This study focuses on biomimetic sensory motor control of a robotic arm. We have developed a command circuit that was mathematically deduced from physical and mathematical constraints describing the function of cerebellar pathways. The control circuit contains an internal predictive model of the direct biomechanical function of the limb placed in a closed loop, so that the circuit computes an approximate inverse function. The structure of the model resembles the anatomic connectivity of the cerebellar pathways. In this paper, we present an application of this model to the control of a 2-link robotic arm actuated by four single-joint McKibben muscles and report the results obtained by simulation and real-time learning of 2 degrees of freedom pointing movements.

This letter presents a novel unsupervised sensory matching learning technique for the development of an internal representation of three-dimensional information. The representation is invariant with respect to the sensory modalities involved. Acquisition of the internal representation is demonstrated with a neural network model of a sensorimotor system of a simple model creature, consisting of a tactile-sensitive body and a multiple-degrees-of-freedom arm with proprioceptive sensitivity. Acquisition of the 3D representation as well as a distributed representation of the body scheme, occurs through sensorimotor interactions (i.e., the sensory-motor experience of the creature). Convergence of the learning is demonstrated through computer simulations for the model creature with a 7-DoF arm and a spherical body covered by 20 tactile fields.

We have developed and tested a novel artificial neural network for the processing of temporal signals. The working of the units (TempUnit) is based on the mechanism of temporal summation as observed in biological neurons. Contrary to traditional neural networks, the TempUnit optimizes its basis function by supervised learning. The model was tested on cortical and associated muscular (EMG) recordings from the behaving primate. The TempUnit showed a 2.3 times better performance in mapping spiking to EMG activity than a time delay multi-layer perceptron. The TempUnit model demonstrated correct capacities for inverse computation. Indeed, we calculated biologically compatible activities for 3 cortical neurons from EMG recordings. Data compression capacity of the TempUnit was tested on audio data and compared to the MP3 compression standard. For a similar reproduction quality, we found a compression rate 5 times higher than in MP3.

In recent years, advances and improvements in engineering and robotics have been strengthening interactions between biological science and robotics in the goal of mimicking the complexity of biological systems. In this paper, motor control paradigms inspired by human mechanisms of sensory-motor coordination are applied to a biologically-inspired, purpose-designed robotic platform. The goal was to define and implement a multi-network architecture and to demonstrate that progressive learning of object grasping and manipulation can greatly increase performance of a robotic system in terms of adaptability, flexibility, growing competences and generalization, while preserving the robustness of traditional control. The paper presents the neural approach to sensory-motor coordination and shows preliminary results of the integration with the robotic system by means of simulation tests and experimental trials.

We investigated spatio-temporal information processing in the primate motor system. Corticomotoneuronal (CM) cells provide monosynaptic excitatory connections from motor cortex to spinal motoneurons and contribute causally to the time-varying electromyogram (EMG) of their target muscle. A multilayer perceptron (MLP) was used to evaluate the transfer function between neural activity of single CM cells and their target muscle EMG, using data from in-vivo recordings in primate motor cortex. For an optimal MLP performance, i.e., minimal error between recorded target EMG and MLP-derived EMG, the CM cell input period had to span the latency observed between CM cell peak activity and EMG peak activity. We argue that the same spike train may code two types of information: 1) rate coding within the input window accounted for large-amplitude variations in the EMG signal and 2) temporal coding within a window of 40 ms just prior to the EMG output signal accounted for EMG variations of small amplitude. The transfer function of the MLP, thus, combines rate and temporal coding and suggests that CM cell output may also combine these two forms of coding. We predict that mutual constraints of rate and temporal coding would, however, would limit the CM output to code for particular temporal profiles of EMG, possibly adapted to bio-mechanical constraints.