Abstract:
A method of audio source segregation includes selecting an audio attribute of an audio signal. The method also includes representing a portion of the audio attribute that is dominated by a single source as a source spiking event. In addition, the method includes representing a remaining portion of the audio signal as an audio signal spiking event. The method further includes determining whether the remaining portion coincides with the single source based on coincidence of the source spiking event and audio signal spiking event.
Abstract:
A method for maintaining a state variable in a synapse of a neural network includes maintaining a state variable in an axon. The state variable in the axon may be updated based on an occurrence of a first predetermined event. The method also includes updating the state variable in the synapse based on the state variable in the axon and an occurrence of a second predetermined event.
Abstract:
A method of dynamically modifying target selection with a neural network includes dynamically modifying a selection function by controlling an amount of imbalance of connections in the neural network. A selected neuron represents one of multiple candidate targets.
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:
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.
Abstract:
A method for performing a desired sequence of actions includes determining a list of candidate activities based on negotiations with at least one other entity. The determining is also based on preference information, an expected reward, a priority and/or a task list. The list of candidate activities may also be determined based on reinforcement learning. The method also includes receiving a selection of one of the candidate activities. The method further includes performing a sequence of actions corresponding to the selected candidate activity. In this manner, a smartphone or other computing device may be transformed into an intelligent companion for planning activities.
Abstract:
A method of executing co-processing in a neural network comprises swapping a portion of the neural network to a first processing node for a period of time. The method also includes executing the portion of the neural network with the first processing node. Additionally, the method includes returning the portion of the neural network to a second processing node after the period of time. Further, the method includes executing the portion of the neural network with the second processing node.
Abstract:
Values are synchronized across processing blocks in a neural network by encoding spikes in a first processing block with a value to be shared across the neural network. The spikes may be transmitted to a second processing block in the neural network via an interblock interface. The received spikes are decoded in the second processing block so as to generate a value that is synchronized with the value of the first processing block.