Spiking model to learn arbitrary multiple transformations for a self-realizing network
AssigneeHRL Laboratories, LLC
InventorsNarayan Srinivasa, Youngkwan Cho
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Abstract
A neural network, wherein a portion of the neural network comprises: a first array having a first number of neurons, wherein the dendrite of each neuron of the first array is provided for receiving an input signal indicating that a measured parameter gets closer to a predetermined value assigned to said neuron; and a second array having a second number of neurons, wherein the second number is smaller than the first number, the dendrite of each neuron of the second array forming an excitatory STDP synapse with the axon of a plurality of neurons of the first array; the dendrite of each neuron of the second array forming an excitatory STDP synapse with the axon of neighboring neurons of the second array.
Claims27
A neural network circuitry, wherein a portion of the neural network circuitry comprises:
- a first array circuitry having a first number of neurons, wherein the dendrite of each neuron of the first array circuitry is provided for receiving an input signal indicating that a measured parameter gets closer to a predetermined value assigned to said neuron;
- a second array circuitry having a second number of neurons, the dendrite of each neuron of the second array circuitry forming an excitatory Spike-Timing-Dependent-Plasticity (STDP) synapse with the axon of a plurality of neurons of the first array circuitry;
- and the dendrite of each neuron of the second array circuitry forming an excitatory Spike-Timing-Dependent-Plasticity (STDP) synapse with the axon of neighboring neurons of the second array circuitry.
A method of programming a neural network, comprising:
- providing a first neural network portion comprising a first array having a first number of neurons and a second array having a second number of neurons, the dendrite of each neuron of the second array forming an excitatory Spike-Timing-Dependent-Plasticity (STDP) synapse with the axon of a plurality of neurons of the first array;
- the dendrite of each neuron of the second array forming an excitatory Spike-Timing-Dependent-Plasticity (STDP) synapse with the axon of neighboring neurons of the second array;
- and providing to the dendrite of each neuron of the first array an input signal indicating that a measured parameter gets closer to a predetermined value assigned to said neuron.
Dependencies are inferred from the claim text.