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1. 发明授权

US09122994B2 Apparatus and methods for temporally proximate object recognition 有权
标题翻译：用于时间上靠近物体识别的装置和方法
公开(公告)号：US09122994B2
公开(公告)日：2015-09-01
申请号：US13152105
申请日：2011-06-02
申请人： Filip Lukasz Piekniewski , Csaba Petre , Sach Hansen Sokol , Botond Szatmary , Jayram Moorkanikara Nageswaran , Eugene M. Izhikevich
发明人： Filip Lukasz Piekniewski , Csaba Petre , Sach Hansen Sokol , Botond Szatmary , Jayram Moorkanikara Nageswaran , Eugene M. Izhikevich
IPC分类号： G06N3/04
CPC分类号： G06N3/049
摘要： Object recognition apparatus and methods useful for extracting information from an input signal. In one embodiment, the input signal is representative of an element of an image, and the extracted information is encoded into patterns of pulses. The patterns of pulses are directed via transmission channels to a plurality of detector nodes configured to generate an output pulse upon detecting an object of interest. Upon detecting a particular object, a given detector node elevates its sensitivity to that particular object when processing subsequent inputs. In one implementation, one or more of the detector nodes are also configured to prevent adjacent detector nodes from generating detection signals in response to the same object representation. The object recognition apparatus modulates properties of the transmission channels by promoting contributions from channels carrying information used in object recognition.
摘要翻译：用于从输入信号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，并且将所提取的信息编码为脉冲图案。脉冲的模式经由传输通道被引导到多个检测器节点，其被配置为在检测到感兴趣对象时产生输出脉冲。当检测到特定物体时，给定的检测器节点在处理后续输入时提高其对该特定物体的敏感度。在一个实现中，一个或多个检测器节点还被配置为防止相邻检测器节点响应于相同的对象表示而产生检测信号。对象识别装置通过促进携带在对象识别中使用的信息的信道的贡献来调制传输信道的属性。

2. 发明授权

US08719199B2 Systems and methods for providing a neural network having an elementary network description for efficient implementation of event-triggered plasticity rules 失效
标题翻译：用于提供具有用于有效实现事件触发可塑性规则的基本网络描述的神经网络的系统和方法
公开(公告)号：US08719199B2
公开(公告)日：2014-05-06
申请号：US13239163
申请日：2011-09-21
申请人： Eugene M. Izhikevich , Botond Szatmary , Csaba Petre , Filip Piekniewski , Jayram Moorkanikara Nageswaran
发明人： Eugene M. Izhikevich , Botond Szatmary , Csaba Petre , Filip Piekniewski , Jayram Moorkanikara Nageswaran
IPC分类号： G05B13/02 , G06N3/08
CPC分类号： G06N3/08 , G05B13/027 , G06N3/049 , G06N3/10
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 software and hardware engines are optimized to take into account short-term and long-term synaptic plasticity in the form of LTD, LTP, and STDP.
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。软件和硬件引擎经过优化考虑了LTD，LTP和STDP形式的短期和长期突触可塑性。

3. 发明申请

US20130251278A1 INVARIANT PULSE LATENCY CODING SYSTEMS AND METHODS 有权
标题翻译：不确定脉冲序列编码系统和方法
公开(公告)号：US20130251278A1
公开(公告)日：2013-09-26
申请号：US13895246
申请日：2013-05-15
申请人： Eugene M. Izhikevich , Botond Szatmary , Csaba Petre
发明人： Eugene M. Izhikevich , Botond Szatmary , Csaba Petre
IPC分类号： G06T9/00
CPC分类号： H04N19/107 , G06K9/46 , G06N3/049 , G06T7/20 , G06T7/40 , G06T9/002 , G06T2207/10016 , H04B14/026 , H04N19/00
摘要： Systems and methods for processing image signals are described. One method comprises obtaining a generator signal based on an image signal and determining relative latencies associated with two or more pulses in a pulsed signal using a function of the generator signal that can comprise a logarithmic function. The function of the generator signal can be the absolute value of its argument. Information can be encoded in the pattern of relative latencies. Latencies can be determined using a scaling parameter that is calculated from a history of the image signal. The pulsed signal is typically received from a plurality of channels and the scaling parameter corresponds to at least one of the channels. The scaling parameter may be adaptively calculated such that the latency of the next pulse falls within one or more of a desired interval and an optimal interval.
摘要翻译：描述用于处理图像信号的系统和方法。一种方法包括基于图像信号获得发生器信号，并且使用可以包括对数函数的发生器信号的函数来确定与脉冲信号中的两个或更多个脉冲相关联的相对延迟。发电机信号的功能可以是其参数的绝对值。信息可以以相对延迟的模式进行编码。可以使用从图像信号的历史计算的缩放参数来确定延迟。通常从多个通道接收脉冲信号，并且缩放参数对应于至少一个通道。可以自适应地计算缩放参数，使得下一个脉冲的等待时间落入期望的间隔和最佳间隔的一个或多个之内。

4. 发明申请

US20130073484A1 ELEMENTARY NETWORK DESCRIPTION FOR EFFICIENT MEMORY MANAGEMENT IN NEUROMORPHIC SYSTEMS 失效
标题翻译：神经网络系统中有效的内存管理的基本网络描述
公开(公告)号：US20130073484A1
公开(公告)日：2013-03-21
申请号：US13239155
申请日：2011-09-21
申请人： Eugene M. Izhikevich , Botond Szatmary , Csaba Petre , Filip Piekniewski
发明人： Eugene M. Izhikevich , Botond Szatmary , Csaba Petre , Filip Piekniewski
IPC分类号： G06N3/04
CPC分类号： G06N3/10 , G06N3/04 , G06N3/049
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. Methods for managing memory in a processing system are described whereby memory can be allocated among a plurality of elements and rules configured for each element such that the parallel execution of the spiking networks is most optimal.
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。描述了用于管理处理系统中的存储器的方法，其中可以在为每个元件配置的多个元件和规则之间分配存储器，使得加标网络的并行执行是最佳的。

5. 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。

6. 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。

7. 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。

8. 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。

9. 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。

10. 发明授权

US08725658B2 Elementary network description for efficient memory management in neuromorphic systems 失效
标题翻译：神经元系统中有效记忆管理的基本网络描述
公开(公告)号：US08725658B2
公开(公告)日：2014-05-13
申请号：US13239155
申请日：2011-09-21
申请人： Eugene M. Izhikevich , Botond Szatmary , Csaba Petre , Filip Piekniewski
发明人： Eugene M. Izhikevich , Botond Szatmary , Csaba Petre , Filip Piekniewski
IPC分类号： G06F19/12 , G06N3/10 , G06N3/04
CPC分类号： G06N3/10 , G06N3/04 , G06N3/049
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. Methods for managing memory in a processing system are described whereby memory can be allocated among a plurality of elements and rules configured for each element such that the parallel execution of the spiking networks is most optimal.
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。描述了用于管理处理系统中的存储器的方法，其中可以在为每个元件配置的多个元件和规则之间分配存储器，使得加标网络的并行执行是最佳的。

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