Modeling spiking neural networks

A notation for the functional specification of a wide range of neural networks consisting of temporal or non-temporal neurons, is proposed. The notation is primarily a mathematical framework, but it can also be illustrated graphically and can be extended into a language in order to be automated. Its basic building blocks are processing entities, finer grained than neurons, connected by instant links, and as such they form sets of interacting entities resulting in bigger and more sophisticated structures. The hierarchical nature of the notation supports both top-down and bottom-up specification approaches. The use of the notation is evaluated by a detailed example of an integrated tangible agent consisting of sensors, a computational part, and actuators. A process from specification to both software and hardware implementation is proposed.