Abstract: A cortical column emulation circuit includes a capacitor which is coupled at a first end to a source of reference potential by a switch, a current source coupled to the first end of the capacitor for charging the capacitor when the switch is open to develop a capacitor voltage between the first end and the second end of the capacitor; and a comparator which compares the voltage across the capacitor to a threshold potential and generates a pulse signal when the capacitor voltage is greater than the threshold voltage. The pulse signal closes the switch to connect the first end of the capacitor to the source of reference potential. A set of cortical column emulation circuits may be coupled together by an adaptive coupling to form a cortical region emulation circuit. The adaptive coupling circuit weights an input stimulus and a plurality of state vector elements by variable coupling coefficients.

Abstract: Data analysis systems are provided, especially target imaging and identification systems, which utilize a CAM that associatively stores a plurality of known data sets such as target data sets in a synaptic interconnectivity matrix modeled upon the model of learning of neural networks. In accordance with preferred embodiments the systems are able to identify unknown objects when only a partial data set from the object is available. The system is robust and fast, utilizing parallel processing due to the massive interconnectivity of neural elements so that the image produced exhibits the properties of super-resolution. Since the system is modeled after a neural network, it is fault tolerant and highly reliable.