Abstract: A system, method, and computer program product are provided for efficient allocation of attributes corresponding to neurons or connections of multiple types using a common data structure. A map file is generated by a pre-processor in order to map an attribute of a neuron or connection to a particular location within the common data structure based on a type associated with the neuron or connection, while allowing a neuron or connection of a different type to map its own attribute to that same particular location. Additionally, kernel code can be written to reference attribute names made available by the map file in order to provide reusability of code.

Abstract: Disclosed are systems, apparatuses, and methods for implementing a phase-model neural network using a fixed amount of memory. Such a phase-model neural network includes a plurality of neurons, wherein each neuron is associated with two parameters—an activity and a phase. Example methods include (i) generating a sequence of variables associated with a probability distribution of phases and (ii) sequentially sampling the probability distribution of phases using a fixed amount of memory, regardless of a number of phases used in the phase-model neural network.