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A general data-flow framework featuring nondeterminism, laziness and neurological pseudo-terminology. It can be
used for example for data-flow computations or event propagation networks. It tries hard to aide type checking and to
allow proper initialization and cleanup so that interfaces to input and output devices (data or events producers or
consumers) can be made (so that created models/systems/networks can be used directly in real world applications, for
example robots).
Its main goal is to model complex neural networks with more biological realism. Namely that impulses do
take time to travel and neuron responses are also not instantaneous. And of course that neural systems are in
its base nondeterministic and that some level of determinism is build upon that. All this of course makes reasoning
about such networks even harder (impossible?).
This framework is in fact just a simple abstraction of Haskell threads and data passing between them through
channels with threads' initialization and cleanup wrapped into a Haskell type class.
Feel free to contribute or suggest additional features or (example) programs or to create interfaces to other modules.
