Abstract: The embodiments disclosed herein relate to chips used to receive and process neurological events in brain matter as captured by electrodes. Such chips may include an array of amplifiers and electrodes to receive neurological voltage signals, the chip including a config circuitry in communication with the array of amplifiers and a controller, the config circuitry configured to receive program instructions and instruct the amplifiers of a voltage threshold and instruct the controller to pass on signals from only specific rows and columns of amplifiers, the controller in communication with the array of amplifiers, the controller configured to packetize the neurological voltage signals into data packets.

Abstract: Methods involving interpreting signals from a brain-machine interface (BMI) are described, as well as methods involving adjusting an implanted or wearable BMI device. The method includes receiving neural signals from a brain of a subject into a BMI decoder. The method includes determining an activity change of the subject based on a sensor. The method includes routing the neural signals from a first model to a second model in the BMI decoder based on the determined activity change. The method includes translating, using the second model in the BMI decoder, the neural signals into a command. The method includes sending the command to a controller.

Abstract: An event-driven neural network including a plurality of interconnected core circuits is provided. Each core circuit includes an electronic synapse array that 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.

Abstract: 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.

Abstract: 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.

Abstract: The embodiments disclosed herein relate to chips used to receive and process neurological events in brain matter as captured by electrodes. Such chips may include an array of amplifiers and electrodes to receive neurological voltage signals, the chip including a config circuitry in communication with the array of amplifiers and a controller, the config circuitry configured to receive program instructions and instruct the amplifiers of a voltage threshold and instruct the controller to pass on signals from only specific rows and columns of amplifiers, the controller in communication with the array of amplifiers, the controller configured to packetize the neurological voltage signals into data packets.

Abstract: Embodiments of the invention relate to a scalable neural hardware for the noisy-OR model of Bayesian networks. One embodiment comprises a neural core circuit including a pseudo-random number generator for generating random numbers. The neural core circuit further comprises a plurality of incoming electronic axons, a plurality of neural modules, and a plurality of electronic synapses interconnecting the axons to the neural modules. Each synapse interconnects an axon with a neural module. Each neural module receives incoming spikes from interconnected axons. Each neural module represents a noisy-OR gate. Each neural module spikes probabilistically based on at least one random number generated by the pseudo-random number generator unit.

Abstract: The embodiments disclosed herein relate to chips used to receive and process neurological events in brain matter as captured by electrodes. Such chips may include an array of amplifiers and electrodes to receive neurological voltage signals, the chip including a config circuitry in communication with the array of amplifiers and a controller, the config circuitry configured to receive program instructions and instruct the amplifiers of a voltage threshold and instruct the controller to pass on signals from only specific rows and columns of amplifiers, the controller in communication with the array of amplifiers, the controller configured to packetize the neurological voltage signals into data packets.

Abstract: Embodiments of the invention provide a neurosynaptic network circuit comprising multiple neurosynaptic devices including a plurality of neurosynaptic core circuits for processing one or more data packets. The neurosynaptic devices further include a routing system for routing the data packets between the core circuits. At least one of the neurosynaptic devices is faulty. The routing system is configured for selectively bypassing each faulty neurosynaptic device when processing and routing the data packets.

Abstract: Embodiments of the invention relate to providing transposable access to a synapse array using a recursive array layout. One embodiment comprises maintaining synaptic weights for multiple synapses connecting multiple axons and multiple neurons, wherein the synaptic weights are maintained based on a recursive array layout. The recursive array layout facilitates transposable access to the synaptic weights. A neuronal spike event between an axon and a neuron is communicated via a corresponding connecting synapse by accessing the synaptic weight of the corresponding connecting synapse in the recursive array layout.

Abstract: The present invention provides a system comprising multiple core circuits. Each core circuit comprises multiple electronic axons for receiving event packets, multiple electronic neurons for generating event packets, and a fanout crossbar including multiple electronic synapse devices for interconnecting the neurons with the axons. The system further comprises a routing system for routing event packets between the core circuits. The routing system virtually connects each neuron with one or more programmable target axons for the neuron by routing each event packet generated by the neuron to the target axons. Each target axon for each neuron of each core circuit is an axon located on the same core circuit as, or a different core circuit than, the neuron.

Abstract: The present invention provides a system comprising multiple core circuits. Each core circuit comprises multiple electronic axons for receiving event packets, multiple electronic neurons for generating event packets, and a fanout crossbar including multiple electronic synapse devices for interconnecting the neurons with the axons. The system further comprises a routing system for routing event packets between the core circuits. The routing system virtually connects each neuron with one or more programmable target axons for the neuron by routing each event packet generated by the neuron to the target axons. Each target axon for each neuron of each core circuit is an axon located on the same core circuit as, or a different core circuit than, the neuron.

Abstract: A multiplexed neural core circuit according to one embodiment comprises, for an integer multiplexing factor T that is greater than zero, T sets of electronic neurons, T sets of electronic axons, where each set of the T sets of electronic axons corresponds to one of the T sets of electronic neurons, and a synaptic interconnection network comprising a plurality of electronic synapses that each interconnect a single electronic axon to a single electronic neuron, where the interconnection network interconnects each set of the T sets of electronic axons to its corresponding set of electronic neurons.

Abstract: Embodiments of the invention provide a system and circuit interconnecting peripheral devices to neurosynaptic core circuits. The neurosynaptic system includes an interconnect that includes different types of communication channels. A device connects to the neurosynaptic system via the interconnect.

Abstract: Embodiments of the invention provide a method for mapping a bipartite graph onto a neuromorphic architecture comprising of a plurality of interconnected neuromorphic core circuits. The graph includes a set of source nodes and a set of target nodes. The method comprises, for each source node, creating a corresponding splitter construct configured to duplicate input. Each splitter construct comprises a first portion of a core circuit. The method further comprises, for each target node, creating a corresponding merger construct configured to combine input. Each merger construct comprises a second portion of a core circuit. Source nodes and target nodes are connected based on a permutation of an interconnect network interconnecting the core circuits.

Abstract: 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.

Abstract: The embodiments disclosed herein relate to chips used to receive and process neurological events in brain matter as captured by electrodes. Such chips may include an array of amplifiers and electrodes to receive neurological voltage signals, the chip including a config circuitry in communication with the array of amplifiers and a controller, the config circuitry configured to receive program instructions and instruct the amplifiers of a voltage threshold and instruct the controller to pass on signals from only specific rows and columns of amplifiers, the controller in communication with the array of amplifiers, the controller configured to packetize the neurological voltage signals into data packets.

Abstract: A method is described for real-time detecting and classifying of a characteristic signal, such as a neural spike, and forwarding information for further processing if it meets certain criteria. A system (e.g., an on-chip system implanted in a subject's cranium with limited processing power) receives an electrical biological signal. The system filters the signal to generate a filtered signal and fits the filtered signal to a model. The system identifies a set of fit values based on the model, the set of fit values comprising a plurality of sample amplitude values and a respective plurality of time values. Based on the fit values, the system computes a set of characteristic values. The system compares the characteristic values to a corresponding set of threshold values. Based on the comparison, the system determines whether the received biological signal corresponds to a neural spike and, if a spike is detected, forwards on information.

Abstract: Embodiments of the invention relate to a globally asynchronous and locally synchronous neuromorphic network. One embodiment comprises generating a synchronization signal that is distributed to a plurality of neural core circuits. In response to the synchronization signal, in at least one core circuit, incoming spike events maintained by said at least one core circuit are processed to generate an outgoing spike event. Spike events are asynchronously communicated between the core circuits via a routing fabric comprising multiple asynchronous routers.

Abstract: Embodiments of the invention relate to a globally asynchronous and locally synchronous neuromorphic network. One embodiment comprises generating a synchronization signal that is distributed to a plurality of neural core circuits. In response to the synchronization signal, in at least one core circuit, incoming spike events maintained by said at least one core circuit are processed to generate an outgoing spike event. Spike events are asynchronously communicated between the core circuits via a routing fabric comprising multiple asynchronous routers.