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Fig. 15.8
A visual metaphor for the elaboration of evoked neural temporal pattern resonances
through successive interaction. The
concentric circles
represent stimulus-driven and stimulus-trig-
gered temporal patterns of spikes produced by neural assemblies, which interact with those of other
assemblies
patterns that encode the syntactic, semantic, and pragmatic aspects of an elaborated
neural activity pattern. Eventually, the signals in the global workspace would con-
verge into a stable set of neural signals that then sets a context for subsequent events,
interpretations, anticipations, and actions.
What we have outlined is an open-ended representational system in which exist-
ing primitives can be combined and new primitives formed. Combinatoric creativity
is achieved in such a system by independent signal types that can be nondestruc-
tively combined in complex composite signals. The complex composites form vec-
tors of attributes that can be individually accessed. Emergent creativity is achieved
when new signal types are created through reward-driven adaptive tuning of new
neural assemblies. When new signal types are created, new effective signal dimen-
sions appear in the system.
What we do not yet know is the exact nature of the central temporal codes that
would be involved, the binding mechanisms that would group attribute-related sig-
nals together into objects, the means by which relations between objects could be
represented in terms of temporal tags, and how universals might be distinguished
from individual instances (Marcus
2001
).
Despite its incomplete and highly tentative nature, this high level schematic nev-
ertheless does provide a basic scaffold for new thinking about the generation of
novel conceptual primitives in neural networks. We want to provide encouragement
and heuristics to those who seek to design mixed analog-digital self-organising arti-
ficial brains that might one day be capable of producing the kinds of combinatorial
and emergent creativities that regularly arise in our own heads.
Acknowledgements
I would like to gratefully thank Margaret Boden, Mark d'Inverno, and Jon
McCormack and the Leibniz Center for Informatics for organising and sponsoring the Dagstuhl
Seminar on Computational Creativity in July 2009 that made this present work possible.
