Dynamically stable associative learning neural network system
First Claim
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1. A neural network architectural unit comprising:
- (a) a first input for receiving a set of conditioned stimuli;
(b) a second input for receiving an unconditioned stimulus;
(c) a patch element coupled to the first and second inputs and for receiving and processing the set of conditioned stimuli and the unconditioned stimulus, the patch element producing an output; and
(d) an output neuronal element for accepting the output of the patch element and for providing an output for the neural network architectural unit.
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Abstract
A dynamically stable associative learning neural system includes a plurality of neural network architectural units. A neural network architectural unit has as input both condition stimuli and unconditioned stimulus, an output neuron for accepting the input, and patch elements interposed between each input and the output neuron. The patches in the architectural unit can be modified and added. A neural network can be formed from a single unit, a layer of units, or multiple layers of units.
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Citations
51 Claims
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1. A neural network architectural unit comprising:
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(a) a first input for receiving a set of conditioned stimuli; (b) a second input for receiving an unconditioned stimulus; (c) a patch element coupled to the first and second inputs and for receiving and processing the set of conditioned stimuli and the unconditioned stimulus, the patch element producing an output; and (d) an output neuronal element for accepting the output of the patch element and for providing an output for the neural network architectural unit. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14)
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15. A method for deciding whether to create a new patch in a neural network, said method comprising:
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(a) presenting as input to the neural network a conditioned stimulus and an unconditioned stimulus with which the conditioned stimulus is to be associated; (b) for each output neuron of the neural network, comparing values of the presented conditioned stimulus within the receptive field of the output neuron, with values of conditioned stimuli stored in all preexisting patches of the output neuron; (c) selecting the preexisting patch with the stored value most similar to the value of the presented conditioned stimulus; and (d) determining whether the similarity between the stored value of the selected preexisting patch and the value of the presented conditioned stimulus requires formation of a new patch. - View Dependent Claims (16, 17, 18, 19)
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20. A neural network apparatus comprising;
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input terminals for receiving signals; signal processing means configured in a plurality of layers including; (i) a first layer of neuron circuits configured to isolate features from the signals received at the input terminals, the first layer being both an input layer and a non-output layer; (ii) a second layer of neuron circuits connected to communicate with the first layer of neuron circuits to receive feature information carrying signals from the first layer, the second layer being both a non-input layer and an output layer and being operative to establish classifications of the features in the absence of a priori knowledge of the features; (iii) output terminals receiving classification carrying signals from the second layer; wherein at least one intermediate layer of neuron circuits connected successively between said first and second layers of neuron circuits, each said intermediate layer being both a non-input and a non-output layer and having at least one neuron circuit connected to receive outputs of neuron circuits from a preceding layer and to provide inputs to neuron circuits of a subsequent layer; wherein the neuron circuits of the input layer include means for storing in a memory associations between an input pattern received by the neuron circuits and an input unconditioned stimulus value, thereby generating patches; and wherein the non-input layers include means for storing arbitrary labels of patches of neuron circuits from a preceding layer, thereby generating patches designated as meta-patches. - View Dependent Claims (21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37)
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38. A neural network comprising:
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a plurality of sequential subnetworks having at least a first subnetwork and a final subnetwork, each subnetwork having input neurons, a plurality of patches, and output neurons; wherein output signals are generated at the output neurons of each subnetwork by transmitting input signals received at the input neurons of the subnetwork through the plurality of patches of the subnetwork; wherein the input neurons of the first subnetwork receive input signals from sources external to the neural network; wherein each patch of the plurality of patches of the first subnetwork is generated in response to the input signals received by the input neurons of the first subnetwork; wherein the input neurons of each subnetwork, except for the first subnetwork, are coupled to the output neurons of the previous subnetwork and receive as input signals the output signals generated at the output neurons of the previous subnetwork; wherein the input neurons of the final subnetwork further receive unconditioned stimuli; and wherein one of the subnetworks, except the first subnetwork, receives identifying values from only a subset of the plurality of patches of the first subnetwork. - View Dependent Claims (39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51)
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Specification