Denso Corporation

Qualcomm Incorporated

Other

Jason Frank Hunzinger

Victor Hokkiu Chan

Jilei Hou

Mark W Cheng

Rohit Kapoor

Bibhu P Mohanty

Jeffrey Alexander Levin

Vladimir Aparin

Michael-David Nakayoshi Canoy

Bardia Fallah Behabadi

Haitong Sun

Sharad Deepak Sambhwani

Aziz Gholmieh

Vinay Chande

Paul E Bender

Danlu Zhang

Ravi Agarwal

Pawan Kumar Baheti

Anthony Sarah

Yibo Jiang

Eugene Baik

Ramon A Khalona

Thomas Zheng

Robert Yamaguchi

Gina Marcela Escobar Mora

Jilel Hou

Aspects relate to a Remote NodeB Relay that appears similar to a NodeB, a Radio Network Controller (RNC), and served mobile devices. Also provided is a Super-Light Router Relay that can provide better performance and QoS to served mobile devices while mitigating modifications to mobile devices, NodeBs, or interfaces between RNC and intermediary NodeBs. Aspects also relate to an Internet Protocol (IP) Relay that requires few, if any, modifications to mobile devices, NodeBs, or interfaces between RNC and intermediary NodeBs. Further, changes to an RNC and/or a core network can be mitigated though utilization of a strategic Relay Gateway.

Relays in a multihop heterogeneous UMTS wireless communication system

A method of monitoring a neural network includes monitoring activity of the neural network. The method also includes detecting a condition based on the activity. The method further includes performing an exception event based on the detected condition.

Neural watchdog

Methods and apparatus are provided for implementing structural plasticity in an artificial nervous system. One example method for altering a structure of an artificial nervous system generally includes determining a synapse in the artificial nervous system for reassignment, determining a first artificial neuron and a second artificial neuron for connecting via the synapse, and reassigning the synapse to connect the first artificial neuron with the second artificial neuron. Another example method for operating an artificial nervous system, generally includes determining a synapse in the artificial nervous system for assignment; determining a first artificial neuron and a second artificial neuron for connecting via the synapse, wherein at least one of the synapse or the first and second artificial neurons are determined randomly or pseudo-randomly; and assigning the synapse to connect the first artificial neuron with the second artificial neuron.

Implementing structural plasticity in an artificial nervous system

Aspects of the present disclosure support techniques for neural pattern sequence completion and neural pattern hierarchical replay. At least a portion of a pattern can be invoked for replay upon referencing the pattern and learning relational aspects between elements of the pattern and the referencing of the pattern using hierarchical levels of neurons.

Methods and apparatus for neural pattern sequence completion and neural pattern hierarchical replay by invoking replay of a referenced neural pattern

Aspects of the present disclosure support techniques for neural component memory transfer. A pattern in a plurality of afferent neuron outputs can be first referenced with one or more referencing neurons. One or more first relational aspects can be matched, with one or more first relational aspect neurons, between the referenced pattern and an output of the one or more referencing neurons. The referenced pattern can be transferred to one or more transferee neurons by inducing the plurality of afferent neurons to output a pattern substantially the same as the referenced pattern by the one or more referencing neurons.

Methods and apparatus for neural component memory transfer of a referenced pattern by including neurons to output a pattern substantially the same as the referenced pattern

Aspects of the present disclosure support techniques for unsupervised neural component replay. A pattern in a plurality of afferent neuron outputs can be first referenced with one or more referencing neurons. One or more relational aspects can be matched, with one or more relational aspect neurons, between the pattern and an output of the one or more referencing neurons. One or more of the plurality of afferent neurons can be induced to output a pattern that is substantially the same as the referenced pattern by the one or more referencing neurons.

Methods and apparatus for unsupervised neural component replay by referencing a pattern in neuron outputs

Methods and apparatus are provided for inferring and accounting for missing post-synaptic events (e.g., a post-synaptic spike that is not associated with any pre-synaptic spikes) at an artificial neuron and adjusting spike-timing dependent plasticity (STDP) accordingly. One example method generally includes receiving, at an artificial neuron, a plurality of pre-synaptic spikes associated with a synapse, tracking a plurality of post-synaptic spikes output by the artificial neuron, and determining at least one of the post-synaptic spikes is associated with none of the plurality of pre-synaptic spikes. According to certain aspects, determining inferring missing post-synaptic events may be accomplished by using a flag, counter, or other variable that is updated on post-synaptic firings. If this post-ghost variable changes between pre-synaptic-triggered adjustments, then the artificial nervous system can determine there was a missing post-synaptic pairing.

Post ghost plasticity

Certain aspects of the present disclosure provide methods and apparatus for spiking neural computation of general linear systems. One example aspect is a neuron model that codes information in the relative timing between spikes. However, synaptic weights are unnecessary. In other words, a connection may either exist (significant synapse) or not (insignificant or non-existent synapse). Certain aspects of the present disclosure use binary-valued inputs and outputs and do not require post-synaptic filtering. However, certain aspects may involve modeling of connection delays (e.g., dendritic delays). A single neuron model may be used to compute any general linear transformation x=AX+BU to any arbitrary precision. This neuron model may also be capable of learning, such as learning input delays (e.g., corresponding to scaling values) to achieve a target output delay (or output value). Learning may also be used to determine a logical relation of causal inputs.

Methods and apparatus for spiking neural computation

Methods and apparatus are provided for implementing behavioral homeostasis in artificial neurons that use a dynamical spiking neuron model. The homeostatic mechanism may be driven by neuron state, rather than by neuron spiking rate, and this mechanism may drive changes to the neuron temporal dynamics, rather than to contributions of input or weights. As a result, certain aspects of the present disclosure are a more natural fit with spiking neural networks and have many functional and computational advantages. One example method for implementing homeostasis of an artificial nervous system generally includes determining one or more state variables of a neuron model used by an artificial neuron, based at least in part on dynamics of the neuron model; determining one or more conditions based at least in part on the state variables; and adjusting the dynamics based at least in part on the conditions.

Behavioral homeostasis in artificial nervous systems using dynamical spiking neuron models

Certain aspects of the present disclosure support a technique for strategic synaptic failure and learning in spiking neural networks. A synaptic weight for a synaptic connection between a pre-synaptic neuron and a post-synaptic neuron can be first determined (e.g., according to a learning rule). Then, one or more failures of the synaptic connection can be determined based on a set of characteristics of the synaptic connection. The one or more failures can be omitted from computation of a neuronal behavior of the post-synaptic neuron.

Method and apparatus for strategic synaptic failure and learning in spiking neural networks

Certain aspects of the present disclosure support a technique for neural temporal coding, learning and recognition. A method of neural coding of large or long spatial-temporal patterns is also proposed. Further, generalized neural coding and learning with temporal and rate coding is disclosed in the present disclosure.

Method and apparatus for neural temporal coding, learning and recognition

Certain aspects of the present disclosure support a technique for neural learning of natural multi-spike trains in spiking neural networks. A synaptic weight can be adapted depending on a resource associated with the synapse, which can be depleted by weight change and can recover over time. In one aspect of the present disclosure, the weight adaptation may depend on a time since the last significant weight change.

Method and apparatus for neural learning of natural multi-spike trains in spiking neural networks

Certain aspects of the present disclosure relate to a technique for adaptive structural delay plasticity applied in spiking neural networks. With the proposed method of structural delay plasticity, the requirement of modeling multiple synapses with different delays can be avoided. In this case, far fewer potential synapses should be modeled for learning.

Method and apparatus for structural delay plasticity in spiking neural networks

In accordance with aspects of the disclosure, a method, apparatus, and computer program product are provided for wireless communication. The method, apparatus, and computer program product may be provided for detecting a change in power of received signals and adjusting amplification of the received signals based on the detected change in power prior to transmitting the signals.

Growing community of inventors

Escondido, CA, United States of America

Jason Frank Hunzinger