This paper is concerned with the evaluation of a model of human imitation of arm movements. The model consists of a hierarchy of articial neural networks, which are abstractions of brain regions involved in visuo-motor control. These are the spinal cord, the primary and pre-motor cortexes (M1 & PM), the cerebellum, and the temporal cortex. A biomechanical simulation is developed which models the muscles and the complete dynamics of a 37 degree of freedom humanoid. Input to the model are data from human arm movements recorded using video and marker-based tracking systems. The model's performance is evaluated for reproducing reaching movements and oscillatory movements of the two arms. Results show a high qualitative and quantitative agreement with human data. In particular, the model reproduces the well known features of reaching movements in humans, namely the bellshaped curves for the velocity and quasi-linear hand trajectories. Finally, the model's performance is compared to that o...

This article investigates the neural mechanisms underlying salamander locomotion, and develops a biologically plausible connectionist model of a central pattern generator capable of producing the typical aquatic and terrestrial gaits of the salamander. It investigates, in particular, what type of neural circuitry can produce and modulate the two locomotor programs identified within the salamander's spinal cord, namely, a traveling wave of neural activity for swimming and a standing wave for trotting. A two-dimensional biomechanical simulation of the salamander's body is developed whose muscle contraction is determined by the locomotion controller simulated as a leaky-integrator neural network. While the connectivity of the neural circuitry underlying locomotion in the salamander has not been decoded for the moment, this article presents the design of a neural circuit which has a general organization corresponding to that hypothesized by neurobiologists. In particular, the locomotion c...

This article presents a biologically inspired model for motor skills imitation. The model is composed of modules whose functinalities are inspired by corresponding brain regions responsible for the control of movement in primates. These modules are high-level abstractions of the spinal cord, the primary and premotor cortexes (M1 and PM), the cerebellum, and the temporal cortex. Each module is modeled at a connectionist level. Neurons in PM respond both to visual observation of movements and to corresponding motor commands produced by the cerebellum. As such, they give an abstract representation of mirror neurons. Learning of new combinations of movements is done in PM and in the cerebellum. Premotor cortexes and cerebellum are modeled by the DRAMA neural architecture which allows learning of times series and of spatio-temporal invariance in multimodal inputs. The model is implemented in a mechanical simulation of two humanoid avatars, the imitator and the imitatee. Three types of sequences learning are presented: (1) learning of repetitive patterns of arm and leg movements; (2) learning of oscillatory movements of shoulders and elbows, using video data of a human demonstration; 3) learning of precise movements of the extremities for grasp and reach

and walking

The article contributes to the quest to relate global data on brain and behavior (e.g. from PET, Positron Emission Tomography, and fMRI, functional Magnetic Resonance Imaging) to the underpinning neural networks. Models tied to human brain imaging data often focus on a few "boxes" based on brain regions associated with exceptionally high blood flow, rather than analyzing the cooperative computation of multiple brain regions. For analysis directly at the level of such data, a schema-based model may be most appropriate. To further address neurophysiological data, the Synthetic PET imaging method uses computational models of biological neural circuitry based on animal data to predict and analyze the results of human PET studies. This technique makes use of the hypothesis that rCBF (regional cerebral blood flow) is correlated with the integrated synaptic activity in a localized brain region. We also describe the possible extension of the Synthetic PET method to fMRI. The second half of the...

This paper describes the design of neural control architectures for locomotion using an evolutionary approach. Inspired by the central pattern generators found in animals, we develop neural controllers which can produce the patterns of oscillations necessary for the swimming of a simulated lamprey. This work is inspired by Ekeberg's neuronal and mechanical model of a lamprey [11], and follows experiments in which swimming controllers were evolved using a simple encoding scheme [26, 25]. Here, controllers are developed using an evolutionary algorithm based on the SGOCE encoding [31, 32] in which a genetic programming approach is used to evolve developmental programs which encode the growing of a dynamical neural network. The developmental programs determine how neurons located on a 2D substrate produce new cells through cellular division and how they form efferent or afferent interconnections. Swimming controllers are generated when the growing networks eventually create connections to ...

The problem of controlling locomotion is an area in which neuroscience and robotics can fruitfully interact. In this article, I will review research carried out on locomotor central pattern generators (CPGs), i.e. neural circuits capable of producing coordinated patterns of high-dimensional rhythmic output signals while receiving only simple, lowdimensional, input signals. The review will first cover neurobiological observations concerning locomotor CPGs and their numerical modelling, with a special focus on vertebrates. It will then cover how CPG models implemented as neural networks or systems of coupled oscillators can be used in robotics for controlling the locomotion of articulated robots. The review also presents how robots can be used as scientific tools to obtain a better understanding of the functioning of biological CPGs. Finally, various methods for designing CPGs to control specific modes of locomotion will be briefly reviewed. In this process, I will discuss different types of CPG models, the pros and cons of using CPGs with robots, and the pros and cons of using robots as scientific tools. Open research topics both in biology and in robotics will also be discussed. 1

This paper presents a general approach for employing lesion analysis to address the fundamental challenge of localizing functions in a neural system. We describe the Functional Contribution Analysis (FCA) which assigns contribution values to the elements of the network such that the ability to predict the network's performance in response to multi-lesions is maximized. The approach is thoroughly examined on neurocontroller networks of evolved autonomous agents. The FCA portrays a stable set of neuronal contributions and accurate multi-lesion predictions, which are significantly better than those obtained based on the classical single lesion approach. It is also utilized for a detailed synaptic analysis of the neurocontroller connectivity network, delineating its main functional backbone. The FCA provides a...

One of the first challenges in understanding neural information processing is the identification of the functional roles of neural network elements. Aiming at this goal, lesion studies have been classically used in neuroscience, most of which have employed single lesions which are limited in their ability to reveal the significance of interacting elements. The recently developed Functional Contribution Analysis (FCA) method has addressed the functional localization challenge by analyzing data composed of multiple lesioning experiments and corresponding functional performance levels, using an operative minimization approach. This paper presents the Multi-lesion Shapley value Ana/ysis (MSA), an axiomatic, scalable and rigorous method for deducing causal function localization from multiple lesioning data, overcoming several shortcomings of the FCA. The MSA, based on fundamental concepts from game theory, accurately quantifies the contributions of network elements and their interactions. While the original game theoretical definition and calculation of the Shapley value requires a data set of a potentially vast number of all multiple lesion experiments, we developed several MSA prediction and estimation variants which use only a relatively small set of experiments. The successful working of the MSA is demonstrated in a theoretical test case, in artificially evolved neurocontrollers and for the analysis of an example of biological, reversible deactivation data. MSA has a wide range of potential applications in neuroscience for the analysis of reversible deactivation experiments and transcranial magnetic stimulation "virtual lesions", and in biology in general, for the analysis of gene networks via "multi-knockout" experiments.

. This article describes a 3D biomechanical simulation of a salamander to be used in experiments in computational neuroethology. The physically-based simulation represents the salamander as an articulated body, actuated by muscles simulated as springs and dampers, in interaction with a simple environment. The aim of the simulation is to investigate the neural circuits underlying the aquatic and terrestrial locomotion of the real salamander, as well as to serve as test bed for investigating vertebrate sensorimotor coordination in silico. 1 Computational neuroethology This article describes the design of a 3D biomechanical model for experiments in computational neuroethology [1, 2], a field which studies how behaviors of an autonomous agent (a simulated animal or a robot) arise from neural circuits. A central aspect of computational neuroethology is that it integrates the simulated central nervous system into a body and an environment, and that it investigates behavior as the re...