Dynamic updating of distributed neural representations using forward models

In this paper, we present a continuous attractor network model that we hypothesize will give some suggestion of the mechanisms underlying several neural processes such as velocity tuning to visual stimulus, sensory discrimination, sensorimotor transformations, motor control, motor imagery, and imitation. All of these processes share the fundamental characteristic of having to deal with the dynamic integration of motor and sensory variables in order to achieve accurate sensory prediction and/or discrimination. Such principles have already been described in the literature by other high-level modeling studies (Decety and Sommerville in Trends Cogn Sci 7:527-533, 2003; Oztop etal. in Neural Netw 19(3):254-271, 2006; Wolpert etal. in Philos Trans R Soc 358:593-602, 2003). With respect to these studies, our work is more concerned with biologically plausible neural dynamics at a population level. Indeed, we show that a relatively simple extension of the classical neural field models can endow these networks with additional dynamic properties for updating their internal representation using external commands. Moreover, an analysis of the interactions between our model and external inputs also shows interesting properties, which we argue are relevant for a better understanding of the neural processes of the brai

Dynamic Updating of Distributed Neural Representations using Forward Models

In this paper, we present a continuous attractor network model, which we hypothesize will give some suggestion of the mechanisms underlying several neural processes, such as velocity tuning to visual stimulus, sensory discrimination, sensorimotor-transformations,motor control, motor imagery and imitation. All of these processes share the fundamental characteristic of having to deal with the dynamic integration of motor and sensory variables in order to achieve accurate sensory prediction and/or discrimination. Such principles have already been described in the literature by other high-level modeling studies. With respect to them, our work is more concerned with biologically plausible neural dynamics at a population level. Indeed, we show that a relatively simple extension of the classical neural field models can endow these networks with additional dynamic properties for updating their internal representation using external commands. Moreover, an analysis of the interactions between our model and external inputs also shows interesting properties, which we argue to be relevant for a better understanding of the neural processes of the brain

Modeling the neural correlates of imitation from a neuropsychological perspective

Imitation is a fundamental mechanism by which humans learn and understand the actions of others. This thesis addresses the low-level neural mechanisms underlying the imitation of meaningless gestures, using tools from computational neuroscience. We investigate how the human brain perceives these gestures and translates them into appropriate motor commands. In addition, we take a relatively unexplored neuropsychological perspective, which looks at imitation following a brain lesion. The analysis of how imitation breaks down in apraxia, a complex disorder of voluntary movement, enables us to reverse engineer brain function through the identification of those building blocks that are preserved. To better understand the phenomenon of apraxia, we develop a neurocomputational model of imitation that proposes potential neuroanatomical correlates, such as the flow of information across the two brain hemispheres. The model accounts for the pattern of errors observed in apraxic patients with disconnected brain hemispheres. To validate the predictions of our model, we further analyze the experimental errors and uncover a goal-dissociation, where a goal is defined as the spatial relation between two body parts. The experimental observations suggest that the imitation deficit in apraxia arises from an incorrect coordination between the reproductions of multiple goals. A prediction of this hypothesis was validated on three apraxic patients. The collected body of kinematic and neuropsychological data allowed us to refine our neurocomputational model of imitation, and to propose a biologically plausible mathematical model for the execution stage of the imitation. The model controls movement by following nonlinear dynamics, and precisely reproduces both the spatial and temporal aspects of unconstrained and natural three-dimensional reaching movements. Importantly, the model is stable and robust against external perturbations. Overall, our computational models and neuropsychological experiments contribute to a better understanding of how the brain performs the imitation of meaningless gestures; that is, by first decomposing the gesture into imitation goals, and then reproducing these goals through the association of different sensory modalities