Abstract: According to one aspect of one or more embodiments of the present invention, a system comprises: an HTM network executable at least in part on multiple node processing units (NPUs). In one embodiment the NPUs include one or more nodes, each of which can be executed by its NPU. In one embodiment, the present invention includes a technique for coordinating and scheduling HTM computation across one or more CPUs which (1) enables concurrent computation (2) does not require a central point of control (e.g. a controller entity that “orchestrates” the computation), (3) does not require global synchronization, (4) in some embodiments ensures that the same results are achieved whether the nodes are executed in parallel or serially.

Abstract: A calculation processing apparatus for executing network calculations defined by hierarchically connecting a plurality of logical processing nodes that apply calculation processing to input data, sequentially designates a processing node which is to execute calculation processing based on sequence information that specifies an execution order of calculations of predetermined processing units to be executed by the plurality of processing nodes, so as to implement the network calculations, and executes the calculation processing of the designated processing node in the processing unit to obtain a calculation result. The calculation apparatus allocates partial areas of a memory to the plurality of processing nodes as ring buffers, and writes the calculation result in the memory while circulating a write destination of data to have a memory area corresponding to the amount of the calculation result of the processing unit as a unit.

Abstract: Neuron component and method for use in artificial neural networks (ANNs) with input synapses (204, 204b . . . 204n), each synapse includes multiple weights called synapse weights (206-1, 206-2, 206-3). Each synapse further includes a facility to modulate, or gate, an input signal connected to the synapses, by each of the respective synapse weights within the synapse, supplying the result of each modulating operation. The neuron also sums the results of all modulating operations, and subjects the results to a transfer function. Each of the multiple weights associated with a given synapse, may be specified to have its own weight-adjustment facility (214, 214b, 214c), with its own error-values (216, 216b, 216c), and its own specified learning and aspect (1000) includes a separate sum (1018, 1018b) and transfer function (1020, 1020b) for each synapse weight.

Abstract: A method and system of creating an approximate kernel matrix to train a kernel machine. In one embodiment of the invention, a set of kernel machine training data is partitioned into a set of partitioned training data based on a set of partition parameters. The set of partition parameters includes one or more of, an axis-aligned grid location, or an axis-aligned grid resolution in one embodiment of the invention. A partition matrix that approximates a kernel machine kernel matrix is created from the set of partitioned training data and the partition matrix is used to train a kernel machine in one embodiment of the invention.

Abstract: A method and system for processing a parameter for an item in an electronic order processing system is provided. The method has a first step of associating a calculation code with the item. The second step of the method is applying the calculation rule to the item to produce an amount. The third step of the method is providing the amount to an output device. Each operation within each of the first step, the second step and the third step may be modified and flow of execution amongst the first step, the second step and the third step remains the same.

Abstract: Embodiments of the present invention are directed to neuromorphic circuits containing two or more internal neuron computational units. Each internal neuron computational unit includes a synchronization-signal input for receiving a synchronizing signal, at least one input for receiving input signals, and at least one output for transmitting an output signal. A memristive synapse connects an output signal line carrying output signals from a first set of one or more internal neurons to an input signal line that carries signals to a second set of one or more internal neurons.

Abstract: At least one embodiment of the invention addresses the problem of accidental user operation (e.g., dialing) of a mobile communication device such as a mobile telephone. In this regard, embodiments of the invention provide a proactive solution for preventing unintended operation of a mobile device based on various types of input data of the device, most notably sensory data.

Abstract: In general, the invention is directed to a technique for selection of parameter configurations for a neurostimulator using neural networks. The technique may be employed by a programming device to allow a clinician to select parameter configurations, and then program an implantable neurostimulator to deliver therapy using the selected parameter configurations. The parameter configurations may include one or more of a variety of parameters, such as electrode configurations defining electrode combinations and polarities for an electrode set implanted in a patient. The electrode set may be carried by one or more implanted leads that are electrically coupled to the neurostimulator. In operation, the programming device executes a parameter configuration search algorithm to guide the clinician in the selection of parameter configurations. The search algorithm relies on a neural network that identifies potential optimum parameter configurations.

Abstract: This invention is in the field of machine learning and neural associative memory. In particular the invention discloses a neural associative memory structure for storing and maintaining associations between memory address patterns and memory content patterns using a neural network, as well as methods for retrieving such associations. A method for a non-linear synaptic learning of discrete synapses is disclosed, and its application on neural networks is laid out.

Abstract: A neural network comprising artificial neurons interconnected by connections, wherein each artificial neuron is configured to receive an input signal from and send an output signal to one or more of the other artificial neurons through one of the connections; each input and output signal is either positive or negative valued; and each artificial neuron has an activation at a time point, the activation being determined by at least input signals received by the artificial neuron, output signals sent by the artificial neuron, and a plurality of weights, wherein at least one weight is self-tuned at the time point. Also provided are methods of tuning neural networks.

Abstract: A method of emulating the human brain with its thought and rationalization processes is presented here, as well as a method of storing human-like thought. The invention provides for inclusion of psychological profiles, experience and societal position in an electronic emulation of the human brain. This permits a realistic human-like response by that emulation to the people and the interactive environment around it.

Abstract: A method is provided for designing a product. The method may include obtaining data records relating to one or more input variables and one or more output parameters associated with the product; and pre-processing the data records based on characteristics of the input variables. The method may also include selecting one or more input parameters from the one or more input variables; and generating a computational model indicative of interrelationships between the one or more input parameters and the one or more output parameters based on the data records. Further, the method may include providing a set of constraints to the computational model representative of a compliance state for the product; and using the computational model and the provided set of constraints to generate statistical distributions for the one or more input parameters and the one or more output parameters, wherein the one or more input parameters and the one or more output parameters represent a design for the product.

Abstract: An emotional memory control system and method for generating behavior. A sensory encoder provides a condensed encoding of a current circumstance received from an external environment. A memory associated with a regulator recognizes the encoding and activates one or more emotional springs according to a predefined set of instructions. The activated emotional springs can then transmit signals to at least one moment on a fractal moment sheet incorporated with a timeline for each channel in order to form one or more watersheds. An activation magnitude can be calculated for each moment and transmitted to a reaction relay. A synaptic link can then form between the moment and a motor encoder, thereby linking a specific moment with a specific action state.

Abstract: Fractal memory systems and methods include a fractal tree that includes one or more fractal trunks. One or more object circuits are associated with the fractal tree. The object circuit(s) can be configured from a plurality of nanotechnology-based components to provide a scalable distributed computing architecture for fractal computing. Additionally, a plurality of router circuits is associated with the fractal tree, wherein one or more fractal addresses output from a recognition circuit can be provided at a fractal trunk by the router circuits.

Abstract: A crystal lookup table used to define a matching relationship between a signal position of a detected event in a PET scanner and a corresponding detector pixel location is generated using a neural network-based algorithm, and is implemented by a FPGA.

Abstract: In order to utilize variable neuron thresholds and extended Hebb's rule in a neural network for proper control, a neuron device (101) for simulating a nerve cell comprises a threshold storage unit (102) storing a threshold variable ? and two threshold coefficients ??1 and ??2; an input reception unit (103) receiving one or multiple input signal values at predetermined time intervals; an output unit (104) outputting, as an output signal value, a value “1” for indicating that the neuron device (101) is firing when the sum total s of received input signal values is equal to or greater than the value of the stored threshold variable ?, and otherwise a value “0” for indicating that the neuron device (101) is resting; and a threshold updating unit (105) calculating ??1X+??2(X?1) using the output signal value X and the stored threshold coefficients ??1 and ??2 and updating the value of the threshold variable ? stored in the threshold storage unit (102) by increasing it by the calculation result.

Abstract: Associative plasticity rules are described to control the strength of inputs to an artificial neuron. Inputs to a neuron consist of both synaptic inputs and non-synaptic, voltage-regulated inputs. The neuron's output is voltage. Hebbian and anti-Hebbian-type plasticity rules are implemented to select amongst a spectrum of voltage-regulated inputs, differing in their voltage-dependence and kinetic properties. An anti-Hebbian-type rule selects inputs that predict and counteract deviations in membrane voltage, thereby generating an output that corresponds to a prediction error. A Hebbian-type rule selects inputs that predict and amplify deviations in membrane voltage, thereby contributing to pattern generation. In further embodiments, Hebbian and anti-Hebbian-type plasticity rules are also applied to synaptic inputs. In other embodiments, reward information is incorporated into Hebbian-type plasticity rules.

Abstract: The invention relates to an electric synapse that comprises a main conductor with a predetermined potential V1, a secondary conductor said secondary conductor having a potential VX1+ that can vary between Vref?Vn and Vref+Vn, Vref being the reference potential, a nanoconductor with an adjustable conductance W1, the main conductor being connected to said secondary conductor through an adjustable conductance nanoconductor, one end at least of the main conductor being connected to an electric neuron, said electric neuron being capable of realizing a threshold function and applying a training control potential Va of Vref?Vp or Vref+Vp to the main conductor when the voltage O1 obtained at the output of said threshold function is different from the expected voltage T1, wherein the Vn and Vp potentials comply with: 2*Vn<Vt and |Vp?Vn|<Vt<|Vp+Vn|.

Abstract: A system, method, and computer readable-media for creating a stable synthetic neural system. The method includes training an intellectual choice-driven synthetic neural system (SNS), training an emotional rule-driven SNS by generating emotions from rules, incorporating the rule-driven SNS into the choice-driven SNS through an evolvable interface, and balancing the emotional SNS and the intellectual SNS to achieve stability in a nontrivial autonomous environment with a Stability Algorithm for Neural Entities (SANE). Generating emotions from rules can include coding the rules into the rule-driven SNS in a self-consistent way. Training the emotional rule-driven SNS can occur during a training stage in parallel with training the choice-driven SNS. The training stage can include a self assessment loop which measures performance characteristics of the rule-driven SNS against core genetic code.

Abstract: 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 plurality of inputs and a plurality outputs thereof, wherein the multi-layer physical neural network comprises a plurality of layers, wherein each layer comprises one or more connection networks and associated neurons. Thereafter, a training wave can be initiated across the connection networks associated with an initial layer of the multi-layer physical neural network which propagates thereafter through succeeding connection networks of succeeding layers of the neural network by successively closing and opening switches associated with each layer. One or more feedback signals thereof can be automatically provided to strengthen or weaken nanoconnections associated with each connection network.

Abstract: A radio frequency (RF) calibrating system and a method for calibrating RF power of communication devices are provided. The method collects RF signals transmitted from the communication devices, and generates a group of training samples by retrieving measurement data from the RF signals. The method further constructs a neural network according to the group of training samples, calibrate RF power of the communication devices using the neural network, and generate corresponding calibration results of the RF power. In addition, the method generates a frequency spectrum of the RF power according to the calibration results of the RF power, and displays the frequency spectrum on a display device of the RF calibrating system.

Abstract: Determination of a connectivity-metrics for graphs representative of networks of interest. A graph that represents a network of interest is accessed. The graph includes nodes representing points in the network of interest, and edges corresponding to the nodes. Bit-vectors are generated corresponding to the nodes and/or edges, wherein individual bits in the bit-vectors respectively provide a logical indication of connectedness. The connectivity-metric is then determined by applying a logical bit operation to the plurality of bit-vectors. Examples of connectivity metrics include a connected components, shortest paths, betweenness, clustering, and tree-based determinations.

Abstract: An apparatus and method for processing mixed data for a selected task is provided. An input transformation module converts mixed data into converted data. A functional mapping module processes the converted data to provide a functional output for the selected task. The selected task may be one or a combination of a variety of possible tasks, including search, recall, prediction, classification, etc. For example, the selected task may be for data mining, database search, targeted marketing, computer virus detection, etc.

Abstract: In a parallel pulse signal processing apparatus including a plurality of pulse output arithmetic elements (2), a plurality of connection elements (3) which parallelly connect predetermined arithmetic elements, and a gate circuit (5) which selectively passes pulse signals from the plurality of connection elements, the arithmetic element inputs a plurality of time series pulse signals, executes predetermined modulation processing on the basis of the plurality of time series pulse signals which are input, and outputs a pulse signal on the basis of a result of modulation processing, wherein the gate circuit selectively passes, of the signals from the plurality of connection elements, a finite number of pulse signals corresponding to predetermined upper output levels.

Abstract: Designs for cognitive memory systems storing input data, images, or patterns, and retrieving it without knowledge of where stored when cognitive memory is prompted by query pattern that is related to sought stored pattern. Retrieval system of cognitive memory uses autoassociative neural networks and techniques for pre-processing query pattern to establish relationship between query pattern and sought stored pattern, to locate sought pattern, and to retrieve it and ancillary data.

Abstract: The calculation of L* in the magnetosphere can be calculated with essentially the same accuracy as with a physics based model at many times the speed by developing a surrogate trained to be a surrogate for the physics-based model. The trained model can then beneficially process input data falling within the training range of the surrogate model. The surrogate model can be a feedforward neural network and the physics-based model can be the TSK03 model. Operatively, the surrogate model can use parameters on which the physics-based model was based, spatial data for the location where L* is to be calculated. Surrogate models should be provided for each of a plurality of pitch angles. Accordingly, a surrogate model having a closed drift shell can be used from the plurality of models.

Abstract: In a hierarchical neural network having a module structure, learning necessary for detection of a new feature class is executed by a processing module which has not finished learning yet and includes a plurality of neurons which should learn an unlearned feature class and have an undetermined receptor field structure by presenting a predetermined pattern to a data input layer. Thus, a feature class necessary for subject recognition can be learned automatically and efficiently.

Abstract: Determination of a connectivity-metrics for graphs representative of networks of interest. A graph that represents a network of interest is accessed. The graph includes nodes representing points in the network of interest, and edges corresponding to the nodes. Bit-vectors are generated corresponding to the nodes and/or edges, wherein individual bits in the bit-vectors respectively provide a logical indication of connectedness. The connectivity-metric is then determined by applying a logical bit operation to the plurality of bit-vectors. Examples of connectivity metrics include a connected components, shortest paths, betweenness, clustering, and tree-based determinations.

Abstract: A method of emulating the human brain with its thought and rationalization processes is presented here, as well as a method of storing human-like thought. The invention provides for inclusion of psychological profiles, experience and societal position in an electronic emulation of the human brain. This permits a realistic human-like response by that emulation to the people and the interactive environment around it.

Abstract: A method of emulating the human brain with its thought and rationalization processes is presented here, as well as a method of storing human-like thought. The invention provides for inclusion of psychological profiles, experience and societal position in an electronic emulation of the human brain. This permits a realistic human-like response by that emulation to the people and the interactive environment around it.

Abstract: Associative memories include associative memory cells. A respective cell includes a sensor input, a prior association representation, a next association representation and an associative output. The cells are serially interconnected to form a linear array, such that the sensor inputs, the prior association representations and the next association representations of the serially connected cells are arranged in a sequence from distal to proximal cells based on affinities of associations among the series of sensor inputs. A respective cell also includes processing logic. The processing logic is responsive to the associated sensor input being active, to send a measure of the next association representation to an adjacent proximal cell and/or to send a measure of prior association representation to an adjacent distal cell.

Abstract: A first total number of nodes in a first node set directly linked to a first node can be computed. A second total number of nodes in a second node set directly linked to a second node can be computed. A shared total number of nodes in a union of the first node set and the second node set can be computed. A mutual information metric can then be computed from the first total, the second total, and the shared total. A decision as to whether a new connection should be added between the first node and the second node, which were not previously directly connected, can be determined from the value of mutual information metric.

Abstract: An apparatus includes a deformable structure in which a neural network comprising a plurality of deformation sensors, e.g. nanowire sensors, and distributed in-situ processing circuits. The circuits generate a signal characterising features of the local deformation of the structure and/or a command signal corresponding to the detected deformation. The structure may be a wearable sleeve that conforms to deformations of a user's skin, part of an electronic device, such as a touch sensitive screen, or an object in itself. The apparatus can provide a user interface, wherein a command corresponding to a current shape of the structure is generated and acted upon by a integrated or remote device, or a device for monitoring a user's position or movement e.g. for replication by a robotic device. The apparatus may have machine learning capability to improve the matching of commands with determined shapes of the deformable structure.

Abstract: An artificial neural network apparatus comprising an array of neural units, each comprising a router, at least one neuron device and at least one synapse unit. The routers of respective neural units communicate with one another using data packets. The synapse units receive and create analogue signals, the routers converting these signals from or into packet form for communication between neural units. The use of routers in this way simplifies the required interconnectivity between neural units in the array and so facilitates the creation of large artificial neural networks.

Abstract: By mathematizing input/output relations of a nerve circuit, a synapse and a cell body, it is intended to provide a nerve equivalent circuit, a synapse equivalent circuit and a cell body equivalent circuit whereby electrical characteristics in accordance with the physiological functions and physical structures of nerve cells are faithfully reproduced. A nerve equivalent circuit simulating the electrical characteristics of nerve cells wherein an input signal fin(t) and an output signal fout(t) satisfies the relationship represented by [Numerical formula 11], wherein kP, kI and TI are each a definite constant number, N represents the total number of synapses, M represents the total number of the kinds of the first transmitters carried by the synapses, and L represents the total number of the kinds of the second transmitters carried by the synapses.

Abstract: A system and method for characterizing a pattern, in which a spiking neural network having at least one layer of neurons is provided. The spiking neural network has a plurality of connected neurons for transmitting signals between the connected neurons. A model for inducing spiking in the neurons is specified. Each neuron is connected to a global regulating unit for transmitting signals between the neuron and the global regulating unit. Each neuron is connected to at least one other neuron for transmitting signals from this neuron to the at least one other neuron, this neuron and the at least one other neuron being on the same layer. Spiking of each neuron is synchronized according to a number of active neurons connected to the neuron. At least one pattern is submitted to the spiking neural network for generating sequences of spikes in the spiking neural network, the sequences of spikes (i) being modulated over time by the synchronization of the spiking and (ii) being regulated by the global regulating unit.

Abstract: A method, system, API, GUI, and computer readable media for managing object-based clusters is provided. The method provides a computer executable methodology for discovering, monitoring, and managing object-based clusters. The system provides a computer-based system for facilitating interactions with heterogeneous cluster solutions. The system includes computer components for detecting clusters and supervising detected clusters and/or components.

Abstract: A method of tracking short-range wireless communication in a vehicle. The method includes tracking a total usage time of a short-range wireless connection between a telematics unit and a portable wireless communication device, sending the tracked total usage time from the telematics unit to a call center via a wireless connection and generating a usage bill at the call center based on the total usage time.

Abstract: A physical neural network synapse chip and a method for forming such a synapse chip. The synapse chip can be configured to include an input layer comprising a plurality of input electrodes and an output layer comprising a plurality of output electrodes, such that the output electrodes are located perpendicular to the input electrodes. A gap is generally formed between the input layer and the output layer. A solution can then be provided which is prepared from a plurality of nanoconductors and a dielectric solvent. The solution is located within the gap, such that an electric field is applied across the gap from the input layer to the output layer to form nanoconnections of a physical neural network implemented by the synapse chip. Such a gap can thus be configured as an electrode gap. The input electrodes can be configured as an array of input electrodes, while the output electrodes can be configured as an array of output electrodes.

Abstract: A network architecture of radial basis function neural network system utilizes a blocking layer (4) to exclude successfully mapped neighborhoods from later node influence. A signal is inserted into the system at input nodes (I1, I2, . . . In), which then promulgates to a non-linear layer (2). The non-linear layer (2) comprises a number of non-linear activation function nodes (10). After passing through the non-linear layer (2), the signal passes through the blocking layer (4) that is comprised of either binary signal blocking nodes, or inverted symmetrical Sigmoidal signal blocking nodes (12) that act in a binary fashion. Finally, the signal is weighted by a weighting function (6a, 6b, 6c, 6n), summed at a summer (8) and outputted at (O).

Abstract: A method for automated feature selection is provided. One or more initial sets of features are generated and evaluated to determine quality scores for the feature sets. Selected ones of the feature sets are (i) chosen according to the quality scores and modified to generate a generation of modified feature sets, (ii) the modified feature sets are evaluated to determine quality scores for the modified feature sets, and (i) and (ii) are repeated until a modified feature set is satisfactory.

Abstract: A method of configuring a communication channel prior to the transmission of an input signal along the communication channel, the communication channel comprising a plurality of sub-channels, the method comprising determining the strength of the input signal and in accordance with the determined signal strength, selecting a set of the plurality of sub-channels and transmitting said in put signal along the set of sub-channels in parallel, wherein each of the sub-channels has a predetermined noise characteristic such that the set of selected sub-channels exhibits a combined noise characteristic in which the standard deviation of the noise is proportional to the signal strength.

Abstract: Systems and methods are disclosed for forming reconfigurable neural networks with interconnected FPGAs each having a packet router. Neural network nodes are formed within the FPGAs and connections between nodes within an FPGA and connections to nodes external to the FPGA are made using packet routers that are configured within each FPGA. The FPGAs can be connected to each other using high-speed interconnects, such as high-speed serial digital interconnects. The FPGA arrays with packet routing allow for dynamic and reconfigurable neural networks to be formed thereby greatly improving the performance and intelligence of the neural network.

Abstract: A method and system is provided for predicting loads within a power system through the training of on-line and an off-line neural networks. Load data and load increments are used with an on-line load prediction scheme to generate predicted load values to optimize power generation and minimize costs. This objective is achieved by employing a method and system which predicts short term load trends through the use of historical load data and short term load forecast data.

Abstract: Solver state merging in parallel constraint satisfaction problem (CSP) solvers. Solver state during processing of a computational thread of parallel CSP solvers is represented as a set of support graphs. The support graphs are merged in a pairwise fashion, yielding a new conflict-free graph. The merge process is free of cycles, conflicts are removed, and thread processing is lock-free. The architecture can be applied, generally, in any CSP solver (e.g., a Boolean SAT solver) having certain formal properties. A system is provided that facilitates solver processing, the system comprising a bookkeeping component for representing input solver state of a computational thread as a set of graphs, and a merge component for pairwise merging of at least two input graphs of the set of graphs into a merged graph that represents final state of the computational thread.

Abstract: An artificial neuron integrates current and prior information, each of which predicts the state of a part of the world. The neuron's output corresponds to the discrepancy between the two predictions, or prediction error. Inputs contributing prior information are selected in order to minimize the error, which can occur through an anti-Hebbian-type plasticity rule. Current information sources are selected to maximize errors, which can occur through a Hebbian-type rule. This insures that the neuron receives new information from its external world that is not redundant with the prior information that the neuron already possesses. By learning on its own to make predictions, a neuron or network of these neurons acquires information necessary to generate intelligent and advantageous outputs.

Abstract: The present invention provides a method of real-time crystal peak tracking for avalanche-photodiode (APD) detectors on positron emission tomography (PET) scanners that satisfies the need to compensate for the significant gain drifting due to thermal variations in APD detectors on PET scanners.

Abstract: Fractal memory systems and methods include a fractal tree that includes one or more fractal trunks. One or more object circuits are associated with the fractal tree. The object circuit(s) is configured from a plurality of nanotechnology-based components to provide a scalable distributed computing architecture for fractal computing. Additionally, a plurality of router circuits is associated with the fractal tree, wherein one or more fractal addresses output from a recognition circuit can be provided at a fractal trunk by the router circuits.

Abstract: This invention provides an interconnecting neural network system capable of freely taking a network form for inputting a plurality of input vectors, and facilitating additionally training an artificial neural network structure. The artificial neural network structure is constructed by interconnecting RBF elements relating to each other among all RBF elements via a weight. Each RBF element outputs an excitation strength according to a similarity between each input vector and a centroid vector based on a radius base function when the RBF element is excited by the input vector applied from an outside, and outputs a pseudo excitation strength obtained based on the excitation strength output from the other RBF element when the RBF element is excited in a chain reaction to excitation of the other neuron connected to the neuron.

Abstract: A system, method and computer program for developing artificial intelligence through the generational evolution of one or more genomes. Each genome includes a set of functions. The method includes creating one or more cortices, operating the one or more cortices to perform one or more specified tasks, calculating a fitness score for each cortex based on its ability to perform the specified tasks, and selecting one or more of the cortices based on the respective fitness scores. Each cortex includes a plurality of cortical units. Each cortical unit includes a set of functions. Each cortical unit is created from the one or more genomes.