Artificial synapse component using multiple distinct learning means with distinct predetermined learning acquisition times
First Claim
1. An artificial neuron component, comprising:
- (a.) at least one artificial synapse, comprising;
(1.) a connection means that connects an input signal to be conveyed to said artificial synapse, and(2.) a plurality of means for storing or supplying a plurality of respective synapse weights or synapse weight values, and(3.) a synapse modulating means for modulating said input signal by each of said plurality of respective synapse weights or synapse weight values, thereby to produce a plurality of respective results determined by modulating said input signal by said plurality of respective synapse weights or synapse weight values,(b.) a summing means for accumulating said plurality of respective results of said synapse modulating means as an internal sum, and(c.) a transfer conversion means for subjecting said internal sum accumulated by said summing means to a predetermined transfer function to provide a resultant output value,(d.) a plurality of weight adjustment means, each comprising a predetermined learning means for adjusting a respective one of the synapse weight values within said artificial synapse, and for correcting the value of said respective one of the synapse weight values within said artificial synapse as a function of said predetermined learning means,whereby each of said plurality of weight adjustment means will train each of said plurality of respective synapse weights or synapse weight values using a distinct predetermined learning means, and a distinct predetermined learning rate, providing each of said plurality of respective synapse weights or synapse weight values with a distinct predetermined learning acquisition time, and the output of said transfer conversion means is a representation of said input signal, modulated by each of said plurality of respective synapse weights or synapse weight values within said artificial synapse.
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Abstract
Neuron component and method for use in artificial neural networks (ANNs) with input synapses (204, 204b . . . 204n), each synapse includes multiple weights called synapse weights (206-1, 206-2, 206-3). Each synapse further includes a facility to modulate, or gate, an input signal connected to the synapses, by each of the respective synapse weights within the synapse, supplying the result of each modulating operation. The neuron also sums the results of all modulating operations, and subjects the results to a transfer function. Each of the multiple weights associated with a given synapse, may be specified to have its own weight-adjustment facility (214, 214b, 214c), with its own error-values (216, 216b, 216c), and its own specified learning and aspect (1000) includes a separate sum (1018, 1018b) and transfer function (1020, 1020b) for each synapse weight.
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Citations
16 Claims
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1. An artificial neuron component, comprising:
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(a.) at least one artificial synapse, comprising; (1.) a connection means that connects an input signal to be conveyed to said artificial synapse, and (2.) a plurality of means for storing or supplying a plurality of respective synapse weights or synapse weight values, and (3.) a synapse modulating means for modulating said input signal by each of said plurality of respective synapse weights or synapse weight values, thereby to produce a plurality of respective results determined by modulating said input signal by said plurality of respective synapse weights or synapse weight values, (b.) a summing means for accumulating said plurality of respective results of said synapse modulating means as an internal sum, and (c.) a transfer conversion means for subjecting said internal sum accumulated by said summing means to a predetermined transfer function to provide a resultant output value, (d.) a plurality of weight adjustment means, each comprising a predetermined learning means for adjusting a respective one of the synapse weight values within said artificial synapse, and for correcting the value of said respective one of the synapse weight values within said artificial synapse as a function of said predetermined learning means, whereby each of said plurality of weight adjustment means will train each of said plurality of respective synapse weights or synapse weight values using a distinct predetermined learning means, and a distinct predetermined learning rate, providing each of said plurality of respective synapse weights or synapse weight values with a distinct predetermined learning acquisition time, and the output of said transfer conversion means is a representation of said input signal, modulated by each of said plurality of respective synapse weights or synapse weight values within said artificial synapse. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8)
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9. A method of emulating a neuron for use in artificial neural networks comprising:
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(a.) providing at least one synapse means, comprising; (1.) a plurality of means for storing a plurality of respective synapse weights or weight values, and (2.) a synapse modulating means for respectively modulating an input signal by each of said plurality of respective synapse weights or weight values, to produce a plurality of respective results, (b.) accumulating the results of said synapse modulating means for each of said plurality of respective synapse weights or weight values in said synapse means into an internal sum, and (c.) subjecting said internal sum to a predetermined transfer function to provide a resultant output, (d.) providing a plurality of weight-adjustment means each comprising a predetermined learning means, for adjusting a respective one of said plurality of respective synapse weights or weight values within said at least one synapse means, and for correcting the value of said respective one of said plurality of respective synapse weights or weight values within said at least one synapse means as a function of said predetermined learning means, whereby said resultant output is a representation of at least one input signal, individually modulated by each of said plurality of respective synapse weights or weight values comprising each of said at least one synapse means, and said weight-adjustment means will train each of said synapse weights using a distinct predetermined learning means, and a distinct predetermined learning rate, providing each of said plurality of respective synapse weights or weight values with a distinct, predetermined learning acquisition time. - View Dependent Claims (10, 11, 12, 13, 14, 15, 16)
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Specification