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11. 发明申请

US20120239602A1 SOLVING THE DISTAL REWARD PROBLEM THROUGH LINKAGE OF STDP AND DOPAMINE SIGNALING 有权
标题翻译：通过STDP和DOPAMINE信号的链接解决远程问题
公开(公告)号：US20120239602A1
公开(公告)日：2012-09-20
申请号：US13356166
申请日：2012-01-23
申请人： Eugene M. Izhikevich
发明人： Eugene M. Izhikevich
IPC分类号： G06N3/02
CPC分类号： G06N3/049 , G06N3/02 , G06N3/063 , G06N3/0635 , G06N99/005
摘要： In Pavlovian and instrumental conditioning, rewards typically come seconds after reward-triggering actions, creating an explanatory conundrum known as the distal reward problem or the credit assignment problem. How does the brain know what firing patterns of what neurons are responsible for the reward if (1) the firing patterns are no longer there when the reward arrives and (2) most neurons and synapses are active during the waiting period to the reward? A model network and computer simulation of cortical spiking neurons with spike-timing-dependent plasticity (STDP) modulated by dopamine (DA) is disclosed to answer this question. STDP is triggered by nearly-coincident firing patterns of a presynaptic neuron and a postsynaptic neuron on a millisecond time scale, with slow kinetics of subsequent synaptic plasticity being sensitive to changes in the extracellular dopamine DA concentration during the critical period of a few seconds after the nearly-coincident firing patterns.
摘要翻译：在巴甫洛夫和工具条件下，奖励通常会在奖励触发动作之后几秒钟，创造一个被称为远程奖励问题或信用分配问题的解释性难题。如果（1）当奖励到达时，射击模式不再在那里，（2）大多数神经元和突触在等待期间是活跃的，大脑如何知道什么是神经元对于奖励的触发模式？披露了由多巴胺（DA）调制的具有刺激时间依赖性可塑性（STDP）的皮层加标神经元的模型网络和计算机模拟来回答这个问题。 STDP在几毫秒的时间尺度上由突触前神经元和突触后神经元的几乎一致的发射模式触发，随后突触可塑性的缓慢动力学对于在数秒后的几秒的关键时期内对细胞外多巴胺DA浓度的变化敏感几乎一致的射击模式。

12. 发明授权

US09405975B2 Apparatus and methods for pulse-code invariant object recognition 有权
标题翻译：用于脉码编码不变对象识别的装置和方法
公开(公告)号：US09405975B2
公开(公告)日：2016-08-02
申请号：US13152084
申请日：2011-06-02
申请人： Eugene M. Izhikevich
发明人： Eugene M. Izhikevich
IPC分类号： G06K9/00 , G06K9/46 , G06K9/52 , G06N3/00 , G06N3/04
CPC分类号： G06K9/00744 , G06K9/46 , G06K9/52 , G06N3/008 , G06N3/049
摘要： Object recognition apparatus and methods useful for extracting information from sensory input. In one embodiment, the input signal is representative of an element of an image, and the extracted information is encoded in a pulsed output signal. The information is encoded in one variant as a pattern of pulse latencies relative to an occurrence of a temporal event; e.g., the appearance of a new visual frame or movement of the image. The pattern of pulses advantageously is substantially insensitive to such image parameters as size, position, and orientation, so the image identity can be readily decoded. The size, position, and rotation affect the timing of occurrence of the pattern relative to the event; hence, changing the image size or position will not change the pattern of relative pulse latencies but will shift it in time, e.g., will advance or delay its occurrence.
摘要翻译：用于从感官输入中提取信息的对象识别装置和方法。在一个实施例中，输入信号表示图像的元素，并且所提取的信息被编码在脉冲输出信号中。该信息以一种变化形式被编码为相对于时间事件的发生的脉冲延迟的模式; 例如，新的视觉框架的出现或图像的移动。脉冲图案有利地对诸如尺寸，位置和取向的图像参数基本上不敏感，因此可以容易地解码图像标识。尺寸，位置和旋转影响图案相对于事件发生的时间; 因此，改变图像尺寸或位置将不会改变相对脉冲延迟的模式，而是在时间上移动它，例如将提前或延迟其出现。

13. 发明授权

US09104973B2 Elementary network description for neuromorphic systems with plurality of doublets wherein doublet events rules are executed in parallel 有权
标题翻译：具有多个双重的神经形态系统的基本网络描述，其中并行执行双重事件规则
公开(公告)号：US09104973B2
公开(公告)日：2015-08-11
申请号：US13239123
申请日：2011-09-21
申请人： Eugene M. Izhikevich , Botond Szatmary , Csaba Petre , Jayram Moorkanikara Nageswaran , Filip Piekniewski
发明人： Eugene M. Izhikevich , Botond Szatmary , Csaba Petre , Jayram Moorkanikara Nageswaran , Filip Piekniewski
IPC分类号： G06N3/04 , G06N3/063 , G06N99/00
CPC分类号： G06N3/049 , G06N3/0454 , G06N3/063 , G06N99/007
摘要： A simple format is disclosed and referred to as Elementary Network Description (END). The format can fully describe a large-scale neuronal model and embodiments of software or hardware engines to simulate such a model efficiently. The architecture of such neuromorphic engines is optimal for high-performance parallel processing of spiking networks with spike-timing dependent plasticity. Neuronal network and methods for operating neuronal networks comprise a plurality of units, where each unit has a memory and a plurality of doublets, each doublet being connected to a pair of the plurality of units. Execution of unit update rules for the plurality of units is order-independent and execution of doublet event rules for the plurality of doublets is order-independent.
摘要翻译：公开了一种简单的格式，并被称为基本网络描述（END）。该格式可以充分描述大规模神经元模型和软件或硬件引擎的实施例，以有效地模拟这种模型。这种神经形态发动机的架构对于具有尖峰时间依赖可塑性的加标网络的高性能并行处理是最佳的。用于操作神经元网络的神经元网络和方法包括多个单元，其中每个单元具有存储器和多个双重元件，每个双引脚连接到一对多个单元。多个单元的单元更新规则的执行是与订单无关的，并且用于多个双工的双重事件规则的执行是与订单无关的。

14. 发明授权

US08712941B2 Elementary network description for efficient link between neuronal models and neuromorphic systems 失效
标题翻译：神经元模型与神经元系统之间有效联系的基本网络描述
公开(公告)号：US08712941B2
公开(公告)日：2014-04-29
申请号：US13239148
申请日：2011-09-21
申请人： Eugene M. Izhikevich , Csaba Petre , Filip Piekniewski , Botond Szatmary
发明人： Eugene M. Izhikevich , Csaba Petre , Filip Piekniewski , Botond Szatmary
IPC分类号： G06F15/18 , G06N3/10 , G06F19/12 , G06F9/50 , G06F19/00
CPC分类号： G06F9/5011 , G06N3/105 , G16H50/50
摘要： A simple format is disclosed and referred to as Elementary Network Description (END). The format can fully describe a large-scale neuronal model and embodiments of software or hardware engines to simulate such a model efficiently. The architecture of such neuromorphic engines is optimal for high-performance parallel processing of spiking networks with spike-timing dependent plasticity. The format is specifically tuned for neural systems and specialized neuromorphic hardware, thereby serving as a bridge between developers of brain models and neuromorphic hardware manufactures.
摘要翻译：公开了一种简单的格式，并被称为基本网络描述（END）。该格式可以充分描述大规模神经元模型和软件或硬件引擎的实施例，以有效地模拟这种模型。这种神经形态发动机的架构对于具有尖峰时间依赖可塑性的加标网络的高性能并行处理是最佳的。该格式专门用于神经系统和专门的神经元硬件，从而作为脑模型和神经元硬件制造商的开发人员之间的桥梁。

15. 发明申请

US20130073498A1 ELEMENTARY NETWORK DESCRIPTION FOR EFFICIENT LINK BETWEEN NEURONAL MODELS AND NEUROMORPHIC SYSTEMS 失效
标题翻译：神经网络模型与神经系统之间有效联系的基本网络描述
公开(公告)号：US20130073498A1
公开(公告)日：2013-03-21
申请号：US13239148
申请日：2011-09-21
申请人： Eugene M. Izhikevich , Csaba Petre , Filip Piekniewski , Botond Szatmary
发明人： Eugene M. Izhikevich , Csaba Petre , Filip Piekniewski , Botond Szatmary
IPC分类号： G06N3/04
CPC分类号： G06F9/5011 , G06N3/105 , G16H50/50
摘要： A simple format is disclosed and referred to as Elementary Network Description (END). The format can fully describe a large-scale neuronal model and embodiments of software or hardware engines to simulate such a model efficiently. The architecture of such neuromorphic engines is optimal for high-performance parallel processing of spiking networks with spike-timing dependent plasticity. The format is specifically tuned for neural systems and specialized neuromorphic hardware, thereby serving as a bridge between developers of brain models and neuromorphic hardware manufactures.
摘要翻译：公开了一种简单的格式，并被称为基本网络描述（END）。该格式可以充分描述大规模神经元模型和软件或硬件引擎的实施例，以有效地模拟这种模型。这种神经形态发动机的架构对于具有尖峰时间依赖可塑性的加标网络的高性能并行处理是最佳的。该格式专门用于神经系统和专门的神经元硬件，从而作为脑模型和神经元硬件制造商的开发人员之间的桥梁。

16. 发明申请

US20130073495A1 ELEMENTARY NETWORK DESCRIPTION FOR NEUROMORPHIC SYSTEMS 有权
标题翻译：神经系统的基本网络描述
公开(公告)号：US20130073495A1
公开(公告)日：2013-03-21
申请号：US13239123
申请日：2011-09-21
申请人： Eugene M. Izhikevich , Botond Szatmary , Csaba Petre , Jayram Moorkanikara Nageswaran , Filip Piekniewski
发明人： Eugene M. Izhikevich , Botond Szatmary , Csaba Petre , Jayram Moorkanikara Nageswaran , Filip Piekniewski
IPC分类号： G06N3/08 , G06N3/04
CPC分类号： G06N3/049 , G06N3/0454 , G06N3/063 , G06N99/007
摘要： A simple format is disclosed and referred to as Elementary Network Description (END). The format can fully describe a large-scale neuronal model and embodiments of software or hardware engines to simulate such a model efficiently. The architecture of such neuromorphic engines is optimal for high-performance parallel processing of spiking networks with spike-timing dependent plasticity. Neuronal network and methods for operating neuronal networks comprise a plurality of units, where each unit has a memory and a plurality of doublets, each doublet being connected to a pair of the plurality of units. Execution of unit update rules for the plurality of units is order-independent and execution of doublet event rules for the plurality of doublets is order-independent.
摘要翻译：公开了一种简单的格式，并被称为基本网络描述（END）。该格式可以充分描述大规模神经元模型和软件或硬件引擎的实施例，以有效地模拟这种模型。这种神经形态发动机的架构对于具有尖峰时间依赖可塑性的加标网络的高性能并行处理是最佳的。用于操作神经元网络的神经元网络和方法包括多个单元，其中每个单元具有存储器和多个双重元件，每个双引脚连接到一对多个单元。多个单元的单元更新规则的执行是与订单无关的，并且用于多个双工的双重事件规则的执行是与订单无关的。

17. 发明申请

US20130073491A1 APPARATUS AND METHODS FOR SYNAPTIC UPDATE IN A PULSE-CODED NETWORK 有权
标题翻译：用于脉冲编码网络中的快速更新的装置和方法
公开(公告)号：US20130073491A1
公开(公告)日：2013-03-21
申请号：US13239255
申请日：2011-09-21
申请人： Eugene M. Izhikevich , Filip Piekniewski , Jayram Moorkanikara Nageswaran
发明人： Eugene M. Izhikevich , Filip Piekniewski , Jayram Moorkanikara Nageswaran
IPC分类号： G06N3/02
CPC分类号： G06N3/049
摘要： Apparatus and methods for efficient synaptic update in a network such as a spiking neural network. In one embodiment, the post-synaptic updates, in response to generation of a post-synaptic pulse by a post-synaptic unit, are delayed until a subsequent pre-synaptic pulse is received by the unit. Pre-synaptic updates are performed first following by the post-synaptic update, thus ensuring synaptic connection status is up-to-date. The delay update mechanism is used in conjunction with system “flush” events in order to ensure accurate network operation, and prevent loss of information under a variety of pre-synaptic and post-synaptic unit firing rates. A large network partition mechanism is used in one variant with network processing apparatus in order to enable processing of network signals in a limited functionality embedded hardware environment.
摘要翻译：用于在诸如尖峰神经网络的网络中有效突触更新的装置和方法。在一个实施例中，响应于由突触后单元产生后突触脉冲的突触后更新被延迟，直到该单元接收到后续的突触前脉冲。先突触后更新是在突触后更新之后进行的，因此确保突触连接状态是最新的。延迟更新机制与系统刷新事件结合使用，以确保准确的网络运行，并防止各种突触前和突触后单位发射速率下的信息丢失。在一个变型中，网络分割机制用于网络处理设备，以便能够在有限的功能嵌入式硬件环境中处理网络信号。

18. 发明申请

US20120308136A1 APPARATUS AND METHODS FOR PULSE-CODE INVARIANT OBJECT RECOGNITION 有权
标题翻译：脉冲代码不确定对象识别的装置和方法
公开(公告)号：US20120308136A1
公开(公告)日：2012-12-06
申请号：US13152084
申请日：2011-06-02
申请人： Eugene M. Izhikevich
发明人： Eugene M. Izhikevich
IPC分类号： G06K9/00
CPC分类号： G06K9/00744 , G06K9/46 , G06K9/52 , G06N3/008 , G06N3/049
摘要： Object recognition apparatus and methods useful for extracting information from sensory input. In one embodiment, the input signal is representative of an element of an image, and the extracted information is encoded in a pulsed output signal. The information is encoded in one variant as a pattern of pulse latencies relative to an occurrence of a temporal event; e.g., the appearance of a new visual frame or movement of the image. The pattern of pulses advantageously is substantially insensitive to such image parameters as size, position, and orientation, so the image identity can be readily decoded. The size, position, and rotation affect the timing of occurrence of the pattern relative to the event; hence, changing the image size or position will not change the pattern of relative pulse latencies but will shift it in time, e.g., will advance or delay its occurrence.
摘要翻译：用于从感官输入中提取信息的对象识别装置和方法。在一个实施例中，输入信号表示图像的元素，并且所提取的信息被编码在脉冲输出信号中。该信息以一种变化形式被编码为相对于时间事件的发生的脉冲延迟的模式; 例如，新的视觉框架的出现或图像的移动。脉冲图案有利地对诸如尺寸，位置和取向的图像参数基本上不敏感，因此可以容易地解码图像标识。尺寸，位置和旋转影响图案相对于事件发生的时间; 因此，改变图像尺寸或位置将不会改变相对脉冲延迟的模式，而是在时间上移动它，例如将提前或延迟其出现。

19. 发明申请

US20080162391A1 SOLVING THE DISTAL REWARD PROBLEM THROUGH LINKAGE OF STDP AND DOPAMINE SIGNALING 有权
标题翻译：通过STDP和DOPAMINE信号的链接解决远程问题
公开(公告)号：US20080162391A1
公开(公告)日：2008-07-03
申请号：US11963403
申请日：2007-12-21
申请人： Eugene M. Izhikevich
发明人： Eugene M. Izhikevich
IPC分类号： G06N3/08
CPC分类号： G06N3/049 , G06N3/02 , G06N3/063 , G06N3/0635 , G06N99/005
摘要： In Pavlovian and instrumental conditioning, rewards typically come seconds after reward-triggering actions, creating an explanatory conundrum known as the distal reward problem or the credit assignment problem. How does the brain know what firing patterns of what neurons are responsible for the reward if (1) the firing patterns are no longer there when the reward arrives and (2) most neurons and synapses are active during the waiting period to the reward? A model network and computer simulation of cortical spiking neurons with spike-timing-dependent plasticity (STDP) modulated by dopamine (DA) is disclosed to answer this question. STDP is triggered by nearly-coincident firing patterns of a presynaptic neuron and a postsynaptic neuron on a millisecond time scale, with slow kinetics of subsequent synaptic plasticity being sensitive to changes in the extracellular dopamine DA concentration during the critical period of a few seconds after the nearly-coincident firing patterns. Random neuronal firings during the waiting period leading to the reward do not affect STDP, and hence make the neural network insensitive to this ongoing random firing activity. The importance of precise firing patterns in brain dynamics and the use of a global diffusive reinforcement signal in the form of extracellular dopamine DA can selectively influence the right synapses at the right time.
摘要翻译：在巴甫洛夫和工具条件下，奖励通常会在奖励触发动作之后几秒钟，创造一个被称为远程奖励问题或信用分配问题的解释性难题。如果（1）当奖励到达时，射击模式不再在那里，（2）大多数神经元和突触在等待期间是活跃的，大脑如何知道什么是神经元对于奖励的触发模式？披露了由多巴胺（DA）调制的具有刺激时间依赖性可塑性（STDP）的皮层加标神经元的模型网络和计算机模拟来回答这个问题。 STDP在几毫秒的时间尺度上由突触前神经元和突触后神经元的几乎一致的发射模式触发，随后突触可塑性的缓慢动力学对于在数秒后的几秒的关键时期内对细胞外多巴胺DA浓度的变化敏感几乎一致的射击模式。导致奖励的等待期间的随机神经元激发不会影响STDP，因此使神经网络对这种持续的随机射击活动不敏感。精确射击模式在脑动力学中的重要性以及以细胞外多巴胺DA的形式使用全局扩散加强信号的选择可以选择性地在正确的时间影响右侧突触。

20. 发明授权

US09117176B2 Round-trip engineering apparatus and methods for neural networks 有权
标题翻译：神经网络的往返工程设备和方法
公开(公告)号：US09117176B2
公开(公告)日：2015-08-25
申请号：US13385937
申请日：2012-03-15
申请人： Botond Szatmary , Eugene M. Izhikevich , Csaba Petre , Jayram Moorkanikara Nageswaran , Filip Piekniewski
发明人： Botond Szatmary , Eugene M. Izhikevich , Csaba Petre , Jayram Moorkanikara Nageswaran , Filip Piekniewski
IPC分类号： G06N7/00 , G06N3/08 , G06N3/10 , G06F9/44
CPC分类号： G06N3/08 , G06F8/355 , G06N3/10
摘要： Apparatus and methods for high-level neuromorphic network description (HLND) framework that may be configured to enable users to define neuromorphic network architectures using a unified and unambiguous representation that is both human-readable and machine-interpretable. The framework may be used to define nodes types, node-to-node connection types, instantiate node instances for different node types, and to generate instances of connection types between these nodes. To facilitate framework usage, the HLND format may provide the flexibility required by computational neuroscientists and, at the same time, provides a user-friendly interface for users with limited experience in modeling neurons. The HLND kernel may comprise an interface to Elementary Network Description (END) that is optimized for efficient representation of neuronal systems in hardware-independent manner and enables seamless translation of HLND model description into hardware instructions for execution by various processing modules.
摘要翻译：用于高级神经形态网络描述（HLND）框架的装置和方法，其可以被配置为使得用户能够使用统一且明确的表示来定义神经形态网络架构，其是可读和机器可解释的。该框架可用于定义节点类型，节点到节点连接类型，不同节点类型的实例化节点实例，以及生成这些节点之间连接类型的实例。为了促进框架使用，HLND格式可以提供计算神经科学家所需的灵活性，并且同时为具有有限的神经元建模经验的用户提供用户友好的界面。 HLND内核可以包括到基本网络描述（END）的接口，其被优化用于以与硬件无关的方式有效地表示神经元系统，并且能够将HLND模型描述无缝地转换成硬件指令以供各种处理模块执行。

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