Abstract: Certain aspects of the present disclosure provide methods and apparatus for learning or determining delays between neuron models so that the uncertainty in input spike timing is accounted for in the margin of time between a delayed pre-synaptic input spike and a post-synaptic spike. In this manner, a neural network can correctly match patterns (even in the presence of significant jitter) and correctly distinguish between different noisy patterns. One example method generally includes determining an uncertainty associated with a first pre-synaptic spike time of a first neuron model for a pattern to be learned; and determining a delay based on the uncertainty, such that the delay added to a second pre-synaptic spike time of the first neuron model results in a causal margin of time between the delayed second pre-synaptic spike time and a post-synaptic spike time of a second neuron model.

Abstract: Methods and apparatus are provided for effecting modulation using global scalar values in a spiking neural network. One example method for operating an artificial nervous system generally includes determining one or more updated values for artificial neuromodulators to be used by a plurality of entities in a neuron model and providing the updated values to the plurality of entities.

Abstract: Certain aspects of the present disclosure support a technique for robust neural temporal coding, learning and cell recruitments for memory using oscillations. Methods are proposed for distinguishing temporal patterns and, in contrast to other “temporal pattern” methods, not merely coincidence of inputs or order of inputs. Moreover, the present disclosure propose practical methods that are biologically-inspired/consistent but reduced in complexity and capable of coding, decoding, recognizing, and learning temporal spike signal patterns. In this disclosure, extensions are proposed to a scalable temporal neural model for robustness, confidence or integrity coding, and recruitment of cells for efficient temporal pattern memory.

Abstract: Certain aspects of the present disclosure relate to methods and apparatus for neuro-simulation with a single two-dimensional device to track objects. The neuro-simulation may report a point of interest in an image that is provided by the device. The device may center on the point of interest using one or more actuators. The simulation mechanism may input pixels and output a plurality of angles to the actuators to adjust their direction.

Abstract: Certain aspects of the present disclosure provide methods and apparatus for creating tags (static or dynamic) for input/output classes of a neural network model using supervised learning. The method includes augmenting a neural network model with a plurality of neurons and training the augmented network using spike timing dependent plasticity (STDP) to determine one or more tags.

Abstract: Certain aspects of the present disclosure support techniques for unsupervised neural replay, learning refinement, association and memory transfer.

Abstract: Certain aspects of the present disclosure provide methods and apparatus for generating neural adaptive behavior, which may be based on neuromodulator-mediated meta-plasticity and/or gain control. In this manner, flexible associations between sensory cues and motor actions are generated, which enable an agent to efficiently gather rewards in a changing environment. One example method generally includes receiving one or more input stimuli; processing the received input stimuli to generate an output signal, wherein the processing is modulated with a first neuromodulation signal generated by a gain control unit; controlling the gain control unit to switch between at least two different neural activity modes, wherein at least one of a level or timing of the first neuromodulation signal generated by the gain control unit is determined based on the neural activity modes; and sending the output signal to an output unit.

Abstract: Methods and apparatus are provided for implementing structural plasticity in an artificial nervous system. One example method for altering a structure of an artificial nervous system generally includes determining a synapse in the artificial nervous system for reassignment, determining a first artificial neuron and a second artificial neuron for connecting via the synapse, and reassigning the synapse to connect the first artificial neuron with the second artificial neuron. Another example method for operating an artificial nervous system, generally includes determining a synapse in the artificial nervous system for assignment; determining a first artificial neuron and a second artificial neuron for connecting via the synapse, wherein at least one of the synapse or the first and second artificial neurons are determined randomly or pseudo-randomly; and assigning the synapse to connect the first artificial neuron with the second artificial neuron.

Abstract: Methods and apparatus are provided for implementing behavioral homeostasis in artificial neurons that use a dynamical spiking neuron model. The homeostatic mechanism may be driven by neuron state, rather than by neuron spiking rate, and this mechanism may drive changes to the neuron temporal dynamics, rather than to contributions of input or weights. As a result, certain aspects of the present disclosure are a more natural fit with spiking neural networks and have many functional and computational advantages. One example method for implementing homeostasis of an artificial nervous system generally includes determining one or more state variables of a neuron model used by an artificial neuron, based at least in part on dynamics of the neuron model; determining one or more conditions based at least in part on the state variables; and adjusting the dynamics based at least in part on the conditions.

Abstract: Certain aspects of the present disclosure support a technique for neuronal firing modulation via noise control. Response curve of a typical neuron with a threshold can transition from not firing to always firing with a very small change in the neuron's input, thus limiting the range of excitable input patterns for the neuron. By introducing local, region and global noise terms, the slope of the neuron's response curve can be reduced. This may enable a larger set of input spike patterns to be effective in causing the neuron to fire, i.e., the neuron can be responsive to a large range of input patterns instead of an inherently small set of patterns in a noiseless situation.

Abstract: Certain aspects of the present disclosure provide methods and apparatus for generating neural adaptive behavior, which may be based on neuromodulator-mediated meta-plasticity and/or gain control. In this manner, flexible associations between sensory cues and motor actions are generated, which enable an agent to efficiently gather rewards in a changing environment. One example method generally includes receiving one or more input stimuli; processing the received input stimuli to generate an output signal, wherein the processing is modulated with a first neuromodulation signal generated by a gain control unit; controlling the gain control unit to switch between at least two different neural activity modes, wherein at least one of a level or timing of the first neuromodulation signal generated by the gain control unit is determined based on the neural activity modes; and sending the output signal to an output unit.

Abstract: Certain aspects of the present disclosure provide methods and apparatus for learning or determining delays between neuron models so that the uncertainty in input spike timing is accounted for in the margin of time between a delayed pre-synaptic input spike and a post-synaptic spike. In this manner, a neural network can correctly match patterns (even in the presence of significant jitter) and correctly distinguish between different noisy patterns. One example method generally includes determining an uncertainty associated with a first pre-synaptic spike time of a first neuron model for a pattern to be learned; and determining a delay based on the uncertainty, such that the delay added to a second pre-synaptic spike time of the first neuron model results in a causal margin of time between the delayed second pre-synaptic spike time and a post-synaptic spike time of a second neuron model.

Abstract: Descriptions are provided of various implementations of an automated tuning process configured to optimize a procedure for post-processing images captured by a camera sensor.

Abstract: Certain aspects of the present disclosure support techniques for unsupervised neural replay, learning refinement, association and memory transfer.

Abstract: Certain aspects of the present disclosure support techniques for unsupervised neural replay, learning refinement, association and memory transfer.

Abstract: Certain aspects of the present disclosure support techniques for unsupervised neural replay, learning refinement, association and memory transfer.

Abstract: Certain aspects of the present disclosure support techniques for unsupervised neural replay, learning refinement, association and memory transfer.

Abstract: Certain aspects of the present disclosure relate to a technique for adaptive structural delay plasticity applied in spiking neural networks. With the proposed method of structural delay plasticity, the requirement of modeling multiple synapses with different delays can be avoided. In this case, far fewer potential synapses should be modeled for learning.

Abstract: Certain aspects of the present disclosure support a technique for neural temporal coding, learning and recognition. A method of neural coding of large or long spatial-temporal patterns is also proposed. Further, generalized neural coding and learning with temporal and rate coding is disclosed in the present disclosure.

Abstract: Certain aspects of the present disclosure support a technique for neuronal firing modulation via noise control. Response curve of a typical neuron with a threshold can transition from not firing to always firing with a very small change in the neuron's input, thus limiting the range of excitable input patterns for the neuron. By introducing local, region and global noise terms, the slope of the neuron's response curve can be reduced. This may enable a larger set of input spike patterns to be effective in causing the neuron to fire, i.e., the neuron can be responsive to a large range of input patterns instead of an inherently small set of patterns in a noiseless situation.