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US 11,308,386 B2Granted
Signal processing method and apparatus based on spiking neural network
AssigneeHuawei Technologies Co., Ltd.
InventorsMing Wang, Zheng Yan, Xijun Xue
Life of this patent
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Abstract
A signal processing method and apparatus includes determining a first signal F1(t) output by a first neuron, processing the first signal F1(t) using q orders of synapse weight parameters wq(t), wq−1(t), . . . , w1(t) to obtain a second signal F2(t), and inputting the second signal F2(t) to a second neuron, where the second neuron is in a layer immediately subsequent to that of the first neuron.
Claims20
A signal processing method implemented in a spiking neural network and comprising:
- receiving, by a memristor, a first signal F1(t) output by a first neuron;
- processing, by the memristor, the first signal F1(t) using q orders of synapse weight parameters wq(t), wq−1(t), . . . w1(t) to obtain a second signal F2(t), wherein a speed at which an initial function wx+10(t) met by an (x+1)th-order synapse weight parameter of the q orders of synapse weight parameters attenuates with time t is higher than a speed at which an initial function wx0(t) met by an xth-order synapse weight parameter attenuates with the time t, wherein q is a positive integer greater than 1, and wherein 1≤x≤q−1;
- and
- outputting, by the memristor, the second signal F2(t) to a second neuron, wherein the second neuron is a next-layer neuron of the first neuron,
- wherein the first signal F1(t) comprises a first sub-signal F1(t1) output by the first neuron at a moment t1,
- wherein a qth-order synapse weight parameter wq(t1) at the moment t1 meets the following condition:
- wq(t1)=wq(t1−1)+ΔFq(t1),
- wherein wq(t1−1) represents a qth-order synapse weight parameter at a moment t1−1, the moment t1−1 is a previous moment of the moment t1, and ΔFq(t1) is an update quantity based on a learning rule and a stimulation effect at the moment t1 of the first sub-signal F1(t1) on the qth-order synapse weight parameter wq(t1−1) at the moment t1−1,
- wherein an xth-order synapse weight parameter wq(t1) at the moment t1 meets the following condition:
- wq(t1)=wq(t1−1)+ΔFx(t1)+Δwx,x+1(t1)+Δwx,x+2(t1)+ . . . +Δwx,q(t1),
- wherein wx(t1−1) represents an xth-order synapse weight parameter at the moment t1−1 and ΔFx(t1) is an update quantity based on the learning rule and the stimulation effect at the moment t1 of the first sub-signal F1(t1) on the xth-order synapse weight parameter wx(t1−1) at the moment t1−1, and
- wherein Δwx,x+1(t1), Δwx,x+2(t1), . . . , Δwx,q(t1) are respectively quantities of impact of an (x+1)th-order synapse weight parameter, an (x+2)th-order synapse weight parameter, . . . , and the qth-order synapse weight parameter that are at the moment t1 on the xth-order synapse weight parameter wx(t1).
9 more elements
A spiking neural network comprising:
- a first neuron configured to output a first signal F1(t);
- a memristor coupled to the first neuron and configured to:
- receive the first signal F1(t) output by the first neuron;
- process the first signal F1(t) using q orders of synapse weight parameters wq(t), wq−1(t), . . . w1(t) to obtain a second signal F2(t), wherein a speed at which an initial function wx+10(t) met by an (x+1)th-order synapse weight parameter of the q orders of synapse weight parameters attenuates with time t is higher than a speed at which an initial function wx0(t) met by an xth-order synapse weight parameter attenuates with the time t, wherein q is a positive integer greater than 1, and wherein 1≤x≤q−1;
- and
- output the second signal F2(t);
- and
- a second neuron coupled to the memristor and configured to receive the second signal F2(t), wherein the second neuron is a neuron in a layer immediately subsequent to a layer of the first neuron,
- wherein the first signal F1(t) comprises a first sub-signal F1(t1) output by the first neuron at a moment t1,
- wherein a qth-order synapse weight parameter at the moment meets the following condition:
- wq(t1)=wq(t1−1)+ΔFq(t1),
- wherein wq(t1−1) represents a qth-order synapse weight parameter at a moment t1−1, the moment t1−1 is a previous moment of the moment t1, and ΔFq(t1) is an update quantity based on a learning rule and a stimulation effect at the moment t1 of the first sub-signal F1(t1) on the qth-order synapse weight parameter wq(t1−1) at the moment t1−1,
- wherein an xth-order synapse weight parameter wx(t1) at the moment t1 meets the following condition:
- wx(t1)=wx(t1−1)+ΔFx(t1)+Δwx,x+1(t1)+Δwx,x+2(t1)+ . . . +Δwx,q(t1),
- wherein wx(t1−1) represents an xth-order synapse weight parameter at the moment t1−1 and ΔFx(t1) is an update quantity based on the learning rule and the stimulation effect at the moment t1 of the first sub-signal F1(t1) on the xth-order synapse weight parameter wx(t1−1) at the moment t1−1, and
- wherein Δwx,x+1(t1), Δwx,x+2(t1), . . . , Δwx,q(t1) are respectively quantities of impact of an (x+1)th-order synapse weight parameter, an (x+2)th-order synapse weight parameter, . . . , and the qth-order synapse weight parameter that are at the moment t1 on the xth-order synapse weight parameter wx(t1).
13 more elements
A non-transitory computer-readable medium comprising instructions that, when executed by at least one processor, cause a spiking neural network to:
- receive a first signal F1(t) output by a first neuron;
- process the first signal F1(t) using q orders of synapse weight parameters wq(t), wq−1(t), . . . w1(t) to obtain a second signal F2(t), wherein a speed at which an initial function wx+10(t) met by an (x+1)th-order synapse weight parameter of the q orders of synapse weight parameters attenuates with time t is higher than a speed at which an initial function wx0(t) met by an xth-order synapse weight parameter attenuates with the time t, wherein q is a positive integer greater than 1, and wherein 1≤x≤q−1;
- and
- output the second signal F2(t) to a second neuron, wherein the second neuron is a neuron in a layer immediately subsequent to a layer of the first neuron,
- wherein the first signal F1(t) comprises a first sub-signal F1(t1) output by the first neuron at a moment t1,
- wherein a qth-order synapse weight parameter wq(t1) at the moment t1 meets the following condition:
- wq(t1)=wq(t1−1)+ΔFq(t1),
- wherein wq(t1−1) represents a qth-order synapse weight parameter at a moment t1−1, the moment t1−1 is a previous moment of the moment t1, and ΔFq(t1) is an update quantity based on a learning rule and a stimulation effect at the moment t1 of the first sub-signal F1(t1) on the qth-order synapse weight parameter wq(t1−1) at the moment t1−1,
- wherein an xth-order synapse weight parameter wx(t1) at the moment t1 meets the following condition:
- wx(t1)=wx(t1−1)+ΔFx(t1)+Δwx,x+1(t1)+Δwx,x+2(t1)+ . . . +Δwx,q(t1),
- wherein wx(t1−1) represents an xth-order synapse weight parameter at the moment t1−1 and ΔFF(t1) is an update quantity based on the learning rule and the stimulation effect at the moment t1 of the first sub-signal F1(t1) on the xth-order synapse weight parameter wx(t1−1) at the moment t1−1, and
- wherein Δwx,x+1(t1), Δwx,x+2(t1), . . . , Δwx,q(t1) are respectively quantities of impact of an (x+1)th-order synapse weight parameter, an (x+2)th-order synapse weight parameter, . . . , and the qth-order synapse weight parameter that are at the moment t1 on the xth-order synapse weight parameter wx(t1).
9 more elements
Dependencies are inferred from the claim text.