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    SYSTEMS, APPARATUSES, AND METHODS FOR A DISTRIBUTED ROBOTIC NETWORK OF DATA COLLECTION AND INSIGHT GENERATION
    1.
    发明申请
    SYSTEMS, APPARATUSES, AND METHODS FOR A DISTRIBUTED ROBOTIC NETWORK OF DATA COLLECTION AND INSIGHT GENERATION 审中-公开

    公开(公告)号：WO2020206068A1

    公开(公告)日：2020-10-08

    申请号：PCT/US2020/026314

    申请日：2020-04-02

    Applicant: BRAIN CORPORATION

    Inventor： SZATMARY, Botond ,  ROSS, David

    IPC: H04L29/06

    Abstract: Systems, apparatuses, and methods for a distributed network of data collection and insight generation by server are disclosed herein. According to at least one non-limiting exemplary embodiment, the server may be configured to receive data from a network of data sources, receive an application from an application creator, and execute the application based on the data from the network of data sources to generate at least one insight, wherein the network of data sources may comprise mobile robots, stationary devices, IoT (Internet of Things) devices, and/or public data sources. The at least one insight may be utilized by robots to improve efficiency of operation or by humans to gain useful insights to the environment in which the data sources operate.

    SYSTEMS AND METHODS FOR DETECTION OF FEATURES WITHIN DATA COLLECTED BY A PLURALITY OF ROBOTS BY A CENTRALIZED SERVER
    4.
    发明申请
    SYSTEMS AND METHODS FOR DETECTION OF FEATURES WITHIN DATA COLLECTED BY A PLURALITY OF ROBOTS BY A CENTRALIZED SERVER 审中-公开

    公开(公告)号：WO2021003338A1

    公开(公告)日：2021-01-07

    申请号：PCT/US2020/040609

    申请日：2020-07-02

    Applicant: BRAIN CORPORATION

    Inventor： ROSS, David ,  SZATMARY, Botond

    IPC: G05B19/04 ,  G06N3/08 ,  G08G1/01

    Abstract: Systems and methods for detection of features within data collected by a plurality of robots by a centralized server are disclosed herein. According to at least one non-limiting exemplary embodiment, a plurality of robots may be utilized to collect a substantial amount of feature data using one or more sensors coupled thereto, wherein use of the plurality of robots to collect the feature data yields accurate localization of the feature data and consistent acquisition of the feature data. Systems and methods disclosed herein further enable a cloud server to identify a substantial number of features within the acquired feature data for purposes of generating insights. The substantial number of features far exceed a practical number of features of which a single neural network may be trained to identify.

    SYSTEMS AND METHODS FOR LOCALIZING DEVICES USING NETWORK SIGNATURES AND COVERAGE MAPS MEASURED BY ROBOTS
    5.
    发明申请
    SYSTEMS AND METHODS FOR LOCALIZING DEVICES USING NETWORK SIGNATURES AND COVERAGE MAPS MEASURED BY ROBOTS 审中-公开

    公开(公告)号：WO2021026427A1

    公开(公告)日：2021-02-11

    申请号：PCT/US2020/045352

    申请日：2020-08-07

    Applicant: BRAIN CORPORATION

    Inventor： ROSS, David ,  SZATMARY, Botond

    IPC: B25J9/16

    Abstract: Systems and methods for localizing devices using network signatures and coverage maps measured by robots are disclosed herein. According to at least one non-limiting exemplary embodiment, a robot may generate a coverage map based on measurements collected during operation of the robot. The coverage map generated by the robot may be temporally accurate such that a device may be localized within the coverage map based on a received network signature from the device. The network signature comprising a measure of amplitudes of Wi-Fi networks and/or cellular networks at a point within an environment of the coverage map.

    SENSORY INPUT PROCESSING APPARATUS AND METHODS
    6.
    发明申请
    SENSORY INPUT PROCESSING APPARATUS AND METHODS 审中-公开
    传感器输入处理装置及方法

    公开(公告)号：WO2013106074A1

    公开(公告)日：2013-07-18

    申请号：PCT/US2012/040576

    申请日：2012-06-01

    Applicant: BRAIN CORPORATION ,  PETRE, Csaba ,  SOKOL, Sach, Hansen ,  PIEKNIEWSKI, Filip, Lukasz ,  SZATMARY, Botond ,  IZHIKEVICH, Eugene, M.

    Inventor： PETRE, Csaba ,  SOKOL, Sach, Hansen ,  PIEKNIEWSKI, Filip, Lukasz ,  SZATMARY, Botond ,  IZHIKEVICH, Eugene, M.

    IPC: H05K1/00

    CPC classification number: G06K9/6232 ,  G06K9/4628 ,  G06N3/049

    Abstract: Sensory input processing apparatus and methods useful for adaptive encoding and decoding of features. In one embodiment, the apparatus receives an input frame having a representation of the object feature, generates a sequence of sub-frames that are displaced from one another (and correspond to different areas within the frame), and encodes the sub-frame sequence into groups of pulses. The patterns of pulses are directed via transmission channels to detection apparatus configured to generate an output pulse upon detecting a predetermined pattern within received groups of pulses that is associated with the feature. Upon detecting a particular pattern, the detection apparatus provides feedback to the displacement module in order to optimize sub-frame displacement for detecting the feature of interest. In another embodiment, the detections apparatus elevates its sensitivity (and/or channel characteristics) to that particular pulse pattern when processing subsequent pulse group inputs, thereby increasing the likelihood of feature detection.

    Abstract translation: 感觉输入处理装置和方法对于特征的自适应编码和解码是有用的。 在一个实施例中，设备接收具有对象特征的表示的输入帧，产生彼此相移（并对应于帧内的不同区域）的子帧序列，并将子帧序列编码成 一组脉冲 脉冲的模式经由传输通道引导到检测装置，该检测装置被配置为在检测到与特征相关联的所接收的脉冲组内的预定模式时产生输出脉冲。 在检测到特定模式时，检测装置向位移模块提供反馈以优化用于检测感兴趣特征的子帧位移。 在另一个实施例中，当处理后续的脉冲组输入时，检测装置将其灵敏度（和/或通道特性）提升到该特定脉冲模式，从而增加特征检测的可能性。

    NEURAL NETWORK LEARNING AND COLLABORATION APPARATUS AND METHODS
    8.
    发明申请
    NEURAL NETWORK LEARNING AND COLLABORATION APPARATUS AND METHODS 审中-公开

    公开(公告)号：WO2013181637A3

    公开(公告)日：2013-12-05

    申请号：PCT/US2013/043783

    申请日：2013-05-31

    Applicant: BRAIN CORPORATION

    Inventor： BUIBAS, Marius ,  IZHIKEVICH, Eugene, M. ,  SZATMARY, Botond ,  POLONICHKO, Vadim

    IPC: G06N3/04

    Abstract: Apparatus and methods for learning and training in neural network-based devices. In one implementation, the devices each comprise multiple spiking neurons, configured to process sensory input In one approach, alternate heterosynaptic plasticity mechanisms are used to enhance learning and field diversity within the devices. The selection of alternate plasticity rules is based on recent post-synaptic activity of neighboring neurons. Apparatus and methods for simplifying training of the devices are also disclosed, including a computer- based application. A data representation of the neural network may be imaged and transferred to another computational environment, effectively copying the brain. Techniques and architectures for achieve this training, storing, and distributing these data representations are also disclosed.

    SPIKING NEURAL NETWORK FEEDBACK APPARATUS AND METHODS
    9.
    发明申请
    SPIKING NEURAL NETWORK FEEDBACK APPARATUS AND METHODS 审中-公开
    SPIKING神经网络反馈装置及方法

    公开(公告)号：WO2013169805A2

    公开(公告)日：2013-11-14

    申请号：PCT/US2013/039985

    申请日：2013-05-07

    Applicant: BRAIN CORPORATION

    Inventor： PIEKNIEWSKI, Filip ,  IZHIKEVICH, Eugene ,  SZATMARY, Botond ,  PETRE, Csaba

    IPC: G06N3/04

    CPC classification number: G06N3/049

    Abstract: Apparatus and methods for feedback in a spiking neural network. In one approach, spiking neurons receive sensory stimulus and context signal that correspond to the same context. When the stimulus provides sufficient excitation, neurons generate response. Context connections are adjusted according to inverse spike-timing dependent plasticity. When the context signal precedes the post synaptic spike, context synaptic connections are depressed. Conversely, whenever the context signal follows the post synaptic spike, the connections are potentiated. The inverse STDP connection adjustment ensures precise control of feedback- induced firing, eliminates runaway positive feedback loops, enables self-stabilizing network operation. In another aspect of the invention, the connection adjustment methodology facilitates robust context switching when processing visual information. When a context (such an object) becomes intermittently absent, prior context connection potentiation enables firing for a period of time. If the object remains absent, the connection becomes depressed thereby preventing further firing.

    Abstract translation: 用于在加标神经网络中反馈的装置和方法。 在一种方法中，刺激神经元接收对应于相同上下文的感觉刺激和上下文信号。 当刺激提供足够的激发时，神经元产生反应。 上下文连接根据反时限相关的可塑性进行调整。 当上下文信号在后突触尖端之前时，上下文突触连接被按下。 相反，只要上下文信号跟随突触后的尖峰，连接就被加强了。 逆STDP连接调整可确保精确控制反馈引发的触发，消除正反馈回路，实现自稳定网络运行。 在本发明的另一方面，当处理视觉信息时，连接调整方法有助于鲁棒的上下文切换。 当上下文（这样的对象）间歇地不存在时，先前的上下文连接增强使得能够触发一段时间。 如果物体不存在，则连接被压下，从而防止进一步的烧制。

    ELEMENTARY NETWORK DESCRIPTION FOR NEUROMORPHIC SYSTEMS
    10.
    发明申请
    ELEMENTARY NETWORK DESCRIPTION FOR NEUROMORPHIC SYSTEMS 审中-公开
    神经系统的基本网络描述

    公开(公告)号：WO2013043610A1

    公开(公告)日：2013-03-28

    申请号：PCT/US2012/055933

    申请日：2012-09-18

    Applicant: BRAIN CORPORATION ,  IZHIKEVICH, Eugene M. ,  SZATMARY, Botond ,  PETRE, Csaba ,  NAGESWARAN, Jayram Moorkanikara ,  PIEKNIEWSKI, Filip

    Inventor： IZHIKEVICH, Eugene M. ,  SZATMARY, Botond ,  PETRE, Csaba ,  NAGESWARAN, Jayram Moorkanikara ,  PIEKNIEWSKI, Filip

    IPC: G06F15/18

    CPC classification number: G06N3/049 ,  G06N3/0454 ,  G06N3/063 ,  G06N99/007

    Abstract: 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.

    Abstract translation: 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。

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