@article {,
	title = {Modeling spiking neural networks},
	journal = {Theoretical Computer Science},
	volume = {395},
	year = {2008},
	pages = {57-76},
	publisher = {Elsevier},
	abstract = {<div align="justify">
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.
</div>
},
	keywords = {Formal models, Neural networks, Specification, Systems design methodology},
	attachments = {http://daisy.cti.gr/files/Modeling spiking neural networks.pdf},
	author = {Ioannis Zaharakis and Achilles Kameas}
}
