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Cognitive science is currently a federation of disciplines—psychology,
AI, linguistics, logic, neuroscience,
etc.—lacking a unified theory. Cognitive models are broadly divided
between a logical paradigm, or "cognitivism", and a dynamical
paradigm, or "connectionism". Bridging this gap will
require an intermediate or mesoscopic level of description, at a
finer granularity than symbols but larger structural complexity
than small artificial neural nets.
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Logical systems define high-level
symbols and formal grammars, but do not have a microstructure needed
to explain the fuzzy complexity of perception, memory or learning.
Conversely, dynamical systems define functionality as emerging from
neural networks and low-level activation equations, but lack a
macroscopic level supporting the systematic symbolic composition of
language and reasoning.
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Parallel self-organization of connectivity and activity in an
initially random spiking neural network, to support a
hierarchy of structured representations applied to visual,
auditory or linguistic tasks
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Striking regularities in the connectivity structure of
the visual system and other cortical areas account for their
functional specialization.
Elie Bienenstock and myself have designed a
neural network model that reproduces the development of such
regularities as a phenomenon of spatiotemporal pattern
formation. We show the spontaneous and simultaneous emergence
of regular chains of synaptic connectivity together with a
wave-like propagation of neural activity.
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Starting from an undifferentiated random state, our dynamical,
self-reconfiguring neural network transitions into an ordered
regime of activity and connectivity. In this regime, chains
sustain and guide waves, which in turn create and reinforce chains.
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Coding visual metric with temporal correlations: a model of
deformable template matching based on phase-locking
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In this work, started when I was a postdoc with
Christoph von der Malsburg
and pursued today with my PhD student
Carlos Sánchez, I propose that
nonzero phase-locking (i.e. delayed correlations) among
oscillating units can be exploited to code for metric
relationships among the features of an image. If spiking times
are interpreted as coordinates, then two spatiotemporal patterns
of activity (STPs) can represent a 2D visual pattern.
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Visual perception likely involves two processing levels: bottom-up
processing, during which features spontaneously group according to
low-level cues, and top-down processing, during which global
segmentation is completed from above by schemas previously stored
in memory.
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Bridging the gap between vision and language by importing complex
system modeling into linguistics
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How can the same relationship 'in' apply to containers as topologically
different as 'box', 'tree' or 'bowl'? The objective of this study
is to categorize the infinite diversity of schematic visual scenes
into a small set of grammatical elements. To elucidate this "topology
of language", I have proposed with
Jean Petitot a novel dynamical system approach
to cognitive linguistics based on the generation of traveling waves
in cellular automata and spiking neural networks.
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How can the same linguistic preposition 'in' apply to scenes as
different as "the shoe in the box" (small, hollow, closed volume),
"the bird in the tree" (large, dense, open volume) or "the fruit in
the bowl" (curved surface)? How can language be so insensitive to
wide topological and morphological differences among visual percepts?
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