This paper presents an analog CMOS implementation of a neural
network based on a spinal cord model. The network is comprised by three pairs of cells, Alpha motorneurons, Interneurons and Renshaw cells, which form the basic control motor system for a single limb movement. Behaviour of each neuron is described by a differential equation, which provides it with a dynamic performance. This network is useful to control limb movements based in an antagonist pair of
actuators, i.e. muscles for a human limb or electric motors or SMA fibers for machine applications. This antagonist structure has the main advantage that allows independent control of limb position and stiffness, which makes it suitable for applications where inertial load compensation is a critical factor. For the implementation of the neurons we have developed individual analog operators, like multipliers and integrators, which have been then joined to obtain the cell. The whole circuit works in current mode, and exhibits good
performance in power disipation and bandwidth. The implementation of the network has been done in a 0.35um process from AMS. The layout size is 870 × 480 μm and the power dissipation is 14 mW, using a reference voltage of 3.3 volts. The applications in which this network canbe used fall in two broad cathegories. Firstly, in the development of human-machine interfaces capable to be used both in industry and in handicaped people and secondly in the development o neural controller for industrial robots, providing them with a compliance performance.