Patent application number | Description | Published |
20100220523 | STOCHASTIC SYNAPSE MEMORY ELEMENT WITH SPIKE-TIMING DEPENDENT PLASTICITY (STDP) - An active memory element is provided. One embodiment of the invention includes a bi-polar memory two-terminal element having polarity-dependent switching. A probability of switching of the bi-polar memory element between a first state and a second state decays exponentially based on time delay and a difference between received signals at the two terminals and a switching threshold magnitude. | 09-02-2010 |
20100223220 | ELECTRONIC SYNAPSE - An electronic synapse device is provided. One embodiment of the invention includes a metastable switching synaptic device. Changing conductance of the metastable switching synaptic device occurs by receiving opposite signed first and second voltage pulses at the metastable switching synaptic device where magnitude of the first voltage pulse and the second voltage pulse each are below a switching voltage magnitude threshold. A magnitude difference between the first voltage pulse and the second voltage pulse exceeds the switching voltage magnitude threshold by an amount, wherein the amount is a function of a relative timing between the first voltage pulse and the second voltage pulse. | 09-02-2010 |
20100299296 | ELECTRONIC LEARNING SYNAPSE WITH SPIKE-TIMING DEPENDENT PLASTICITY USING UNIPOLAR MEMORY-SWITCHING ELEMENTS - According to embodiments of the invention, a system, method and computer program product producing spike-dependent plasticity in an artificial synapse. In an embodiment, a method includes: receiving a pre-synaptic spike in an electronic component; receiving a post-synaptic spike in the electronic component; in response to the pre-synaptic spike, generating a pre-synaptic pulse that occurs a predetermined period of time after the received pre-synaptic spike; in response to the post-synaptic spike, generating a post-synaptic pulse that starts at a baseline value and reaches a first voltage value a first period of time after the post-synaptic spike, followed by a second voltage value a second period of time after the post synaptic spike, followed by a return to the baseline voltage a third period of time after the post-synaptic spike; applying the generated pre-synaptic pulse to a pre-synaptic node of a synaptic device that includes a uni-polar, two-terminal bi-stable device in series with a rectifying element; and applying the generated post-synaptic pulse to a post-synaptic node of the synaptic device, wherein the synaptic device changes from a first conductive state to a second conductive state based on the value of input voltage applied to its pre and post-synaptic nodes, wherein the resultant state of the conductance of the synaptic device after the pre- and post-synaptic pulses are applied thereto depends on the relative timing of the received pre-synaptic spike with respect to the post synaptic spike. | 11-25-2010 |
20110119214 | AREA EFFICIENT NEUROMORPHIC CIRCUITS - A neuromorphic circuit includes a first field effect transistor in a first diode configuration establishing an electrical connection between a first gate and a first drain of the first field effect transistor. The neuromorphic circuit also includes a second field effect transistor in a second diode configuration establishing an electrical connection between a second gate and a second drain of the second field effect transistor. The neuromorphic circuit further includes variable resistance material electrically connected to both the first drain and the second drain, where the variable resistance material provides a programmable resistance value. The neuromorphic circuit additionally includes a first junction electrically connected to the variable resistance material and providing a first connection point to an output of a neuron circuit, and a second junction electrically connected to the variable resistance material and providing a second connection point to the output of the neuron circuit. | 05-19-2011 |
20110182349 | ADAPTIVE AND INTEGRATED VISUALIZATION OF SPATIOTEMPORAL DATA FROM LARGE-SCALE SIMULATIONS - Adaptive and integrated visualization of spatiotemporal data from large-scale simulation, is provided. A simulation is performed utilizing a simulator comprising multiple processors, generating spatiotemporal data samples from the simulation. Each data sample has spatial coordinates with a time stamp at a specific time resolution, and a tag. The data samples are assembled into data streams based on at least one of a spatial relationship and the corresponding tag. Each data stream is encoded into multiple formats, and an integrated and adaptive visualization of the data streams is displayed, wherein various data streams are simultaneously and synchronously displayed. | 07-28-2011 |
20120084240 | PHASE CHANGE MEMORY SYNAPTRONIC CIRCUIT FOR SPIKING COMPUTATION, ASSOCIATION AND RECALL - Embodiments of the invention are directed to producing spike-timing dependent plasticity using electronic neurons for computation, and pattern matching tasks such as association and recall. In response to an electronic neuron spiking, a spiking signal is sent from the electronic neuron to each axon driver and each dendrite driver connected to the spiking electronic neuron. Each axon driver receiving the spiking signal sends an axonal signal from the axon driver to a variable state resistor. Each dendrite driver receiving the spiking signal sends a dendritic signal from the dendrite driver to the variable state resistor, wherein the variable state resistor couples the axon driver and the dendrite driver. The combination of the axonal and dendritic signals is capable of increasing or decreasing conductance of the variable state resistor. | 04-05-2012 |
20120084241 | PRODUCING SPIKE-TIMING DEPENDENT PLASTICITY IN A NEUROMORPHIC NETWORK UTILIZING PHASE CHANGE SYNAPTIC DEVICES - Embodiments of the invention relate to a neuromorphic network for producing spike-timing dependent plasticity. The neuromorphic network includes a plurality of electronic neurons and an interconnect circuit coupled for interconnecting the plurality of electronic neurons. The interconnect circuit includes plural synaptic devices for interconnecting the electronic neurons via axon paths, dendrite paths and membrane paths. Each synaptic device includes a variable state resistor and a transistor device with a gate terminal, a source terminal and a drain terminal, wherein the drain terminal is connected in series with a first terminal of the variable state resistor. The source terminal of the transistor device is connected to an axon path, the gate terminal of the transistor device is connected to a membrane path and a second terminal of the variable state resistor is connected to a dendrite path, such that each synaptic device is coupled between a first axon path and a first dendrite path, and between a first membrane path and said first dendrite path. | 04-05-2012 |
20120109863 | CANONICAL SPIKING NEURON NETWORK FOR SPATIOTEMPORAL ASSOCIATIVE MEMORY - Embodiments of the invention relate to canonical spiking neurons for spatiotemporal associative memory. An aspect of the invention provides a spatiotemporal associative memory including a plurality of electronic neurons having a layered neural net relationship with directional synaptic connectivity. The plurality of electronic neurons configured to detect the presence of a spatiotemporal pattern in a real-time data stream, and extract the spatiotemporal pattern. The plurality of electronic neurons are further configured to, based on learning rules, store the spatiotemporal pattern in the plurality of electronic neurons, and upon being presented with a version of the spatiotemporal pattern, retrieve the stored spatiotemporal pattern. | 05-03-2012 |
20120109864 | 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 simulation. One embodiment includes simulating a spiking neural network to generate synaptic weights learned via the simulation while maintaining one-to-one correspondence between the simulation and a digital circuit chip. The learned synaptic weights are loaded into the digital circuit chip implementing a spiking neural network, the digital circuit chip comprising a neuromorphic-synaptronic spiking neural network including plural synapse devices interconnecting multiple digital neurons. | 05-03-2012 |
20120109866 | COMPACT COGNITIVE SYNAPTIC COMPUTING CIRCUITS - Embodiments of the invention relate to producing spike-timing dependent plasticity using electronic neurons interconnected in a crossbar array network. The crossbar array network comprises a plurality of crossbar arrays. Each crossbar array comprises a plurality of axons and a plurality of dendrites such that the axons and dendrites are transverse to one another, and multiple synapse devices, wherein each synapse device is at a cross-point junction of the crossbar array coupled between a dendrite and an axon. The crossbar arrays are spatially in a staggered pattern providing a staggered crossbar layout of the synapse devices. | 05-03-2012 |
20120150781 | INTEGRATE AND FIRE ELECTRONIC NEURONS - An integrate and fire electronic neuron is disclosed. Upon receiving an external spike signal, a digital membrane potential of the electronic neuron is updated based on the external spike signal. The electric potential of the membrane is decayed based on a leak rate. Upon the electric potential of the membrane exceeding a threshold, a spike signal is generated. | 06-14-2012 |
20120173471 | SYNAPTIC WEIGHT NORMALIZED SPIKING NEURONAL NETWORKS - Neuronal networks of electronic neurons interconnected via electronic synapses with synaptic weight normalization. The synaptic weights are based on learning rules for the neuronal network, such that a synaptic weight for a synapse determines the effect of a spiking source neuron on a target neuron connected via the synapse. Each synaptic weight is maintained within a predetermined range by performing synaptic weight normalization for neural network stability. | 07-05-2012 |
20120259804 | RECONFIGURABLE AND CUSTOMIZABLE GENERAL-PURPOSE CIRCUITS FOR NEURAL NETWORKS - A reconfigurable neural network circuit is provided. The reconfigurable neural network circuit comprises an electronic synapse array including multiple synapses interconnecting a plurality of digital electronic neurons. Each neuron comprises an integrator that integrates input spikes and generates a signal when the integrated inputs exceed a threshold. The circuit further comprises a control module for reconfiguring the synapse array. The control module comprises a global final state machine that controls timing for operation of the circuit, and a priority encoder that allows spiking neurons to sequentially access the synapse array. | 10-11-2012 |
20120265719 | ELECTRONIC LEARNING SYNAPSE WITH SPIKE-TIMING DEPENDENT PLASTICITY USING MEMORY-SWITCHING ELEMENTS - A system, method and computer program product produce spike-dependent plasticity in an artificial synapse. A method includes: an electronic device generating a pre-synaptic pulse that occurs a predetermined period of time after receiving a pre-synaptic spike at a first input. The electronic device generating a post-synaptic pulse that starts at a baseline value and reaches a first voltage value a first period of time after receiving a post-synaptic spike at a second input, followed by a second voltage value a second period of time after the post synaptic spike, followed by a return to said baseline voltage a third period of time after the post-synaptic spike. The generated pre-synaptic pulse is applied to a pre-synaptic node of a synaptic device in series with a rectifying element that has a turn-on voltage based on a threshold. The generated post-synaptic pulse is applied to a post-synaptic node of said synaptic device. | 10-18-2012 |
20120284217 | AREA EFFICIENT NEUROMORPHIC SYSTEM - A neuromorphic system includes a plurality of synapse blocks electrically connected to a plurality of neuron circuit blocks. The plurality of synapse blocks includes a plurality of neuromorphic circuits. Each neuromorphic circuit includes a field effect transistor in a diode configuration electrically connected to variable resistance material, where the variable resistance material provides a programmable resistance value. Each neuromorphic circuit also includes a first junction electrically connected to the variable resistance material and an output of one or more of the neuron circuit blocks, and a second junction electrically connected to the field effect transistor and an input of one or more of the neuron circuit blocks. | 11-08-2012 |
20120317062 | RECONFIGURABLE AND CUSTOMIZABLE GENERAL-PURPOSE CIRCUITS FOR NEURAL NETWORKS - A reconfigurable neural network circuit is provided. The reconfigurable neural network circuit comprises an electronic synapse array including multiple synapses interconnecting a plurality of digital electronic neurons. Each neuron comprises an integrator that integrates input spikes and generates a signal when the integrated inputs exceed a threshold. The circuit further comprises a control module for reconfiguring the synapse array. The control module comprises a global final state machine that controls timing for operation of the circuit, and a priority encoder that allows spiking neurons to sequentially access the synapse array. | 12-13-2012 |
20120330872 | CANONICAL SPIKING NEURON NETWORK FOR SPATIOTEMPORAL ASSOCIATIVE MEMORY - Embodiments of the invention relate to canonical spiking neurons for spatiotemporal associative memory. An aspect of the invention provides a spatiotemporal associative memory including a plurality of electronic neurons having a layered neural net relationship with directional synaptic connectivity. The plurality of electronic neurons configured to detect the presence of a spatiotemporal pattern in a real-time data stream, and extract the spatiotemporal pattern. The plurality of electronic neurons are further configured to, based on learning rules, store the spatiotemporal pattern in the plurality of electronic neurons, and upon being presented with a version of the spatiotemporal pattern, retrieve the stored spatiotemporal pattern. | 12-27-2012 |
20130031040 | HIERARCHICAL ROUTING FOR TWO-WAY INFORMATION FLOW AND STRUCTURAL PLASTICITY IN NEURAL NETWORKS - Hierarchical routing for two-way information flow and structural plasticity in a neural network is provided. In one embodiment the network includes multiple core modules, wherein each core module has a plurality of incoming connections with predetermined addresses. Each core module also has a plurality of outgoing connections such that each outgoing connection targets an incoming connection in a core module among the multiple core modules. The network also has a routing system that selectively routes signals among the core modules based on a reconfigurable hierarchical organization of the core modules. The network approximates a fully connected network such that each outgoing connection on any core module can target and reach any incoming connection on any core module without requiring a fully connected network. The routing system provides two-way information flow between neurons utilizing hierarchical routing. | 01-31-2013 |
20130073493 | 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 multiple digital neurons such that each neuron in a neural module has a corresponding neuron in another neural module. An interconnection network comprising a plurality of edges interconnects the plurality of neural modules. Each edge interconnects a first neural module to a second neural module, and each edge comprises a weighted synaptic connection between every neuron in the first neural module and a corresponding neuron in the second neural module. | 03-21-2013 |
20130073494 | EVENT-DRIVEN UNIVERSAL NEURAL NETWORK CIRCUIT - The present invention provides an event-driven universal neural network circuit. The circuit comprises a plurality of neural modules. Each neural module comprises multiple digital neurons such that each neuron in a neural module has a corresponding neuron in another neural module. An interconnection network comprising a plurality of digital synapses interconnects the neural modules. Each synapse interconnects a first neural module to a second neural module by interconnecting a neuron in the first neural module to a corresponding neuron in the second neural module. Corresponding neurons in the first neural module and the second neural module communicate via the synapses. Each synapse comprises a learning rule associating a neuron in the first neural module with a corresponding neuron in the second neural module. A control module generates signals which define a set of time steps for event-driven operation of the neurons and event communication via the interconnection network. | 03-21-2013 |
20130073497 | NEUROMORPHIC EVENT-DRIVEN NEURAL COMPUTING ARCHITECTURE IN A SCALABLE NEURAL NETWORK - An event-driven neural network includes a plurality of interconnected core circuits is provided. Each core circuit includes an electronic synapse array has multiple digital synapses interconnecting a plurality of digital electronic neurons. A synapse interconnects an axon of a pre-synaptic neuron with a dendrite of a post-synaptic neuron. A neuron integrates input spikes and generates a spike event in response to the integrated input spikes exceeding a threshold. Each core circuit also has a scheduler that receives a spike event and delivers the spike event to a selected axon in the synapse array based on a schedule for deterministic event delivery. | 03-21-2013 |
20130134546 | HIGH DENSITY MULTI-ELECTRODE ARRAY - A method includes forming one or more trenches in a substrate; lining the one or more trenches with a dielectric liner; filling the one or more trenches with a conductive electrode to form one or more trench electrodes; forming a transistor layer on the substrate; connecting each of the one or more trench electrodes to at least one access transistor in the transistor layer; and thinning the substrate to expose at least a portion of each of the trench electrodes. | 05-30-2013 |
20130159229 | 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 lattice for cross-associating signaling between the neurons in different modalities. The interconnection lattice includes a plurality of perception neuron populations along a number of bottom-up signaling pathways, and a plurality of action neuron populations along a number of top-down signaling pathways. Each perception neuron along a bottom-up signaling pathway has a corresponding action neuron along a reciprocal top-down signaling pathway. An input neuron population configured to receive sensory input drives perception neurons along a number of bottom-up signaling pathways. A first set of perception neurons along bottom-up signaling pathways drive a first set of action neurons along top-down signaling pathways. Action neurons along a number of top-down signaling pathways drive an output neuron population configured to generate motor output. | 06-20-2013 |
20130159231 | 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 lattice for cross-associating signaling between the neurons in different modalities. The interconnection lattice includes a plurality of perception neuron populations along a number of bottom-up signaling pathways, and a plurality of action neuron populations along a number of top-down signaling pathways. Each perception neuron along a bottom-up signaling pathway has a corresponding action neuron along a reciprocal top-down signaling pathway. An input neuron population configured to receive sensory input drives perception neurons along a number of bottom-up signaling pathways. A first set of perception neurons along bottom-up signaling pathways drive a first set of action neurons along top-down signaling pathways. Action neurons along a number of top-down signaling pathways drive an output neuron population configured to generate motor output. | 06-20-2013 |
20130187253 | HIGH DENSITY MULTI-ELECTRODE ARRAY - A high density micro-electrode array includes a transistor layer including a plurality of access transistors and a substrate in operable communication with the transistor layer including, wherein at least a portion of the substrate includes a plurality of trenches. The system includes a plurality of electrodes at least partially located in the plurality of trenches, wherein each of the plurality of electrodes is connected to at least one of the plurality of access transistors and wherein each of the electrodes is separated by a distance less than approximately one microns. | 07-25-2013 |
20130198121 | MULTI-COMPARTMENT NEURON SUITABLE FOR IMPLEMENTATION IN A DISTRIBUTED HARDWARE MODEL BY REDUCING COMMUNICATION BANDWIDTH - Embodiments of the present invention provide a neural module comprising a multilevel hierarchical structure of neural compartments. Each neural compartment is interconnected to one or more neural compartments of a previous level and a next hierarchical level in the hierarchical structure. Each neural compartment integrates spike signals from interconnected neural compartments of a previous hierarchical level, generates a spike signal in response to the integrated spike signals reaching a threshold of said neural compartment, and delivers a generated spike signal to interconnected neural compartments of a next hierarchical level. Each neural compartment is further interconnected to one or more external spiking systems, such that said neural compartment integrates spike signals from interconnected external spiking systems, and delivers a generated spike signal to interconnected external spiking systems. The neural compartments of a neural module include one soma compartment and a plurality of dendrite compartments. Each neural compartment is excitatory or inhibitory. | 08-01-2013 |
20130339281 | MULTI-PROCESSOR CORTICAL SIMULATIONS WITH RECIPROCAL CONNECTIONS WITH SHARED WEIGHTS - Embodiments of the invention relate to distributed simulation frameworks that provide reciprocal communication. One embodiment comprises interconnecting neuron groups on different processors via a plurality of reciprocal communication pathways, and facilitating the exchange of reciprocal spiking communication between two different processors using at least one Ineuron module. Each processor includes at least one neuron group. Each neuron group includes at least one electronic neuron. | 12-19-2013 |
20140019393 | UNIVERSAL, ONLINE LEARNING IN MULTI-MODAL PERCEPTION-ACTION SEMILATTICES - In one embodiment, the present invention provides a method for interconnecting neurons in a neural network. At least one node among a first set of nodes is interconnected to at least one node among a second set of nodes, and nodes of the first and second set are arranged in a lattice. At least one node of the first set represents a sensory-motor modality of the neural network. At least one node of the second set is a union of at least two nodes of the first set. Each node in the lattice has an acyclic digraph comprising multiple vertices and directed edges. Each vertex represents a neuron population. Each directed edge comprises multiple synaptic connections. Vertices in different acyclic digraphs are interconnected using an acyclic bottom-up digraph. The bottom-up digraph has a corresponding acyclic top-down digraph. Vertices in the bottom-up digraph are interconnected to vertices in the top-down digraph. | 01-16-2014 |
20140032464 | MULTI-COMPARTMENT NEURONS WITH NEURAL CORES - Embodiments of the invention provide a neural core circuit comprising a synaptic interconnect network including plural electronic synapses for interconnecting one or more source electronic neurons with one or more target electronic neurons. The interconnect network further includes multiple axon paths and multiple dendrite paths. Each synapse is at a cross-point junction of the interconnect network between a dendrite path and an axon path. The core circuit further comprises a routing module maintaining routing information. The routing module routes output from a source electronic neuron to one or more selected axon paths. Each synapse provides a configurable level of signal conduction from an axon path of a source electronic neuron to a dendrite path of a target electronic neuron. | 01-30-2014 |
20140032465 | SYNAPTIC, DENDRITIC, SOMATIC, AND AXONAL PLASTICITY IN A NETWORK OF NEURAL CORES USING A PLASTIC MULTI-STAGE CROSSBAR SWITCHING - Embodiments of the invention provide a neural network comprising multiple functional neural core circuits, and a dynamically reconfigurable switch interconnect between the functional neural core circuits. The interconnect comprises multiple connectivity neural core circuits. Each functional neural core circuit comprises a first and a second core module. Each core module comprises a plurality of electronic neurons, a plurality of incoming electronic axons, and multiple electronic synapses interconnecting the incoming axons to the neurons. Each neuron has a corresponding outgoing electronic axon. In one embodiment, zero or more sets of connectivity neural core circuits interconnect outgoing axons in a functional neural core circuit to incoming axons in the same functional neural core circuit. In another embodiment, zero or more sets of connectivity neural core circuits interconnect outgoing and incoming axons in a functional neural core circuit to incoming and outgoing axons in a different functional neural core circuit, respectively. | 01-30-2014 |
20140081893 | STRUCTURAL PLASTICITY IN SPIKING NEURAL NETWORKS WITH SYMMETRIC DUAL OF AN ELECTRONIC NEURON - A neural system comprises multiple neurons interconnected via synapse devices. Each neuron integrates input signals arriving on its dendrite, generates a spike in response to the integrated input signals exceeding a threshold, and sends the spike to the interconnected neurons via its axon. The system further includes multiple noruens, each noruen is interconnected via the interconnect network with those neurons that the noruen's corresponding neuron sends its axon to. Each noruen integrates input spikes from connected spiking neurons and generates a spike in response to the integrated input spikes exceeding a threshold. There can be one noruen for every corresponding neuron. For a first neuron connected via its axon via a synapse to dendrite of a second neuron, a noruen corresponding to the second neuron is connected via its axon through the same synapse to dendrite of the noruen corresponding to the first neuron. | 03-20-2014 |
20140092728 | FAULTY CORE RECOVERY MECHANISMS FOR A THREE-DIMENSIONAL NETWORK ON A PROCESSOR ARRAY - Embodiments of the invention relate to faulty recovery mechanisms for a three-dimensional (3-D) network on a processor array. One embodiment comprises a multidimensional switch network for a processor array. The switch network comprises multiple switches for routing packets between multiple core circuits of the processor array. The switches are organized into multiple planes. The switch network further comprises a redundant plane including multiple redundant switches. Multiple data paths interconnect the switches. The redundant plane is used to facilitate full operation of the processor array in the event of one or more component failures. | 04-03-2014 |
20140095923 | FINAL FAULTY CORE RECOVERY MECHANISMS FOR A TWO-DIMENSIONAL NETWORK ON A PROCESSOR ARRAY - Embodiments of the invention relate to faulty recovery mechanisms for a two-dimensional (2-D) network on a processor array. One embodiment comprises a processor array including multiple processors core circuits, and a redundant routing system for routing packets between the core circuits. The redundant routing system comprises multiple switches, wherein each switch corresponds to one or more core circuits of the processor array. The redundant routing system further comprises multiple data paths interconnecting the switches, and a controller for selecting one or more data paths. Each selected data path is used to bypass at least one component failure of the processor array to facilitate full operation of the processor array. | 04-03-2014 |
20140114893 | 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 neuron with a dendrite of a post-synaptic neuron. Each neuron integrates input spikes and generates a spike event in response to the integrated input spikes exceeding a threshold. A decoder receives spike events sequentially and transmits the spike events to selected axons in the synapse array. An encoder transmits spike events corresponding to spiking neurons. A controller coordinates events from the synapse array to the neurons, and signals when neurons may compute their spike events within each time step, ensuring one-to-one correspondence with an equivalent software model. The synapse array includes an interconnecting crossbar that sequentially receives spike events from axons, wherein one axon at a time drives the crossbar, and the crossbar transmits synaptic events in parallel to multiple neurons. | 04-24-2014 |
20140180984 | TIME-DIVISION MULTIPLEXED NEUROSYNAPTIC MODULE WITH IMPLICIT MEMORY ADDRESSING FOR IMPLEMENTING A UNIVERSAL SUBSTRATE OF ADAPTATION - Embodiments of the invention relate to a time-division multiplexed neurosynaptic module with implicit memory addressing for implementing a universal substrate of adaptation. One embodiment comprises a neurosynaptic device including a memory device that maintains neuron attributes for multiple neurons. The module further includes multiple bit maps that maintain incoming firing events for different periods of delay and a multi-way processor. The processor includes a memory array that maintains a plurality of synaptic weights. The processor integrates incoming firing events in a time-division multiplexing manner. Incoming firing events are integrated based on the neuron attributes and the synaptic weights maintained. | 06-26-2014 |
20140180985 | MAPPING NEURAL DYNAMICS OF A NEURAL MODEL ON TO A COARSELY GRAINED LOOK-UP TABLE - Embodiments of the invention relate to mapping neural dynamics of a neural model on to a lookup table. One embodiment comprises defining a phase plane for a neural model. The phase plane represents neural dynamics of the neural model. The phase plane is coarsely sampled to obtain state transition information for multiple neuronal states. The state transition information is mapped on to a lookup table. | 06-26-2014 |
20140180987 | TIME-DIVISION MULTIPLEXED NEUROSYNAPTIC MODULE WITH IMPLICIT MEMORY ADDRESSING FOR IMPLEMENTING A NEURAL NETWORK - Embodiments of the invention relate to a time-division multiplexed neurosynaptic module with implicit memory addressing for implementing a neural network. One embodiment comprises maintaining neuron attributes for multiple neurons and maintaining incoming firing events for different time steps. For each time step, incoming firing events for said time step are integrated in a time-division multiplexing manner. Incoming firing events are integrated based on the neuron attributes maintained. For each time step, the neuron attributes maintained are updated in parallel based on the integrated incoming firing events for said time step. | 06-26-2014 |
20140180988 | HARDWARE ARCHITECTURE FOR SIMULATING A NEURAL NETWORK OF NEURONS - Embodiments of the invention relate to a neural network system for simulating neurons of a neural model. One embodiment comprises a memory device that maintains neuronal states for multiple neurons, a lookup table that maintains state transition information for multiple neuronal states, and a controller unit that manages the memory device. The controller unit updates a neuronal state for each neuron based on incoming spike events targeting said neuron and state transition information corresponding to said neuronal state. | 06-26-2014 |
20140188771 | 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 simulation. One embodiment includes simulating a spiking neural network to generate synaptic weights learned via the simulation while maintaining one-to-one correspondence between the simulation and a digital circuit chip. The learned synaptic weights are loaded into the digital circuit chip implementing a spiking neural network, the digital circuit chip comprising a neuromorphic-synaptronic spiking neural network including plural synapse devices interconnecting multiple digital neurons. | 07-03-2014 |
20140207719 | STRUCTURAL PLASTICITY IN SPIKING NEURAL NETWORKS WITH SYMMETRIC DUAL OF AN ELECTRONIC NEURON - A neural system comprises multiple neurons interconnected via synapse devices. Each neuron integrates input signals arriving on its dendrite, generates a spike in response to the integrated input signals exceeding a threshold, and sends the spike to the interconnected neurons via its axon. The system further includes multiple noruens, each noruen is interconnected via the interconnect network with those neurons that the noruen's corresponding neuron sends its axon to. Each noruen integrates input spikes from connected spiking neurons and generates a spike in response to the integrated input spikes exceeding a threshold. There can be one noruen for every corresponding neuron. For a first neuron connected via its axon via a synapse to dendrite of a second neuron, a noruen corresponding to the second neuron is connected via its axon through the same synapse to dendrite of the noruen corresponding to the first neuron. | 07-24-2014 |
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 |
20140222740 | CONSOLIDATING MULTIPLE NEUROSYNAPTIC CORES INTO ONE MEMORY - Embodiments of the invention relate to a neural network system comprising a single memory block for multiple neurosynaptic core modules. One embodiment comprises a neural network system including a memory array that maintains information for multiple neurosynaptic core modules. Each neurosynaptic core module comprises multiple neurons. The neural network system further comprises at least one logic circuit. Each logic circuit receives neuronal firing events targeting a neurosynaptic core module of the neural network system, and said logic circuit integrates the firing events received based on information maintained in said memory for said neurosynaptic core module. | 08-07-2014 |
20140244971 | ARRAY OF PROCESSOR CORE CIRCUITS WITH REVERSIBLE TIERS - Embodiments of the invention relate to an array of processor core circuits with reversible tiers. One embodiment comprises multiple tiers of core circuits and multiple switches for routing packets between the core circuits. Each tier comprises at least one core circuit. Each switch comprises multiple router channels for routing packets in different directions relative to the switch, and at least one routing circuit configured for reversing a logical direction of at least one router channel. | 08-28-2014 |
20140250038 | STRUCTURAL PLASTICITY IN SPIKING NEURAL NETWORKS WITH SYMMETRIC DUAL OF AN ELECTRONIC NEURON - A neural system comprises multiple neurons interconnected via synapse devices. Each neuron integrates input signals arriving on its dendrite, generates a spike in response to the integrated input signals exceeding a threshold, and sends the spike to the interconnected neurons via its axon. The system further includes multiple noruens, each noruen is interconnected via the interconnect network with those neurons that the noruen's corresponding neuron sends its axon to. Each noruen integrates input spikes from connected spiking neurons and generates a spike in response to the integrated input spikes exceeding a threshold. There can be one noruen for every corresponding neuron. For a first neuron connected via its axon via a synapse to dendrite of a second neuron, a noruen corresponding to the second neuron is connected via its axon through the same synapse to dendrite of the noruen corresponding to the first neuron. | 09-04-2014 |
20140250039 | 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 multiple digital neurons such that each neuron in a neural module has a corresponding neuron in another neural module. An interconnection network comprising a plurality of edges interconnects the plurality of neural modules. Each edge interconnects a first neural module to a second neural module, and each edge comprises a weighted synaptic connection between every neuron in the first neural module and a corresponding neuron in the second neural module. | 09-04-2014 |
20140258199 | STRUCTURAL DESCRIPTIONS FOR NEUROSYNAPTIC NETWORKS - Embodiments of the invention provide a method comprising creating a structural description for at least one neurosynaptic core circuit. Each core circuit comprises an interconnect network including plural electronic synapses for interconnecting one or more electronic neurons with one or more electronic axons. The structural description defines a desired neuronal activity for the core circuits. The desired neuronal activity is simulated by programming the core circuits with the structural description. The structural description controls routing of neuronal firing events for the core circuits. | 09-11-2014 |
20140310220 | ELECTRONIC SYNAPSES FOR REINFORCEMENT LEARNING - Embodiments of the invention provide electronic synapse devices for reinforcement learning. An electronic synapse is configured for interconnecting a pre-synaptic electronic neuron and a post-synaptic electronic neuron. The electronic synapse comprises memory elements configured for storing a state of the electronic synapse and storing meta information for updating the state of the electronic synapse. The electronic synapse further comprises an update module configured for updating the state of the electronic synapse based on the meta information in response to an update signal for reinforcement learning. The update module is configured for updating the state of the electronic synapse based on the meta information, in response to a delayed update signal for reinforcement learning based on a learning rule. | 10-16-2014 |
20140344201 | PROVIDING TRANSPOSABLE ACCESS TO A SYNAPSE ARRAY USING COLUMN AGGREGATION - Embodiments of the invention relate to providing transposable access to a synapse array using column aggregation. One embodiment comprises a neural network including a plurality of electronic axons, a plurality of electronic neurons, and a crossbar for interconnecting the axons with the neurons. The crossbar comprises a plurality of electronic synapses. Each synapse interconnects an axon with a neuron. The neural network further comprises a column aggregation module for transposable access to one or more synapses of the crossbar using column aggregation. | 11-20-2014 |
20140379625 | SPIKE TAGGING FOR DEBUGGING, QUERYING, AND CAUSAL ANALYSIS - Embodiments of the invention relate to spike tagging for a neural network. One embodiment comprises a neural network including multiple electronic neurons and a plurality of weighted synaptic connections interconnecting the neurons. An originating neuron of the neural network generates a spike event and a message tag that includes information relating to said originating neuron. A neuron of the neural network receives a spike event and a message tag from an interconnected neuron. In response to one or more received spike events, a receiving neuron spikes and sends a message tag selected from received message tags to an interconnected neuron. | 12-25-2014 |
20150039546 | DUAL DETERMINISTIC AND STOCHASTIC NEUROSYNAPTIC CORE CIRCUIT - One embodiment provides a system comprising a memory device for maintaining deterministic neural data relating to a digital neuron and a logic circuit for deterministic neural computation and stochastic neural computation. Deterministic neural computation comprises processing a neuronal state of the neuron based on the deterministic neural data maintained. Stochastic neural computation comprises generating stochastic neural data relating to the neuron and processing the neuronal state of the neuron based on the stochastic neural data generated. | 02-05-2015 |
20150058268 | HIERARCHICAL SCALABLE NEUROMORPHIC SYNAPTRONIC SYSTEM FOR SYNAPTIC AND STRUCTURAL PLASTICITY - In one embodiment, the present invention provides a neural network circuit comprising multiple symmetric core circuits. Each symmetric core circuit comprises a first core module and a second core module. Each core module comprises a plurality of electronic neurons, a plurality of electronic axons, and an interconnection network comprising multiple electronic synapses interconnecting the axons to the neurons. Each synapse interconnects an axon to a neuron. The first core module and the second core module are logically overlayed on one another such that neurons in the first core module are proximal to axons in the second core module, and axons in the first core module are proximal to neurons in the second core module. Each neuron in each core module receives axonal firing events via interconnected axons and generates a neuronal firing event according to a neuronal activation function. | 02-26-2015 |