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公开(公告)号：US09665822B2
公开(公告)日：2017-05-30
申请号：US12828091
申请日：2010-06-30
摘要： Embodiments of the invention relate to canonical spiking neurons for spatiotemporal associative memory. An aspect of the invention provides a spatiotemporal associative memory including a plurality of electronic neurons having a layered neural net relationship with directional synaptic connectivity. The plurality of electronic neurons configured to detect the presence of a spatiotemporal pattern in a real-time data stream, and extract the spatiotemporal pattern. The plurality of electronic neurons are further configured to, based on learning rules, store the spatiotemporal pattern in the plurality of electronic neurons, and upon being presented with a version of the spatiotemporal pattern, retrieve the stored spatiotemporal pattern.
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公开(公告)号：US08655813B2
公开(公告)日：2014-02-18
申请号：US12982546
申请日：2010-12-30
CPC分类号： G06N3/049
摘要： Neuronal networks of electronic neurons interconnected via electronic synapses with synaptic weight normalization. The synaptic weights are based on learning rules for the neuronal network, such that a synaptic weight for a synapse determines the effect of a spiking source neuron on a target neuron connected via the synapse. Each synaptic weight is maintained within a predetermined range by performing synaptic weight normalization for neural network stability.
摘要翻译： 通过电子突触与突触体重标准化相互联系的神经元网络。 突触权重基于神经元网络的学习规则，使得突触的突触权重决定了刺激源神经元对通过突触连接的目标神经元的影响。 通过对神经网络稳定性执行突触权重归一化，将每个突触重量保持在预定范围内。
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3.发明申请公开(公告)号：US20120330872A1
公开(公告)日：2012-12-27
申请号：US13598915
申请日：2012-08-30
IPC分类号： G06N3/08
摘要： Embodiments of the invention relate to canonical spiking neurons for spatiotemporal associative memory. An aspect of the invention provides a spatiotemporal associative memory including a plurality of electronic neurons having a layered neural net relationship with directional synaptic connectivity. The plurality of electronic neurons configured to detect the presence of a spatiotemporal pattern in a real-time data stream, and extract the spatiotemporal pattern. The plurality of electronic neurons are further configured to, based on learning rules, store the spatiotemporal pattern in the plurality of electronic neurons, and upon being presented with a version of the spatiotemporal pattern, retrieve the stored spatiotemporal pattern.
摘要翻译： 本发明的实施例涉及用于时空关联记忆的规范加标神经元。 本发明的一个方面提供了包括具有与定向突触连通性的分层神经网络关系的多个电子神经元的时空相关存储器。 多个电子神经元被配置为检测实时数据流中的时空模式的存在，并提取时空模式。 多个电子神经元还被配置为基于学习规则将时空图案存储在多个电子神经元中，并且在呈现时空图案的版本时，检索存储的时空图案。
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公开(公告)号：US20120259804A1
公开(公告)日：2012-10-11
申请号：US13083414
申请日：2011-04-08
申请人： Bernard V. Brezzo , Leland Chang , Steven K. Esser , Daniel J. Friedman , Yong Liu , Dharmendra S. Modha , Robert K. Montoye , Bipin Rajendran , Jae-sun Seo , Jose A. Tierno
发明人： Bernard V. Brezzo , Leland Chang , Steven K. Esser , Daniel J. Friedman , Yong Liu , Dharmendra S. Modha , Robert K. Montoye , Bipin Rajendran , Jae-sun Seo , Jose A. Tierno
摘要： A reconfigurable neural network circuit is provided. The reconfigurable neural network circuit comprises an electronic synapse array including multiple synapses interconnecting a plurality of digital electronic neurons. Each neuron comprises an integrator that integrates input spikes and generates a signal when the integrated inputs exceed a threshold. The circuit further comprises a control module for reconfiguring the synapse array. The control module comprises a global final state machine that controls timing for operation of the circuit, and a priority encoder that allows spiking neurons to sequentially access the synapse array.
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公开(公告)号：US20120173471A1
公开(公告)日：2012-07-05
申请号：US12982546
申请日：2010-12-30
CPC分类号： G06N3/049
摘要： Neuronal networks of electronic neurons interconnected via electronic synapses with synaptic weight normalization. The synaptic weights are based on learning rules for the neuronal network, such that a synaptic weight for a synapse determines the effect of a spiking source neuron on a target neuron connected via the synapse. Each synaptic weight is maintained within a predetermined range by performing synaptic weight normalization for neural network stability.
摘要翻译： 通过电子突触与突触体重标准化相互联系的神经元网络。 突触权重基于神经元网络的学习规则，使得突触的突触权重决定了刺激源神经元对通过突触连接的目标神经元的影响。 通过对神经网络稳定性执行突触权重归一化，将每个突触重量保持在预定范围内。
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公开(公告)号：US20120150781A1
公开(公告)日：2012-06-14
申请号：US12963558
申请日：2010-12-08
CPC分类号： G06N3/049
摘要： An integrate and fire electronic neuron is disclosed. Upon receiving an external spike signal, a digital membrane potential of the electronic neuron is updated based on the external spike signal. The electric potential of the membrane is decayed based on a leak rate. Upon the electric potential of the membrane exceeding a threshold, a spike signal is generated.
摘要翻译： 公开了一种整合和消防电子神经元。 在接收到外部尖峰信号时，基于外部尖峰信号更新电子神经元的数字膜电位。 膜的电位根据泄漏率衰减。 当膜的电位超过阈值时，产生尖峰信号。
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公开(公告)号：US09275330B2
公开(公告)日：2016-03-01
申请号：US13596278
申请日：2012-08-28
CPC分类号： G06N3/04 , G06F9/44505 , G06N3/049 , G06N3/063 , G06N3/088
摘要： Embodiments of the invention provide a neural core circuit comprising a synaptic interconnect network including plural electronic synapses for interconnecting one or more source electronic neurons with one or more target electronic neurons. The interconnect network further includes multiple axon paths and multiple dendrite paths. Each synapse is at a cross-point junction of the interconnect network between a dendrite path and an axon path. The core circuit further comprises a routing module maintaining routing information. The routing module routes output from a source electronic neuron to one or more selected axon paths. Each synapse provides a configurable level of signal conduction from an axon path of a source electronic neuron to a dendrite path of a target electronic neuron.
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公开(公告)号：US20150286924A1
公开(公告)日：2015-10-08
申请号：US13562187
申请日：2012-07-30
摘要： Embodiments of the invention relate to a scalable neural hardware for the noisy-OR model of Bayesian networks. One embodiment comprises a neural core circuit including a pseudo-random number generator for generating random numbers. The neural core circuit further comprises a plurality of incoming electronic axons, a plurality of neural modules, and a plurality of electronic synapses interconnecting the axons to the neural modules. Each synapse interconnects an axon with a neural module. Each neural module receives incoming spikes from interconnected axons. Each neural module represents a noisy-OR gate. Each neural module spikes probabilistically based on at least one random number generated by the pseudo-random number generator unit.
摘要翻译： 本发明的实施例涉及用于贝叶斯网络的噪声-OR模型的可伸缩神经硬件。 一个实施例包括神经核心电路，其包括用于产生随机数的伪随机数发生器。 神经核心电路还包括多个进入的电子轴突，多个神经模块和将轴突与神经模块相互连接的多个电子突触。 每个突触将轴突与神经模块相互连接。 每个神经模块从相互联系的轴突接收进入的尖峰。 每个神经模块表示噪声或门。 每个神经模块基于由伪随机数发生器单元生成的至少一个随机数来概率地尖峰。
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9.发明授权Multi-processor cortical simulations with reciprocal connections with shared weights 有权多处理器皮质模拟与共享权重的互惠连接公开(公告)号：US08924322B2
公开(公告)日：2014-12-30
申请号：US13524798
申请日：2012-06-15
摘要： Embodiments of the invention relate to distributed simulation frameworks that provide reciprocal communication. One embodiment comprises interconnecting neuron groups on different processors via a plurality of reciprocal communication pathways, and facilitating the exchange of reciprocal spiking communication between two different processors using at least one Ineuron module. Each processor includes at least one neuron group. Each neuron group includes at least one electronic neuron.
摘要翻译： 本发明的实施例涉及提供相互通信的分布式仿真框架。 一个实施例包括经由多个相互通信路径在不同处理器上互连神经元组，并且促进使用至少一个Ineuron模块交换两个不同处理器之间的相互加速通信。 每个处理器包括至少一个神经元组。 每个神经元组包括至少一个电子神经元。
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公开(公告)号：US20120109863A1
公开(公告)日：2012-05-03
申请号：US12828091
申请日：2010-06-30
摘要： Embodiments of the invention relate to canonical spiking neurons for spatiotemporal associative memory. An aspect of the invention provides a spatiotemporal associative memory including a plurality of electronic neurons having a layered neural net relationship with directional synaptic connectivity. The plurality of electronic neurons configured to detect the presence of a spatiotemporal pattern in a real-time data stream, and extract the spatiotemporal pattern. The plurality of electronic neurons are further configured to, based on learning rules, store the spatiotemporal pattern in the plurality of electronic neurons, and upon being presented with a version of the spatiotemporal pattern, retrieve the stored spatiotemporal pattern.
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