公开(公告)号：US20140365417A1

公开(公告)日：2014-12-11

申请号：US14466917

申请日：2014-08-22

Applicant: Brain Corporation

Inventor： Filip Piekniewski ,  Micah Richert ,  Eugene Izhikevich

IPC: G06N3/08 ,  G06N99/00

CPC classification number: G06N3/08 ,  G06N3/02 ,  G06N3/049 ,  G06N3/10 ,  G06N99/005

Abstract: Apparatus and methods for activity based plasticity in a spiking neuron network adapted to process sensory input. In one approach, the plasticity mechanism of a connection may comprise a causal potentiation portion and an anti-causal portion. The anti-causal portion, corresponding to the input into a neuron occurring after the neuron response, may be configured based on the prior activity of the neuron. When the neuron is in low activity state, the connection, when active, may be potentiated by a base amount. When the neuron activity increases due to another input, the efficacy of the connection, if active, may be reduced proportionally to the neuron activity. Such functionality may enable the network to maintain strong, albeit inactive, connections available for use for extended intervals.

Abstract translation: 适用于处理感觉输入的加标神经元网络中基于活动的可塑性的装置和方法。 在一种方法中，连接的可塑性机构可以包括因果增强部分和反因果部分。 可以基于神经元的先前活动来配置对应于在神经元响应之后发生的神经元的输入的反因果部分。 当神经元处于低活动状态时，当活动时，连接可能被基础量增强。 当神经元活动由于另一个输入而增加时，如果活动，连接的功效可以与神经元活动成比例地降低。 这样的功能可以使得网络能够维持可用于延长的间隔的强的，虽然是不活动的连接。

公开(公告)号：US20140250037A1

公开(公告)日：2014-09-04

申请号：US14198550

申请日：2014-03-05

Applicant: BRAIN CORPORATION

Inventor： Eugene M. Izhikevich ,  Botond Szatmary ,  Csaba Petre ,  Filip Piekniewski

IPC: G06N3/10

CPC classification number: G06N3/10 ,  G06N3/04 ,  G06N3/049

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. 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.

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。 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。

公开(公告)号：US20140156574A1

公开(公告)日：2014-06-05

申请号：US13691554

申请日：2012-11-30

Applicant: BRAIN CORPORATION

Inventor： Filip Piekniewski ,  Micah Richert ,  Dimitry Fisher ,  Eugene Izhikevich

IPC: G06N3/02

CPC classification number: G06N3/02 ,  G06N3/049 ,  G06N3/088

Abstract: Apparatus and methods for activity based plasticity in a spiking neuron network adapted to process sensory input. In one embodiment, the plasticity mechanism may be configured for example based on activity of one or more neurons providing feed-forward stimulus and activity of one or more neurons providing inhibitory feedback. When an inhibitory neuron generates an output, inhibitory connections may be potentiated. When an inhibitory neuron receives inhibitory input, the inhibitory connection may be depressed. When the inhibitory input arrives subsequent to the neuron response, the inhibitory connection may be depressed. When input features are unevenly distributed in occurrence, the plasticity mechanism is capable of reducing response rate of neurons that develop receptive fields to more prevalent features. Such functionality may provide network output such that rarely occurring features are not drowned out by more widespread stimulus.

Abstract translation: 适用于处理感觉输入的加标神经元网络中基于活动的可塑性的装置和方法。 在一个实施方案中，可塑性机制可以例如基于提供前馈刺激的一个或多个神经元的活性和提供抑制反馈的一个或多个神经元的活性来配置。 当抑制性神经元产生输出时，可以增强抑制性连接。 当抑制性神经元接受抑制性输入时，可能抑制抑制性连接。 当抑制输入在神经元响应之后到达时，可能抑制抑制性连接。 当输入特征在发生时不均匀分布时，可塑性机制能够将发展接受场的神经元的反应率降低到更普遍的特征。 这样的功能可以提供网络输出，使得很少发生的特征不被更广泛的刺激所淹没。