Home Research Teaching Pub Lib Links

Copyright Notice: The documents distributed here have been provided as a means to ensure timely dissemination of scholarly and technical work on a noncommercial basis. Copyright and all rights therein are maintained by the authors or by other copyright holders, notwithstanding that they have offered their works here electronically. It is understood that all persons copying this information will adhere to the terms and constraints invoked by each author's copyright. These works may not be reposted without the explicit permission of the copyright holder.

Taïx M, Tran MT, Souères P, Guigon E (2012) Generating human-like reaching movements with a humanoid robot: A computational approach. Journal of Computational Science, in press. abstract
Rigoux L, Guigon E (2012) A model of reward- and effort-based optimal decision making and motor control. PLoS Computational Biology, in press. abstract
Reinkensmeyer DJ, Guigon E, Maier MA (2012) A computational model of use-dependent motor recovery following a stroke: Optimizing corticospinal activations via reinforcement learning can explain residual capacity and other strength recovery dynamics. Neural Networks 29-30:60-69. abstract
Guigon E (2010) Active control of bias for the control of posture and movement. Journal of Neurophysiology 104(2):1090-1102. abstract
Guigon E (2011) Models and architectures for motor control: Simple or complex? In: Motor Control, Chp 20 (Danion F, Latash ML, eds), pp 478-502. Oxford, UK: Oxford University Press.
Guigon E (2010) Active control of bias for the control of posture and movement. Journal of Neurophysiology 104(2):1090-1102. abstract
Genet S, Sabarly L, Guigon E, Delord B, Berry H (2010) Dendritic signals command firing dynamics in a mathematical model of cerebellar Purkinje cell. Biophysical Journal 99(2):427-436. abstract
Guigon E (2009) Computational and neural principles for human motor control. Mémoire d'Habilitation à Diriger des Recherches.
Guigon E, Baraduc P, Desmurget M (2008) Computational motor control: Feedback and accuracy. European Journal of Neuroscience 27(4):1003-1016. abstract
Guigon E, Baraduc P, Desmurget M (2008) Optimality, stochasticity, and variability in motor behavior. Journal of Computational Neuroscience 24(1):57-68. abstract
Tran MT, Souères P, Taïx M, Guigon E (2008) A principled approach to biological motor control for generating humanoid robot reaching movement In: Proc IEEE RAS/EMBS International Conference on Biomedical Robotics and Biomechatronics, pp 783-788.
Guigon E, Baraduc P, Desmurget M (2007) Computational motor control: Redundancy and invariance. Journal of Neurophysiology 97(1):331-347. abstract F1000 Biology
Guigon E, Baraduc P, Desmurget M (2007) Coding of movement- and force-related information in primate primary motor cortex: A computational approach. European Journal of Neuroscience 26(1):250-260. abstract F1000 Biology
Delord B, Berry H, Guigon E, Genet S (2007) A new principle for information storage in an enzymatic pathway model. PLoS Computational Biology 3(6):e124. abstract
Desmurget M, Baraduc P, Guigon E (2005) The planning and control of reaching and grasping movements. In: Higher-Order Motor Disorders: From Neuroanatomy and Neurobiology to Clinical Neurology, Chp 3 (Freund H-J, Jeannerod M, Hallett M, Leiguarda R, eds), pp 43-55. Oxford, UK: Oxford University Press.
Fortier PA, Guigon E, Burnod Y (2005) Supervised learning in a recurrent network of rate-model neurons exhibiting frequency-adaptation. Neural Computation 17(9):2060-2076. abstract
Guigon E (2004) Interpolation and extrapolation in human behavior and neural networks. Journal of Cognitive Neuroscience 16(3):382-389. abstract
Brette R, Guigon E (2003) Reliability of spike timing is a general property of spiking model neurons. Neural Computation 15(2):279-308. abstract
Guigon E, Baraduc P, Desmurget M (2003) Constant effort computation as a determinant of motor behavior. Advances in Computational Motor Control II, Symposium at the Society for Neuroscience Conference (Todorov E, Shadmehr R, organizers). PPT presentation
Guigon E (2003) Computing with populations of monotonically tuned neurons. Neural Computation 15(9):2115-2127. abstract
Baraduc P, Guigon E (2002) Population computation of vectorial transformations. Neural Computation 14(4):845-871. abstract
Dreher J-C, Guigon E, Burnod Y (2002) A model of prefrontal cortex dopaminergic modulation during the delayed alternation task. Journal of Cognitive Neuroscience 14(6):853-865. abstract
Guigon E, Baraduc P (2002) A neural model of perceptual-motor alignment. Journal of Cognitive Neuroscience 14(4):538-549. abstract
Guigon E, Koechlin E, Burnod Y (2002) Short-term memory. In: The Handbook of Brain Theory and Neural Networks, 2nd ed (Arbib MA, ed), pp 1030-1034. Cambridge: MIT Press. The MIT Press (c)
Baraduc P, Guigon E, Burnod Y (2001) Recoding arm position to learn visuomotor transformations. Cerebral Cortex 11(10):906-917. abstract Erratum
Delord B, Baraduc P, Costalat R, Burnod Y, Guigon E (2000) A model study of cellular short-term memory produced by slowly inactivating potassium conductances. Journal of Computational Neuroscience 8(3):251-273. abstract
Baraduc P, Guigon E, Burnod Y (1999) Where does the population vector of motor cortical cells point during arm reaching movements? In: Advances in Neural Information Processing Systems, Vol 11 (Kearns MJ, Solla SA, Cohn DA, eds), pp 83-89. Cambridge: MIT Press.
Burnod Y, Baraduc P, Battaglia-Mayer A, Guigon E, Koechlin E, Ferraina S, Lacquaniti F, Caminiti R (1999) Parieto-frontal coding of reaching: an integrated framework. Experimental Brain Research 129(3):325-346. abstract
Delord B, Klaassen AJ, Burnod Y, Costalat R, Guigon E (1997) Bistable behaviour in a neocortical neurone model. NeuroReport 8(4):1019-1023. abstract
Lacquaniti F, Perani D, Guigon E, Bettinardi V, Carrozzo M, Grassi F, Rossetti Y, Fazio F (1997) Visuomotor transformations for reaching to memorized targets: A PET study. NeuroImage 5(2):129-146. abstract
Anton J-L, Benali H, Guigon E, DiPaola M, Bittoun J, Jolivet O, Burnod Y (1996) Functional MR imaging of the human sensorimotor cortex during haptic discrimination. NeuroReport 7(18):2849-2852. abstract
Guigon E, Burnod Y (1995) Modelling the acquisition of goal-directed behaviors by population of neurons. International Journal of Psychophysiology 19(2):103-113. abstract
Guigon E, Burnod Y (1995) Short-term memory. In: The Handbook of Brain Theory and Neural Networks (Arbib MA, ed), pp 867-871. Cambridge: MIT Press.
Guigon E, Dorizzi B, Burnod Y, Schultz W (1995) Neural correlates of learning in the prefrontal cortex of the monkey: A predictive model. Cerebral Cortex 5(2):135-147. abstract
Lacquaniti F, Guigon E, Bianchi L, Ferraina S, Caminiti R (1995) Representing spatial information for limb movement: Role of area 5 in the monkey. Cerebral Cortex 5(5):391-409. abstract
Guigon E, Grandguillaume P, Otto I, Boutkhil L, Burnod Y (1994) Neural network models of cortical functions based on the computational properties of the cerebral cortex. Journal of Physiology (Paris) 88(5):291-308. abstract
Guigon E (1993) Modélisation des propriétés du cortex cérébral - Comparaison entre aires visuelles, motrices et préfrontales. Unpublished doctoral dissertation, Ecole Centrale Paris, Chatenay-Malabry. abstract
Otto I, Guigon E, Boutkhil L, Grandguillaume P, Burnod Y (1992) Direct and indirect cooperation between temporal and parietal networks for invariant visual recognition. Journal of Cognitive Neuroscience 4(1):35-57. abstract


















Rigoux L, Guigon E (2012) A model of reward- and effort-based optimal decision making and motor control. PLoS Computational Biology, in press.
Costs (e.g. energetic expenditure) and benefits (e.g. food) are central determinants of behavior. In ecology and economics, they are combined to form a utility function which is maximized to guide choices. This principle is widely used in neuroscience as a normative model of decision and action, but current versions of this model fail to consider how decisions are actually converted into actions (i.e. the formation of trajectories). Here, we describe an approach where decision making and motor control are optimal, iterative processes derived from the maximization of the discounted, weighted difference between expected rewards and foreseeable motor efforts. The model accounts for decision making in cost/benefit situations, and detailed characteristics of control and goal tracking in realistic motor tasks. As a normative construction, the model is relevant to address the neural bases and pathological aspects of decision making and motor control. pdf

Guigon E (2010) Active control of bias for the control of posture and movement. Journal of Neurophysiology 104(2):1090-1102.
Posture and movement are fundamental, intermixed components of motor coordination. Current approaches consider either that (1) movement is an active, anticipatory process, and posture a passive feedback process, or (2) movement and posture result from a common passive process. In both cases, the presence of a passive component renders control scarcely robust and stable in the face of transmission delays and low feedback gains. Here we show in a model that posture and movement could result from the same active process: an optimal feedback control that drives the body from its estimated state to its goal in a given (planning) time by acting through muscles on the insertion position (bias) of compliant linkages (tendons). Computer simulations show that iteration of this process in the presence of noise indifferently produces realistic postural sway, fast goal-directed movements, and natural transitions between posture and movement. pdf

Genet S, Sabarly L, Guigon E, Delord B, Berry H (2010) Dendritic signals command firing dynamics in a mathematical model of cerebellar Purkinje cell. Biophysical Journal 99(2):427-436.
Dendrites of cerebellar Purkinje cells (PCs) respond to brief excitations from parallel fibers with lasting plateau depolarizations. It is unknown whether these plateaus are local events that boost the synaptic signals or they propagate to the soma and directly take part in setting the cell firing dynamics. To address this issue, we analyzed a likely mechanism underlying plateaus in three representations of a reconstructed PC with increasing complexity. Analysis in an infinite cable suggests that Ca plateaus triggered by direct excitatory inputs from parallel fibers and their mirror signals, valleys (putatively triggered by the local feed forward inhibitory network), cannot propagate. However, simulations of the model in electrotonic equivalent cables prove that Ca plateaus (resp. valleys) are conducted over the entire cell with velocities typical of passive events once they are triggered by threshold synaptic inputs that turn the membrane current inward (resp. outward) over the whole cell surface. Bifurcation analysis of the model in equivalent cables, and simulations in a fully reconstructed PC both indicate that dendritic Ca plateaus and valleys, respectively, command epochs of firing and silencing of PCs. pdf

Guigon E, Baraduc P, Desmurget M (2008) Computational motor control: Feedback and accuracy. European Journal of Neuroscience 27(4):1003-1016.
Speed/accuracy trade-off is a ubiquitous phenomenon in motor behavior, which has been ascribed to the presence of signal-dependent noise in motor commands. Although this explanation can provide a quantitative account of many aspects of motor variability, including Fitts' law, the fact that this law is frequently violated, e.g. during the acquisition of new motor skills, remains unexplained. Here, we describe a principled approach to the influence of noise on motor behavior, in which motor variability results from the interplay between sensory and motor execution noises in an optimal feedback-controlled system. In this framework, we first show that Fitts' law arises due to signal-dependent motor noise when sensory (proprioceptive) noise is low, e.g. under visual feedback. Then we show that the terminal variability of nonvisually guided movement can be explained by the presence of signal-dependent proprioceptive noise. Finally, we show that movement accuracy can be controlled by opposite changes in signal-dependent sensory and motor noise, a phenomenon which could be ascribed to muscular cocontraction. As the model also explains kinematics, kinetics, muscular, and neural characteristics of reaching movements, it provides a unified framework to address motor variability. pdf

Guigon E, Baraduc P, Desmurget M (2008) Optimality, stochasticity, and variability in motor behavior. Journal of Computational Neuroscience 24(1):57-68.
Recent theories of motor control have proposed that the nervous system acts as a stochastically optimal controller, i.e. it plans and executes motor behaviors taking into account the nature and statistics of noise. Detrimental effects of noise are converted into a principled way of controlling movements. Attractive aspects of such theories are their ability to explain not only characteristic features of single motor acts, but also statistical properties of repeated actions. Here, we present a critical analysis of stochastic optimality in motor control which reveals several difficulties with this hypothesis. We show that stochastic control may not be necessary to explain the stochastic nature of motor behavior, and we propose an alternative framework, based on the action of a deterministic controller coupled with an optimal state estimator, which relieves drawbacks of stochastic optimality and appropriately explains movement variability. pdf

Guigon E, Baraduc P, Desmurget M (2007) Computational motor control: Redundancy and invariance. Journal of Neurophysiology 97(1):331-347.
The nervous system controls the behavior of complex redundant biomechanical systems. How it computes appropriate commands to generate movements is unknown. Here we show in a model that characteristic features of redundant movements can be reproduced based on the assumption that the nervous system processes static (e.g. gravitational) and dynamic (e.g. inertial) forces separately, and maximizes the efficiency of its dynamic commands. Furthermore, amplitude/duration scaling and kinematic invariance arise when the size of the dynamic commands (effort) is constant. pdf

Guigon E, Baraduc P, Desmurget M (2007) Coding of movement- and force-related information in primate primary motor cortex: A computational approach. European Journal of Neuroscience 26(1):250-260.
Coordinated movements result from descending commands transmitted by central motor systems to the muscles. Although the resulting effect of the commands has the dimension of a muscular force, it is unclear whether the information transmitted by the commands concerns movement kinematics (e.g. position, velocity) or movement dynamics (e.g. force, torque). To address this issue, we used an optimal control model of movement production which calculates inputs to motoneurons which are appropriate to drive an articulated limb toward a goal. The model quantitatively accounted for kinematic, kinetic and muscular properties of planar, shoulder/elbow arm reaching movements of monkeys, and reproduced detailed features of neuronal correlates of these movements in primate motor cortex. The model also reproduced qualitative spatio-temporal characteristics of movement- and force-related single neuron discharges in nonplanar reaching and isometric force production tasks. The results suggest that the nervous system of the primate controls movements through a muscle-based controller which could be located in the motor cortex. pdf

Delord B, Berry H, Guigon E, Genet S (2007) A new principle for information storage in an enzymatic pathway model. PLoS Computational Biology 3(6):e124. pdf

Fortier PA, Guigon E, Burnod Y (2005) Supervised learning in a recurrent network of rate-model neurons exhibiting frequency-adaptation. Neural Computation 17(9):2060-2076.
For gradient descent learning to yield connectivity consistent with real biological networks, the simulated neurons would have to include more realistic intrinsic properties such as frequency adaptation. However, gradient descent learning cannot be used straightforwardly with adapting rate-model neurons because the derivative of the activation function depends on the activation history. The objectives of this study were to (1) develop a simple computational approach to reproduce mathematical gradient descent and (2) use this computational approach to provide supervised learning in a network formed of rate-model neurons that exhibit frequency adaptation.The results of mathematical gradient descent were used as a reference in evaluating the performance of the computational approach. For this comparison, standard (nonadapting) rate-model neurons were used for both approaches. The only difference was the gradient calculation: the mathematical approach used the derivative at a point in weight space, while the computational approach used the slope for a step change in weight space. Theoretically, the results of the computational approach should match those of the mathematical approach, as the step size is reduced but floating-point accuracy formed a lower limit to usable step sizes. A systematic search for an optimal step size yielded a computational approach that faithfully reproduced the results of mathematical gradient descent.The computational approach was then used for supervised learning of both connection weights and intrinsic properties of rate-model neurons to convert a tonic input into a phasic-tonic output pattern. Learning produced biologically realistic connectivity that essentially used a monosynaptic connection from the tonic input neuron to an output neuron with strong frequency adaptation as compared to a complex network when using nonadapting neurons. Thus, more biologically realistic connectivity was achieved by implementing rate-model neurons with more realistic intrinsic properties. Our computational approach could be applied to learning of other neuron properties. pdf

Guigon E (2004) Interpolation and extrapolation in human behavior and neural networks. Journal of Cognitive Neuroscience 16(3):382-389.
Unlike most artificial systems, the brain is able to face situations that it has not learned or even encountered before. This ability is not in general echoed by the properties of most neural networks. Here, we show that neural computation based on least-square error learning between populations of intensity-coded neurons can explain interpolation and extrapolation capacities of the nervous system in sensorimotor and cognitive tasks. We present simulations for function learning experiments, auditory-visual behavior, and visuomotor transformations. The results suggest that induction in human behavior, be it sensorimotor or cognitive, could arise from a common neural associative mechanism. pdf djvu

Brette R, Guigon E (2003) Reliability of spike timing is a general property of spiking model neurons. Neural Computation 15(2):279-308.
The responses of neurons to time-varying injected currents are reproducible on a trial-by-trial basis in vitro, but when a constant current is injected, small variances in interspike intervals across trials add up, eventually leading to a high variance in spike timing. It is unclear whether this difference is due to the nature of the input currents or the intrinsic properties of the neurons. Neuron responses can fail to be reproducible in two ways: dynamical noise can accumulate over time and lead to a desynchronization over trials, or several stable responses can exist, depending on the initial condition. Here we show, through simulations and theoretical considerations, that for a general class of spiking neuron models, which includes, in particular, the leaky integrate-and-fire model as well as nonlinear spiking models, aperiodic currents, contrary to periodic currents, induce reproducible responses, which are stable under noise, change in initial conditions and deterministic perturbations of the input. We provide a theoretical explanation for aperiodic currents that cross the threshold. pdf djvu

Guigon E (2003) Computing with populations of monotonically tuned neurons. Neural Computation 15(9):2115-2127.
The parametric variation in neuronal discharge according to the values of sensory or motor variables strongly influences the collective behavior of neuronal populations. A multitude of studies on the populations of broadly tuned neurons (e.g., cosine tuning) have led to such well-known computational principles as population coding, noise suppression, and line attractors. Much less is known about the properties of populations of monotonically tuned neurons. In this letter, we show that there exists an efficient weakly biased linear estimator for monotonic populations and that neural processing based on linear collective computation and least-square error learning in populations of intensity-coded neurons has specific generalization capacities. pdf djvu

Baraduc P, Guigon E (2002) Population computation of vectorial transformations. Neural Computation 14(4):845-871.
Many neurons of the central nervous system are broadly tuned to some sensory or motor variables. This property allows one to assign to each neuron a preferred attribute (PA). The width of tuning curves and the distribution of PAs in a population of neurons tuned to a given variable define the collective behavior of the population. In this article, we study the relationship of the nature of the tuning curves, the distribution of PAs, and computational properties of linear neuronal populations. We show that noise-resistant distributed linear algebraic processing and learning can be implemented by a population of cosine tuned neurons assuming a nonuniform but regular distribution of PAs. We extend these results analytically to the noncosine tuning and uniform distribution case and show with a numerical simulation that the results remain valid for a nonuniform regular distribution of PAs for broad noncosine tuning curves. These observations provide a theoretical basis for modeling general nonlinear sensorimotor transformations as sets of local linearized representations. pdf djvu

Dreher J-C, Guigon E, Burnod Y (2002) A model of prefrontal cortex dopaminergic modulation during the delayed alternation task. Journal of Cognitive Neuroscience 14(6):853-865.
Working memory performance is modulated by the level of dopamine (DA) D1 receptors stimulation in the prefrontal cortex (PFC). This modulation is exerted at different time scales. Injection of D1 agonists/antagonists exerts a long-lasting influence (several minutes or hours) on PFC pyramidal neurons. In contrast, during performance of a cognitive task, the duration of the postsynaptic effect of phasic DA release is short lasting. The functional relationships of these two time scales of DA modulation remain poorly understood. Here we propose a model that combines these two time scales of DA modulation on a prefrontal neural network. The model links the cellular and behavioral levels during performance of the delayed alternation task. The network, which represents the activity of deep-layer pyramidal neurons with intrinsic neuronal properties, exhibits two stable states of activity that can be switched on and off by excitatory inputs from long-distance cortical areas arriving in superficial layers. These stable states allow PFC neurons to maintain representations during the delay period. The role of an increase of DA receptors stimulation is to restrict inputs arriving on the prefrontal network. The model explains how the level of working memory performance follows an inverted U-shape with an increased stimulation of DA D1 receptors. The model predicts that (1) D1 receptor agonists increase perseverations, (2) D1 antagonists increase distractability, and (3) the duration of the postsynaptic effect of phasic DA release in the PFC is adjusted to the delay period of the task. These results show how the precise duration of the postsynaptic effect of phasic DA release influences behavioral performance during a simple cognitive task. pdf djvu

Guigon E, Baraduc P (2002) A neural model of perceptual-motor alignment. Journal of Cognitive Neuroscience 14(4):538-549.
Sensorimotor systems face complex and frequent discrepancies among spatial modalities, for example, growth, optical distortion, and telemanipulation. Adaptive mechanisms must act continuously to restore perceptual-motor alignments necessary for perception of a coherent world. Experimental manipulations that exposed participants to localized discrepancies showed that adaptation is revealed by the acquisition of a constrained relation between entire modalities rather than associations between individual exemplars within these modalities. The computational problem faced by the human nervous system can thus be conceived as having to induce constrained relations between continuous stimulus and response dimensions from ambiguous or incomplete training sets, that is, performing interpolation and extrapolation. How biological neuronal networks solve this problem is unknown. Here we show that neural processing based on linear collective computation and least-square (LS) error learning in populations of frequency-coded neurons (i.e., whose discharge varies in a monotonic fashion with a parameter) has built-in interpolation and extrapolation capacities. This model can account for the properties of perceptual-motor adaptations in sensorimotor systems. pdf djvu

Baraduc P, Guigon E, Burnod Y (2001) Recoding arm position to learn visuomotor transformations. Cerebral Cortex 11(10):906-917.
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. pdf djvu

Delord B, Baraduc P, Costalat R, Burnod Y, Guigon E (2000) A model study of cellular short-term memory produced by slowly inactivating potassium conductances. Journal of Computational Neuroscience 8(3):251-273.
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. pdf djvu

Burnod Y, Baraduc P, Battaglia-Mayer A, Guigon E, Koechlin E, Ferraina S, Lacquaniti F, Caminiti R (1999) Parieto-frontal coding of reaching: an integrated framework. Experimental Brain Research 129(3):325-346.
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. pdf djvu

Delord B, Klaassen AJ, Burnod Y, Costalat R, Guigon E (1997) Bistable behaviour in a neocortical neurone model. NeuroReport 8(4):1019-1023.
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. pdf djvu

Lacquaniti F, Perani D, Guigon E, Bettinardi V, Carrozzo M, Grassi F, Rossetti Y, Fazio F (1997) Visuomotor transformations for reaching to memorized targets: A PET study. NeuroImage 5(2):129-146.
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. pdf djvu

Anton J-L, Benali H, Guigon E, DiPaola M, Bittoun J, Jolivet O, Burnod Y (1996) Functional MR imaging of the human sensorimotor cortex during haptic discrimination. NeuroReport 7(18):2849-2852.
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. pdf djvu

Guigon E, Burnod Y (1995) Modelling the acquisition of goal-directed behaviors by population of neurons. International Journal of Psychophysiology 19(2):103-113.
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. pdf djvu

Guigon E, Dorizzi B, Burnod Y, Schultz W (1995) Neural correlates of learning in the prefrontal cortex of the monkey: A predictive model. Cerebral Cortex 5(2):135-147.
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. pdf djvu

Lacquaniti F, Guigon E, Bianchi L, Ferraina S, Caminiti R (1995) Representing spatial information for limb movement: Role of area 5 in the monkey. Cerebral Cortex 5(5):391-409.
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. pdf djvu

Guigon E, Grandguillaume P, Otto I, Boutkhil L, Burnod Y (1994) Neural network models of cortical functions based on the computational properties of the cerebral cortex. Journal of Physiology (Paris) 88(5):291-308.
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. pdf djvu

Guigon E (1993) Modélisation des propriétés du cortex cérébral - Comparaison entre aires visuelles, motrices et préfrontales. Unpublished doctoral dissertation, Ecole Centrale Paris, Chatenay-Malabry.
pdf

Otto I, Guigon E, Boutkhil L, Grandguillaume P, Burnod Y (1992) Direct and indirect cooperation between temporal and parietal networks for invariant visual recognition. Journal of Cognitive Neuroscience 4(1):35-57.
A new type of biologically inspired multilayered network is proposed to model the properties of the primate visual system with respect to invariant visual recognition (IVR). This model is based on 10 major neurobiological and psychological constraints. The first five constraints shape the architecture and properties of the network. 1. The network model has a Y-like double- branched multilayered architecture, with one input (the retina) and two parallel outputs, the 'What' and the 'Where,' which model, respectively, the temporal pathway, specialized for 'object' identification, and the parietal pathway specialized for 'spatial' localization. 2. Four processing layers are sufficient to model the main functional steps of primate visual system that transform the retinal information into prototypes (object-centered reference frame) in the 'What' branch and into an oculomotor command in the 'Where' branch. 3. The distribution of receptive field sizes within and between the two functional pathways provides an appropriate tradeoff between discrimination and invariant recognition capabilities. 4. The two outputs are represented by a population coding: the ocular command is computed as a population vector in the 'Where' branch and the prototypes are coded in a 'semidistributed' way in the 'What' branch. In the intermediate associative steps, processing units learn to associate prototypes (through feedback connections) to component features (through feedforward ones). 5. The basic processing units of the network do not model single cells but model the local neuronal circuits that combine different information flows organized in separate cortical layers. Such a biologically constrained model shows shift- invariant and size-invariant capabilities that resemble those of humans (psychological constraints): 6. During the Learning session, a set of patterns (26 capital letters and 2 geometric figures) are presented to the network: a single presentation of each pattern in one position (at the center) and with one size is sufficient to learn the corresponding prototypes (internal representations). These patterns are thus presented in widely varying new sizes and positions during the Recognition session: 7. The 'What' branch of the network succeeds in immediate recognition for patterns presented in the central zone of the retina with the learned size. 8. The recognition by the 'What' branch is resistant to changes in size within a limited range of variation related to the distribution of receptive field (RF) sizes in the successive processing steps of this pathway. 9. Even when ocular movements are not allowed, the recognition capabilities of the 'What' branch are unaffected by changing positions around the learned one. This significant shift-invariance of the 'What' branch is also related to the distribution of RF sizes. 10. When varying both sizes and locations, the 'What' and the 'Where' branches cooperate for recognition: the location coding in the 'Where' branch can command, under the control of the 'What' branch, an ocular movement efficient to reset peripheral patterns toward the central zone of the retina until successful recognition. This model results in predictions about anatomical connections and physiological interactions between temporal and parietal cortices. pdf djvu