Spiking neural network for probabilistic computation
AssigneeHRL Laboratories, LLC
InventorsHao-Yuan Chang, Aruna Jammalamadaka, Nigel D. Stepp
Life of this patent
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Abstract
Described is a system for computing conditional probabilities of random variables for Bayesian inference. The system implements a spiking neural network of neurons to compute the conditional probability of two random variables X and Y. The spiking neural network includes an increment path for a synaptic weight that is proportional to a product of the synaptic weight and a probability of X, a decrement path for the synaptic weight that is proportional to a probability of X, Y, and delay and spike timing dependent plasticity (STDP) parameters such that the synaptic weight increases and decreases with the same magnitude for a single firing event.
Claims12
A system for computing conditional probabilities of random variables for Bayesian inference, the system comprising:
- neuromorphic hardware configured to implement a spiking neural network, the spiking neural network comprising a plurality of neurons to compute the conditional probability of two random variables X and Y according to the following:
- w*P(X)=P(X,Y)
- where P denotes probability, and w denotes a synaptic weight between an A neuron and a connected B neuron;
- wherein an X neuron and a Y neuron are configured to spike along with the random variables X and Y;
- wherein the spiking neural network comprises an increment path for w that is proportional to a product of w*P(X), a decrement path for w that is proportional to P(X,Y), and delay and spike timing dependent plasticity (STDP) parameters such that w increases and decreases with the same magnitude for a single firing event.
A neuromorphic hardware implemented method for computing conditional probabilities of random variables for Bayesian inference, the method comprising an act of:
- operating a spiking neural network comprising a plurality of neurons to compute the conditional probability of two random variables X and Y according to the following:
- w*P(X)=P(X,Y)
- where P denotes probability, and w denotes a synaptic weight between an A neuron and a connected B neuron;
- wherein an X neuron and a Y neuron are configured to spike along with the random variables X and Y;
- wherein the spiking neural network comprises an increment path for w that is proportional to a product of w*P(X), a decrement path for w that is proportional to P(X,Y), and delay and spike timing dependent plasticity (STDP) parameters such that w increases and decreases with the same magnitude for a single firing event.
Dependencies are inferred from the claim text.