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Fig. 15.7
High level
functional schematic of the
brain as an overlapping and
interconnected set of
pattern-resonance loops each
of which supports a different
functionality
neuronal populations. The loops support pattern amplification cycles of neuronal
signals that allow signals to be dynamically regenerated within them. The loops
might be recurrent connectionist networks (Carpenter and Grossberg
2003
, McCul-
loch
1965
) or alternatively, they could be closed circuits that propagate temporal
patterns of neuronal activity (Thatcher and John
1977
).
That a system has the ability to regenerate alternative sets of neural signals—
neural “pattern resonances”—means that the system can support alternative persis-
tent informational states. The system can then keep a set of neuronal signals cir-
culating indefinitely as long as they remain relevant to ongoing goals. This ability
to hold signals dynamically is critical for short-term memory, informational inte-
gration in global workspaces, and may itself be a requisite for conscious aware-
ness (Baars
1988
, Cariani
2000
). In dynamical systems terms these mental states
are complex signal attractor states that are the stable eigen-behaviour modes of the
neural system (Rocha
1996
, von Foerster
2003
, Cariani
2001b
). In analogy with au-
tocatalytic networks that produce the components of living cells, one could think
of mutually-supporting signal-regenerations in the loops as an autopoiesis of neural
signal productions (Maturana and Varela
1973
).
One can sketch out a loose functional organisation based on regenerative process-
ing loops (Fig.
15.7
). Sensory information comes into the system through a number
of modality-specific sensory pathways. Neural sensory representations are built up
in each of the pathways through bottom-up and top-down loops that integrate infor-
mation in time to form stable perceptual images. When subsequent sensory patterns
are similar to previous ones, these patterns are amplified and stabilised; when they
diverge, new dynamically-created images are formed from the difference between
expectation and input. Such divergences are seen in evoked brain gross electrical
