| Class / Patent application number | Description | Number of patent applications / Date published |
|---|
| 706044000 | Neural simulation environment | 10 |
| 20090076993 | SYSTEM AND METHOD FOR CORTICAL SIMULATION - A cortical simulator optimizing the simulation scale and time through computationally efficient simulation of neurons in a clock-driven and synapses in an event-driven fashion, memory efficient representation of simulation state, and communication efficient message exchanges. | 03-19-2009 |
| 20090099989 | SYSTEM AND METHOD FOR CORTICAL SIMULATION - A cortical simulator optimizing the simulation scale and time through computationally efficient simulation of neurons in a clock-driven and synapses in an event-driven fashion, memory efficient representation of simulation state, and communication efficient message exchanges. | 04-16-2009 |
| 20100211537 | ARTIFICIAL COGNITIVE SYSTEM WITH AMARI-TYPE DYNAMICS OF A NEURAL FIELD - Efficiently simulating an Amari dynamics of a neural field (a), the Amari dynamics being specified by the equation (1) where a(x,t) is the state of the neural field (a), represented in a spatial domain (SR) using coordinates x,t, i(x,i) is a function stating the input to the neural field at time t, f[.] is a bounded monotonic transfer function having values between 0 and 1, F(x) is an interaction kernel, s specifies the time scale on which the neural field (a) changes and h is a constant specifying the global excitation or inhibition of the neural field (a). A method for simulating an Amari dynamics of a neural field (a), comprising the step of simulating an application of the transfer function (f) to the neural field (a). According to the invention, the step of simulating an application of the transfer function (f) comprises smoothing the neural field (a) by applying a smoothing operator (S). | 08-19-2010 |
| 20110270790 | NOISE CLEANUP - Systems, methods, and computer program products are provided to provide noise reduction for an input signal using a neural network. A feed-forward set of neuron groups is provided to enhance neuron activity within a particular frequency band based on prior reception of activity within that frequency band, and also to attenuate surrounding frequency bands. A surround-inhibition set of neuron groups further attenuates activity surrounding the stimulated frequency band. | 11-03-2011 |
| 20140180990 | Activity-Dependent Generation of Simulated Neural Circuits - A simulated neural circuit includes a plurality of simulated neurons. The simulated neurons have input branches that are configured to connect to a plurality of inputs and activate in response to activity in the inputs to which they are connected. In addition, the simulated neurons are configured to activate in response to activity in their input branches. Initial connections are formed between various input branches and various inputs and a set of the inputs are activated. Thereafter, the stability of connections between input branches and inputs to which they are connected is moderated based on the activated set of inputs and a pattern of activity generated in the input branches and simulated neurons in response to the activated set of inputs. | 06-26-2014 |
| 20140201118 | METHOD FOR THE COMPUTER-ASSISTED MODELING OF A TECHNICAL SYSTEM - Disclosed is a method for the computer-assisted modeling of a technical system. One or more output vectors are modeled dependent on one or more input vectors by the learning process of a neural network on the basis of training data of known input vectors and output vectors. Each output vector comprises one or more operating variables of the technical system, and each input vector comprises one or more input variables that influence the operating variable(s). The neural network is a feedforward network with an input layer, a plurality of hidden layers, and an output layer. The output layer comprises a plurality of output clusters, each of which consists of one or more output neurons, the plurality of output clusters corresponding to the plurality of hidden layers. Each output cluster describes the same output vector and is connected to another hidden layer. | 07-17-2014 |
| 20140214739 | CORTICAL SIMULATOR - Embodiments of the invention relate to a function-level simulator for modeling a neurosynaptic chip. One embodiment comprises simulating a neural network using an object-oriented framework including a plurality of object-oriented classes. Each class corresponds to a component of a neural network. Running a simulation model of the neural network includes instantiating multiple simulation objects from the classes. Each simulation object is an instance of one of the classes. | 07-31-2014 |
| 20150058269 | ARTIFICIAL NEURAL CIRCUIT FORMING RE-ACTIVATIBLE FUNCTIONAL LINK BETWEEN THE POSTSYNAPTIC TERMINALS OF TWO SYNAPSES - An electronic neuronal circuit system to model the interaction between the postsynaptic terminal of a first synapse between two neurons and the postsynaptic terminal of a second synapse between two neurons includes comparators to model the presynaptic neurons of the synapses, plurality of three diodes connected to the comparators to model synapses, an AND gate and latch to model the formation of functional link between the postsynaptic terminals, and timer-controlled latches for controlling the life-span of the inter-postsynaptic functional link, durations of re-activation of inter-postsynaptic functional link and flow of activity through the output postsynaptic dendritic terminals. | 02-26-2015 |
| 20150106317 | SHARED MEMORY ARCHITECTURE FOR A NEURAL SIMULATOR - Aspects of the present disclosure provide methods and apparatus for allocating memory in an artificial nervous system simulator implemented in hardware. According to certain aspects, memory resource requirements for one or more components of an artificial nervous system being simulated may be determined and portions of a shared memory pool (which may include on-chip and/or off-chip RAM) may be allocated to the components based on the determination. | 04-16-2015 |
| 20150120632 | EVALUATION OF A SYSTEM INCLUDING SEPARABLE SUB-SYSTEMS OVER A MULTIDIMENSIONAL RANGE - An artificial neural network may be configured to test the impact of certain input parameters. To improve testing efficiency and to avoid test runs that may not alter system performance, the effect of input parameters on neurons or groups of neurons may be determined to classify the neurons into groups based on the impact of certain parameters on those groups. Groups may be ordered serially and/or in parallel based on the interconnected nature of the groups and whether the output of neurons in one group may affect the operation of another. Parameters not affecting group performance may be pruned as inputs to that particular group prior to running system tests, thereby conserving processing resources during testing. | 04-30-2015 |
