Abstract: Technologies are provided for implementing temporal and spatio-temporal spiking neural networks (SNNs) using neuromorphic hardware devices. Temporal synapse circuits, with associated weight values, can be used to control spike times of connected neuron circuits. The controlled spike times of multiple neuron circuits can be used to temporally encode information in a neural network in neuromorphic hardware. Neuron circuits in a state space detection layer can be organized into multiple subsets. Neuron circuits in different subsets can be connected to output neuron circuits in an output layer by separate temporal synapse circuits. Spiking signals sent from the neuron circuits in the state space detection layer via separate temporal synapse circuits can cause associated output neuron circuits to generate output spiking signals at different times. The various spike times of the output neuron circuits can be aggregated to produce an output signal for the network.

Abstract: Technologies are provided for implementing temporal and spatio-temporal spiking neural networks (SNNs) using neuromorphic hardware devices. Temporal synapse circuits, with associated weight values, can be used to control spike times of connected neuron circuits. The controlled spike times of multiple neuron circuits can be used to temporally encode information in a neural network in neuromorphic hardware. Neuron circuits in a state space detection layer can be organized into multiple subsets. Neuron circuits in different subsets can be connected to output neuron circuits in an output layer by separate temporal synapse circuits. Spiking signals sent from the neuron circuits in the state space detection layer via separate temporal synapse circuits can cause associated output neuron circuits to generate output spiking signals at different times. The various spike times of the output neuron circuits can be aggregated to produce an output signal for the network.

Abstract: A data store query system includes a data store that stores a collection of data, a sorted result buffer, and a query interface operable to receive a limit and order query and to identify data in the data store that satisfies the limit and order query using the sorted result buffer. Additionally, a method for satisfying limit and order queries includes receiving a limit and order query that includes a limit criteria specifying a maximum number of records for a result set of records satisfying the limit and order query and an order criteria, filling a sorted result buffer with records from a data store, and iteratively reformulating the limit and order query and updating the sorted result buffer until the sorted result buffer contains the result set of records satisfying the limit and order query.