Neuronal network topology for computing conditional probabilities

A computer implemented method for estimating conditional probabilities for operation of a mobile device, the method comprising an act of:

• causing one or more processers to execute instructions encoded on a non-transitory computer-readable medium, such that upon execution, the one or more processors perform operations of:
• inputting a first input data stream obtained from a first mobile device sensor and a second input data stream obtained from a second mobile device sensor into a neuronal network comprising a plurality of neurons, wherein the first and second input data streams are converted into variable spiking rates of a first neuron and a second neuron;
• learning a conditional probability between the first input data stream and the second input data stream,
• wherein a synaptic weight of interest between the first neuron and the second neuron converges to a fixed-point value corresponding to the conditional probability;
• based on the conditional probability and a new input data stream, estimating a probability of an event;
• and
• based on the probability of the event, causing the mobile device to perform a mobile device operation.

A computer program product for estimating conditional probabilities for operation of a mobile device, the computer program product comprising:

• a non-transitory computer-readable medium having executable instructions encoded thereon, such that upon execution of the instructions by one or more processors, the one or more processors perform operations of:
• inputting a first input data stream obtained from a first mobile device sensor and a second input data stream obtained from a second mobile device sensor into a neuronal network comprising a plurality of neurons, wherein the first and second input data streams are converted into variable spiking rates of a first neuron and a second neuron;
• learning a conditional probability between the first input data stream and the second input data stream,
• wherein a synaptic weight of interest between the first neuron and the second neuron converges to a fixed-point value corresponding to the conditional probability;
• based on the conditional probability and a new input data stream, estimating a probability of an event;
• and
• based on the probability of the event, causing the mobile device to perform a mobile device operation.

A neuromorphic hardware chip for estimating conditional probabilities for a mobile device operation, the neuromorphic hardware chip performing operations of:

• inputting a first input data stream obtained from a first mobile device sensor and a second input data stream obtained from a second mobile device sensor into a neuronal network comprising a plurality of neurons, wherein the first and second input data streams are converted into variable spiking rates of a first neuron and a second neuron;
• learning a conditional probability between the first input data stream and the second input data stream,
• wherein a synaptic weight of interest between the first neuron and the second neuron converges to a fixed-point value corresponding to the conditional probability;
• based on the conditional probability and a new input data stream, estimating a probability of an event;
• and
• based on the probability of the event, causing the mobile device to perform a mobile device operation.