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US20090089230 |
COMPUTER GAME WITH INTUITIVE LEARNING CAPABILITY
A computer game and a method of providing learning capability thereto are provided. The computer game has an objective of matching a skill level of the computer game with a skill level of a game... |
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US20150052092 |
METHODS AND SYSTEMS OF BRAIN-LIKE COMPUTING VIRTUALIZATION
The invention discloses the technology of brain-like computing virtualization. Brain-like computing means the computing technology to mimic human brain and generate human intelligence... |
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US20120246102 |
ADAPTIVE ANALYTICAL BEHAVIORAL AND HEALTH ASSISTANT SYSTEM AND RELATED METHOD OF USE
This present disclosure relates to systems and methods for providing an Adaptive Analytical Behavioral and Health Assistant. These systems and methods may include collecting one or more of patient... |
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US20150052094 |
POST GHOST PLASTICITY
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... |
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US20150248118 |
SYSTEMS AND METHODS FOR MODELING ENERGY CONSUMPTION AND CREATING DEMAND RESPONSE STRATEGIES USING LEARNING-BASED APPROACHES
According to various implementations, a demand response (DR) strategy system is described that can effectively model the HVAC energy consumption of a house using a learning based approach that is... |
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US20150019468 |
THERMODYNAMIC COMPUTING
Methods and systems for thermodynamic computing based on the attractor dynamics of volatile dissipative electronics attempting to maximize circuit power consumption. A general model of memristive... |
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US20130159231 |
MULTI-MODAL NEURAL NETWORK FOR UNIVERSAL, ONLINE LEARNING
In one embodiment, the present invention provides a neural network comprising multiple modalities. Each modality comprises multiple neurons. The neural network further comprises an interconnection... |
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US20130226851 |
METHOD AND APPARATUS FOR MODELING NEURAL RESOURCE BASED SYNAPTIC PLACTICITY
Certain aspects of the present disclosure support a method of designing the resource model in hardware (or software) for learning spiking neural networks. The present disclosure comprises... |
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US20150213356 |
METHOD FOR CONVERTING VALUES INTO SPIKES
A method for transmitting values in a neural network includes obtaining a parameter value. The method also includes encoding the parameter value based on at least one value used by a neuron. The... |
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US20150161506 |
EFFECTING MODULATION BY GLOBAL SCALAR VALUES IN A SPIKING NEURAL NETWORK
Methods and apparatus are provided for effecting modulation using global scalar values in a spiking neural network. One example method for operating an artificial nervous system generally includes... |
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US20100057654 |
SELF-LEARNING SYSTEM AND METHOD FOR PROVIDING A LOTTERY TICKET AT A POINT OF SALE DEVICE
A system for managing a purchase agreement, including: a memory element for at least one specially-programmed general purpose computer, for storing an artificial intelligence program (AIP), and a... |
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US20130325775 |
DYNAMICALLY RECONFIGURABLE STOCHASTIC LEARNING APPARATUS AND METHODS
Generalized learning rules may be implemented. A framework may be used to enable adaptive signal processing system to flexibly combine different learning rules (supervised, unsupervised,... |
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US20120290512 |
Methods for creating a situation dependent library of affective response
Generating a situation-dependent library comprising a user's expected response to tokens representing stimuli that influence the user's affective state, including: receiving samples comprising... |
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US20140258194 |
GENERIC METHOD FOR DESIGNING SPIKE-TIMING DEPENDENT PLASTICITY (STDP) CURVES
Methods and apparatus are provided for designing spike-timing dependent plasticity (STDP) curves whose parameter values are based on a set of equations. One example method generally includes... |
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US20150006454 |
NETWORK-PROBABILITY RECOMMENDATION SYSTEM
A method/apparatus/system for generating a recommendation based on user interactions with nodes and associated tasks within a prerequisite graph. The recommendation is generated by identifying the... |
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US20150134582 |
IMPLEMENTING SYNAPTIC LEARNING USING REPLAY IN SPIKING NEURAL NETWORKS
Aspects of the present disclosure relate to methods and apparatus for training an artificial nervous system. According to certain aspects, timing of spikes of an artificial neuron during a... |
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US20140250039 |
UNSUPERVISED, SUPERVISED AND REINFORCED LEARNING VIA SPIKING COMPUTATION
The present invention relates to unsupervised, supervised and reinforced learning via spiking computation. The neural network comprises a plurality of neural modules. Each neural module comprises... |
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US20130073493 |
UNSUPERVISED, SUPERVISED, AND REINFORCED LEARNING VIA SPIKING COMPUTATION
The present invention relates to unsupervised, supervised and reinforced learning via spiking computation. The neural network comprises a plurality of neural modules. Each neural module comprises... |
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US20140032463 |
ACCURATE AND FAST NEURAL NETWORK TRAINING FOR LIBRARY-BASED CRITICAL DIMENSION (CD) METROLOGY
Approaches for accurate neural network training for library-based critical dimension (CD) metrology are described. Approaches for fast neural network training for library-based CD metrology are... |
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US20120226644 |
Accurate and Fast Neural network Training for Library-Based Critical Dimension (CD) Metrology
Approaches for accurate neural network training for library-based critical dimension (CD) metrology are described. Approaches for fast neural network training for library-based CD metrology are... |
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US20140129498 |
METHOD FOR NON-SUPERVISED LEARNING IN AN ARTIFICIAL NEURAL NETWORK BASED ON MEMRISTIVE NANODEVICES, AND ARTIFICIAL NEURAL NETWORK IMPLEMENTING SAID METHOD
An unsupervised learning method is provided implemented in an artificial neural network based on memristive devices. It consists notably in producing an increase in the conductance of a synapse... |
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US20120109865 |
USING AFFINITY MEASURES WITH SUPERVISED CLASSIFIERS
A non-binary affinity measure between any two data points for a supervised classifier may be determined. For example, affinity measures may be determined for tree, kernel-based, nearest... |
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US20130268472 |
ARTIFICIAL INTELLIGENCE AND METHODS FOR RELATING HERBAL INGREDIENTS WITH ILLNESSES IN TRADITIONAL CHINESE MEDICINE
Described herein are systems and methods for identifying herbal ingredients effective in treating illnesses in Traditional Chinese Medicine (TCM) using an artificial neural network. |
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US20140114893 |
LOW-POWER EVENT-DRIVEN NEURAL COMPUTING ARCHITECTURE IN NEURAL NETWORKS
A neural network includes an electronic synapse array of multiple digital synapses interconnecting a plurality of digital electronic neurons. Each synapse interconnects an axon of a pre-synaptic... |
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US20130046716 |
METHOD AND APPARATUS FOR NEURAL TEMPORAL CODING, LEARNING AND RECOGNITION
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... |
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US20150095273 |
AUTOMATED METHOD FOR MODIFYING NEURAL DYNAMICS
A method for improving neural dynamics includes obtaining prototypical neuron dynamics. The method also includes modifying parameters of a neuron model so that the neuron model matches the... |
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US20140344202 |
NEURAL MODEL FOR REINFORCEMENT LEARNING
A neural model for reinforcement-learning and for action-selection includes a plurality of channels, a population of input neurons in each of the channels, a population of output neurons in each... |
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US20130339280 |
LEARNING SPIKE TIMING PRECISION
Certain aspects of the present disclosure provide methods and apparatus for learning or determining delays between neuron models so that the uncertainty in input spike timing is accounted for in... |
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US20120303566 |
METHOD AND APPARATUS FOR UNSUPERVISED TRAINING OF INPUT SYNAPSES OF PRIMARY VISUAL CORTEX SIMPLE CELLS AND OTHER NEURAL CIRCUITS
Certain aspects of the present disclosure present a technique for unsupervised training of input synapses of primary visual cortex (V1) simple cells and other neural circuits. The proposed... |
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US20120290513 |
Habituation-compensated library of affective response
Generating a habituation-compensated library comprising a user's expected response to tokens representing stimuli that influence the user's affective state, the method comprising: receiving... |
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US20150248609 |
NEURAL NETWORK ADAPTATION TO CURRENT COMPUTATIONAL RESOURCES
Methods and apparatus are provided for processing in an artificial nervous system. According to certain aspects, resolution of one or more functions performed by processing units of a neuron model... |
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US20140032461 |
SYNAPSE MAINTENANCE IN THE DEVELOPMENTAL NETWORKS
The developmental neural network is trained using a synaptic maintenance process. Synaptogenic trimming is first performed on the neuron inputs using a synaptogenic factor for each neuron based on... |
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US20140188771 |
NEUROMORPHIC AND SYNAPTRONIC SPIKING NEURAL NETWORK CROSSBAR CIRCUITS WITH SYNAPTIC WEIGHTS LEARNED USING A ONE-TO-ONE CORRESPONDENCE WITH A SIMULATION
Embodiments of the invention provide neuromorphic-synaptronic systems, including neuromorphic-synaptronic circuit chips implementing spiking neural network with synaptic weights learned using... |
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US20150026110 |
SPIKING MODEL TO LEARN ARBITRARY MULTIPLE TRANSFORMATIONS FOR A SELF-REALIZING NETWORK
A neural network, wherein a portion of the neural network comprises: a first array having a first number of neurons, wherein the dendrite of each neuron of the first array is provided for... |
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US20110131166 |
FUZZY USERS' ATTRIBUTES PREDICTION BASED ON USERS' BEHAVIORS
A method, apparatus, system, article of manufacture, and computer readable storage medium provide the ability to predict and utilize a user's attributes. A sample user behavior and a sample user... |
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US20150170027 |
NEURONAL DIVERSITY IN SPIKING NEURAL NETWORKS AND PATTERN CLASSIFICATION
A method for providing diversity in a set of neurons in a neuron model includes retrieving a set of parameters for the set of neurons. The method also includes perturbing the set of parameters... |
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US20120109864 |
NEUROMORPHIC AND SYNAPTRONIC SPIKING NEURAL NETWORK WITH SYNAPTIC WEIGHTS LEARNED USING SIMULATION
Embodiments of the invention provide neuromorphic-synaptronic systems, including neuromorphic-synaptronic circuits implementing spiking neural network with synaptic weights learned using... |
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US20150242744 |
STOCHASTIC DELAY PLASTICITY
A method of operating a spiking neural network having neurons coupled together with a synapse includes monitoring a timing of a presynaptic spike and monitoring a timing of a postsynaptic spike.... |
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US20150019467 |
FRAMEWORK FOR THE EVOLUTION OF ELECTRONIC NEURAL ASSEMBLIES TOWARD DIRECTED GOALS
Methods and systems for the evolution of electronic neural assemblies toward directed goals. A compact computing architecture includes electronics that allows users of such an architecture to... |
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US20130138589 |
EXPLOITING SPARSENESS IN TRAINING DEEP NEURAL NETWORKS
Deep Neural Network (DNN) training technique embodiments are presented that train a DNN while exploiting the sparseness of non-zero hidden layer interconnection weight values. Generally, a fully... |
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US20150242745 |
EVENT-BASED INFERENCE AND LEARNING FOR STOCHASTIC SPIKING BAYESIAN NETWORKS
A method of performing event-based Bayesian inference and learning includes receiving input events at each node. The method also includes applying bias weights and/or connection weights to the... |
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US20090228415 |
MULTILAYER TRAINING IN A PHYSICAL NEURAL NETWORK FORMED UTILIZING NANOTECHNOLOGY
A method for and system for training a connection network located between neuron layers within a multi-layer physical neural network. A multi-layer physical neural network can be formed having a... |
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US20150206049 |
MONITORING NEURAL NETWORKS WITH SHADOW NETWORKS
A method for generating an event includes monitoring a first neural network with a second neural network. The method also includes generating an event based at least in part on the monitoring. The... |
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US20120041797 |
PROGRESS MONITORING METHOD
The progress monitoring method is based on a critical path method (CPM) and conducts comparisons against multiple possible outcomes utilizing neural networks that classify planned progress at... |
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US20080294580 |
Neuromorphic Device for Proofreading Connection Adjustments in Hardware Artificial Neural Networks
A hardware-implemented method for proofreading updates of connections in a hardware artificial neural network (hANN) includes computing a draft weight change independently at a connection between... |
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US20150081606 |
Reduction of Computation Complexity of Neural Network Sensitivity Analysis
As part of neural network sensitivity analysis, base outputs of hidden layer nodes of a neural network model for non-perturbed variables can be reused when perturbing the variables. Such an... |
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US20120011088 |
COMMUNICATION AND SYNAPSE TRAINING METHOD AND HARDWARE FOR BIOLOGICALLY INSPIRED NETWORKS
Certain embodiments of the present disclosure support techniques for training of synapses in biologically inspired networks. Only one device based on a memristor can be used as a synaptic... |
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US20120254086 |
DEEP CONVEX NETWORK WITH JOINT USE OF NONLINEAR RANDOM PROJECTION, RESTRICTED BOLTZMANN MACHINE AND BATCH-BASED PARALLELIZABLE OPTIMIZATION
A method is disclosed herein that includes an act of causing a processor to access a deep-structured, layered or hierarchical model, called deep convex network, retained in a computer-readable... |
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US20130282634 |
DEEP CONVEX NETWORK WITH JOINT USE OF NONLINEAR RANDOM PROJECTION, RESTRICTED BOLTZMANN MACHINE AND BATCH-BASED PARALLELIZABLE OPTIMIZATION
A method is disclosed herein that includes an act of causing a processor to access a deep-structured, layered or hierarchical model, called a deep convex network, retained in a computer-readable... |
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US20130018833 |
NEURAL NETWORK SYSTEM AND METHOD FOR CONTROLLING OUTPUT BASED ON USER FEEDBACK
For various information sources, information output based on user feedback about information from the sources is controlled. A neural network module selects object(s) to receive information from... |