus, all these sites in the hemisphere (left) contralateral to the moving limb, and cerebellar vermis, during both immediate pointing and pointing to the previous. During immediate pointing there was additional activation of left inferior parietal lobule close to the intraparietal sulcus, and when compared with pointing to the previous, dorsolateral prefrontal cortex bilaterally. During pointing to the previous, instead, there was additional activation of supplementary motor cortex, anterior and midcingulate, and inferior occipital gyrus in the left hemisphere; superior parietal lobule, supramarginal gyrus, and posterior hippocampus in the right hemisphere; lingual gyri and cerebellar hemispheres bilaterally; anterior thalamus; and pulvinar. The activation of two partially distinct cerebral networks in these two motor tasks reflects the different nature of signal processing involved. In particular, the specific activation of intraparietal sulcus and prefrontal cortex in immediate pointing appears characteristic of a network for visuospatial working memory. By contrast, the corticolimbic network engaged in pointing to the previous could mediate spatial attention and the sequence of encoding, recording, and decoding of spatial memories required by a dual task with two competing targets.

Adult↗

PubMed1996

Functional MR imaging of the human sensorimotor cortex during haptic discrimination.

This study attempted to determine whether haptic discriminations of shape (haptic task) activate the same tissue in the central cortical region of normal human subjects as do finger movements (opposition task). Opposition and haptic tasks both activated the central sulcus, as expected from previous imaging studies. The haptic task activated about 50% of the cortical territory activated by the opposition task. The results suggest that exploratory digital movements performed to collect precise somatosensory information and automatic movements performed during finger positioning activate partially overlapping parts of the sensorimotor cortex.

Brain Mapping↗

PubMed1995

Modelling the acquisition of goal-directed behaviors by populations of neurons.

Recent neurophysiological studies have revealed the patterns of neuronal activity during the acquisition of goal-directed behaviors, both in single cells, and in large populations of neurons. We propose a model which helps three sets of experimental results in the monkey to be understood: (1) activity of single cells vary greatly and only population activities are causally related to behavior. The model shows how a population of stochastic neurons, whose behaviors vary widely, can learn a skilled conditioned movement with only local activity-dependent synaptic changes. (2) typical changes in neuronal activity occur when the rules governing the behavior are changed, i.e. when the relationship between cues and actions to reach a goal changes over time. There are two types of neuronal patterns during changes in reward contingency: a monotonic increasing pattern and a non-monotonic pattern which follows the change in the way the reward is obtained. Units in the model display these two types of change, which correspond to synaptic modifications related to the encoding of the behavioral significance of sensory and motor events. (3) These two patterns of neuronal activity define two populations whose anatomical distributions in the frontal lobe overlap with a gradient organized in the rostro-caudal direction. The model consists of two artificial neural networks, defined by the same set of equations, but which differ in the values of two parameters (P and Q). P defines the adaptive properties of processing units and Q describes the coding of information. The model suggests that a balance in the relative strengths of these parameters distributed along a rostro-caudal gradient can explain the distribution of neuronal types in the frontal lobe of the monkey.

Animals↗

PubMed1994

Neural network models of cortical functions based on the computational properties of the cerebral cortex.

We describe a biologically plausible modelling framework based on the architectural and processing characteristics of the cerebral cortex. Its key feature is a multicellular processing unit (cortical column) reflecting the modular nature of cortical organization and function. In this framework, we describe a neural network model organization and function. In this framework, we describe a neural network model of the neuronal circuits of the cerebral cortex that learn different functions associated with different parts of the cortex: 1) visual integration for invariant pattern recognition, performed by a cooperation between temporal and parietal areas; 2) visual-to-motor transformation for 3D arm reaching movements, performed by parietal and motor areas; and 3) temporal integration and storage of sensorimotor programs, performed by networks linking the prefrontal cortex to associative sensory and motor areas. The architecture of the network is inspired from the features of the architecture of cortical pathways involved in these functions. We propose two rules which describe neural processing and plasticity in the network. The first rule (adaptive tuning if gating) is an analog of operant conditioning and permits to learn to anticipate an action. The second rule (adaptive timing) is based on a bistable state of activity and permits to learn temporally separate events forming a behavioral sequence.

Cerebral Cortex↗

PubMedDate not supplied

A model study of cellular short-term memory produced by slowly inactivating potassium conductances.

We analyzed the cellular short-term memory effects induced by a slowly inactivating potassium (Ks) conductance using a biophysical model of a neuron. We first described latency-to-first-spike and temporal changes in firing frequency as a function of parameters of the model, injected current and prior history of the neuron (deinactivation level) under current clamp. This provided a complete set of properties describing the Ks conductance in a neuron. We then showed that the action of the Ks conductance is not generally appropriate for controlling latency-to-first-spike under random synaptic stimulation. However, reliable latencies were found when neuronal population computation was used. Ks inactivation was found to control the rate of convergence to steady-state discharge behavior and to allow frequency to increase at variable rates in sets of synaptically connected neurons. These results suggest that inactivation of the Ks conductance can have a reliable influence on the behavior of neuronal populations under real physiological conditions.

Action Potentials↗

PubMedDate not supplied

Neural correlates of learning in the prefrontal cortex of the monkey: a predictive model.

The principles underlying the organization and operation of the prefrontal cortex have been addressed by neural network modeling. The involvement of the prefrontal cortex in the temporal organization of behavior can be defined by processing units that switch between two stable states of activity (bistable behavior) in response to synaptic inputs. Long-term representation of programs requiring short-term memory can result from activity-dependent modifications of the synaptic transmission controlling the bistable behavior. After learning, the sustained activity of a given neuron represents the selective memorization of a past event, the selective anticipation of a future event, and the predictability of reinforcement. A simulated neural network illustrates the abilities of the model (1) to learn, via a natural step-by-step training protocol, the paradigmatic task (delayed response) used for testing prefrontal neurons in primates, (2) to display the same categories of neuronal activities, and (3) to predict how they change during learning. In agreement with experimental data, two main types of activity contribute to the adaptive properties of the network. The first is transient activity time-locked to events of the task and its profile remains constant during successive training stages. The second is sustained activity that undergoes nonmonotonic changes with changes in reward contingency that occur during the transition between stages.

Animals↗

PubMedDate not supplied

Representing spatial information for limb movement: role of area 5 in the monkey.

How is spatial information for limb movement encoded in the brain? Computational and psychophysical studies suggest that beginning hand position, via-points, and target are specified relative to the body to afford a comparison between the sensory (e.g., kinesthetic) reafferences and the commands that generate limb movement. Here we propose that the superior parietal lobule (Brodmann area 5) might represent a substrate for a body-centered positional code. Monkeys made arm movements in different parts of 3D space in a reaction-time task. We found that the activity of area 5 neurons can be related to either the starting point, or the final point, or combinations of the two. Neural activity is monotonically tuned in a body-centered frame of reference, whose coordinates define the azimuth, elevation, and distance of the hand. Each spatial coordinate tends to be encoded in a different subpopulation of neurons. This parcellation could be a neural correlate of the psychophysical observation that these spatial parameters are processed in parallel and largely independent of each other in man.

Animals↗