Biologically-inspired multi-segmented robot
A biologically inspired, multi-segmented robot is provided, wherein the biologically inspired, multi-segmented robot employs a logic-based technique to control mobility, rather than a mobility control technique that relies on complex mathematical models. The robot is biologically-inspired in that it employs certain artificial, anatomical and neuro-physiological features that are similar to features found in actual biological systems, including a host of artificial joints and muscle-like actuators. These anatomical and neuro-physiological features exhibit certain built-in, mechanical constraints, which provide mechanical feedback that is similar to the type of feedback that is inherent in genuine biological systems. In addition, the robot employs one or more sensors which are capable of measuring the status (e.g., the position) of the robot. A controller then uses the sensor data to activate functional groupings of the muscle-like activators to control mobility.
Publication number: US6532400B1 | Search similar patents
Single-component artificial neuron made from mott insulators, network of artificial neurons, and method for making said artificial neurons
The invention relates to an artificial neuron (30) consisting of a single-component electric dipole comprising a single material (31) which belongs to the class of Mott insulators and is connected to two electric electrodes (32, 33).
Publication number: WO2015165809A2 | Search similar patents
The invention relates to cybernetics and can be used in the form of a cell for neuron networks used for solving the assessment problems of the operation of open complex systems, for assessing the optimality of different solutions, by building the hierarchical and recurrent model of a system to be investigated, taking into account the different initial and operational states of the elements thereof and by taking into account, during the modeling of the separate elements of a system by means of neuron elements, the level of the self-sufficiency thereof and the susceptibility to external signal action. The inventive neuron element makes it possible to take into account the setting error of the parameters thereof and of the parameters of input signals and to ensure the specified accuracy of the neuron network self-learning. The neuron networks based on the inventive neuron elements make it possible to perform different variants of the assessment of the operation of open complex systems and the optimality degree of different solutions and to develop different variants of adaptive information management systems.
Publication number: WO2008072994A1 | Search similar patents
Artificial neuron semiconductor element having three-dimensional structure and artificial neuron semiconductor system using same
An artificial neuron semiconductor element having a three-dimensional structure comprises: a first electrode to which a clock signal is applied and a second electrode in which an output signal is generated; an insulation column; a plurality of electrode layers for receiving an electrical signal from at least one synapse circuit; and a phase change layer which is divided into at least two parts by the insulation column and is in contact with at least two side surfaces of the insulation column, the phase change layer making a phase change by the plurality of electrode layers.
Publication number: WO2016060529A1 | Search similar patents
Method, system, computer program with program code means, and computer program product for analyzing neuronal activities in neuronal areas
The invention relates to an analysis of neuronal activities in neuronal areas. Signals are determined, each of said signals describing the neuronal activity in one of the neuronal areas. Said signals are based on a linear statistical correlation which is described by using coupling parameters describing the linear statistical correlation between the signals. Probabilities of whether or not signals occur are determined, a higher-order statistical distribution being used as a basis for the signals. The coupling parameters are determined and analyzed by optimizing the probabilities.
Publication number: WO2004021244A2 | Search similar patents