公开(公告)号：US09311596B2

公开(公告)日：2016-04-12

申请号：US14198550

申请日：2014-03-05

Applicant: QUALCOMM TECHNOLOGIES INC.

Inventor： Eugene M. Izhikevich ,  Botond Szatmary ,  Csaba Petre ,  Filip Piekniewski ,  Michael-David Nakayoshi Canoy ,  Robert Howard Kimball ,  Jan Krzys Wegrzyn

IPC: G06N3/04 ,  G06N3/02 ,  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：US09412064B2

公开(公告)日：2016-08-09

申请号：US13868944

申请日：2013-04-23

Applicant: QUALCOMM TECHNOLOGIES INC.

Inventor： Botond Szatmary ,  Micah Richert ,  Oleg Sinyavskiy ,  Eugene Izhikevich

IPC: G06F15/18 ,  G06N3/04

CPC classification number: G06N3/049

Abstract: Apparatus and methods for event based communication in a spiking neuron network. The network may comprise units communicating by spikes via synapses. The spikes may communicate a payload data. The data may comprise one or more bits. The payload may be stored in a buffer of a pre-synaptic unit and be configured to accessed by the post-synaptic unit. Spikes of different payload may cause different actions by the recipient unit. Sensory input spikes may cause postsynaptic response and trigger connection efficacy update. Teaching input spikes trigger the efficacy update without causing the post-synaptic response.

Abstract translation: 用于加速神经元网络中基于事件的通信的装置和方法。 网络可以包括通过突触通过尖峰通信的单元。 峰值可以传送有效载荷数据。 数据可以包括一个或多个位。 有效载荷可以存储在突触前单元的缓冲器中，并且被配置为由突触后单元访问。 不同有效负载的峰值可能导致接收单元的不同动作。 感觉输入尖峰可能会引起突触后反应并触发连接功效更新。 教学输入尖峰触发功效更新，而不会导致突触后反应。

公开(公告)号：US20150074026A1

公开(公告)日：2015-03-12

申请号：US14020376

申请日：2013-09-06

Applicant: QUALCOMM TECHNOLOGIES INC.

Inventor： Botond Szatmary ,  Eugene Izhikevich

IPC: G06N3/08

CPC classification number: G06N3/049

Abstract: Event based communication in a spiking neuron network may be provided. The network may comprise units communicating by spikes via synapses. Responsive to a spike generation, a unit may be configured to update states of outgoing synapses. The spikes may communicate a payload data. The data may comprise one or more bits. The payload may be stored in a buffer of a pre-synaptic unit and be configured to accessed by the post-synaptic unit. Spikes of different payload may cause different actions by the recipient unit. Sensory input spikes may cause postsynaptic response and trigger connection efficacy update. Teaching input may be used to modulate plasticity.

Abstract translation: 可以提供在尖峰神经元网络中基于事件的通信。 网络可以包括通过突触通过尖峰通信的单元。 响应于尖峰生成，单元可以被配置为更新外出突触的状态。 峰值可以传送有效载荷数据。 数据可以包括一个或多个位。 有效载荷可以存储在突触前单元的缓冲器中，并且被配置为由突触后单元访问。 不同有效负载的峰值可能导致接收单元的不同动作。 感觉输入尖峰可能会引起突触后反应并触发连接功效更新。 教学输入可用于调节可塑性。

公开(公告)号：US09092738B2

公开(公告)日：2015-07-28

申请号：US14198446

申请日：2014-03-05

Applicant: QUALCOMM TECHNOLOGIES INC.

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

IPC: G06N3/10 ,  G06N3/08 ,  G05B13/02

CPC classification number: G06N3/08 ,  G05B13/027 ,  G06N3/049 ,  G06N3/10

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. 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.

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。 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。