Neuronal network topology for computing conditional probabilities

Described is a system for estimating conditional probabilities for operation of a mobile device. Input data streams from first and second mobile device sensors are input into a neuronal network, where the first and second input data streams are converted into variable spiking rates of first and second neurons. The system learns a conditional probability between the first and second input data streams. A synaptic weight of interest between the first and second neurons converges to a fixed-point value, where the fixed-point value corresponds to the conditional probability. Based on the conditional probability and a new input data stream, a probability of an event is estimated. Based on the probability of the event, the system causes the mobile device to perform a mobile device operation.