Stephen Grossberg, Department of
Cognitive
and Neural Systems, Center for Adaptive Systems, and Center of
Excellence for
Learning in Education, Science, and Technology, Boston University
Major progress has been made in modeling how the laminar circuits of visual cortex see and learn to recognize objects and their 3D shapes. Such models link brain mechanisms to behavioral functions, and predict brain representations of conscious and unconscious experiences, including links between behavior, neuroanatomy, neurophysiology, biophysics, and biochemistry. This progress has included discovery of new computational paradigms whereby advanced brains autonomously adapt in real time to a changing world filled with unexpected events. These new paradigms include Complementary Computing, which clarifies the nature of brain specialization, and Laminar Computing, which clarifies why mammalian neocortex uses laminar circuits to represent multiple types of higher intelligence.
The
talk will discuss a model of how the brain computes representations of
3D
boundaries and surfaces that together compute a multiple-scale
distributed
representation of 3D object shape. This FACADE model, and its further
development and realization in laminar cortical circuits as the 3D
LAMINART
model, provide a unified explanation of many perceptual and
neurobiological
data bases about 3D vision using model mechanisms and circuits that
represent
processes in cortical areas V1, V2, and V4. The talk will then discuss
how the
brain learns to categorize multiple object views into an emerging
view-invariant object representation. This ARTSCAN model clarifies the
following issues: What is an object? How does the brain learn to bind
multiple
views of an object into a view-invariant object category, during both
unsupervised and supervised learning, while scanning its various parts
with
active eye movements? In particular, how does the brain avoid the
problem of
erroneously classifying views of different objects as belonging to a
single
object, and how does the brain direct the eyes to explore an object’s
surface
even before it has a concept of the object? How does the brain
coordinate
object and spatial attention during object learning and recognition?
ARTSCAN
proposes an answer to these questions by modeling interactions between
cortical
areas V1, V2, V3A, V4, ITp, ITa, PPC, LIP, and PFC. This part of the
talk will
outline how processes of Consciousness, Learning, Expectation,
Attention,
Resonance, and Synchrony (CLEARS) interact to enable the brain to learn
object
representations quickly without experiencing catastrophic forgetting.
Cao,
Y. and Grossberg, S. (2005). A laminar cortical model of stereopsis and
3D
surface perception: Closure and da Vinci stereopsis. Spatial Vision, 18,
515-578.
Fang,
L. and Grossberg, S. (2009). From stereogram to surface: How the brain
sees the
world in depth. Spatial Vision,
22, 45-82
Fazl,
A., Grossberg, S., and Mingolla, E. (2009). View-invariant object
category
learning, recognition, and search: How
spatial and object attention are coordinated using surface-based
attentional
shrouds. Cognitive Psychology, 58, 1-48.
Grossberg,
S. (1999). The link between brain learning, attention, and
consciousness. Consciousness
and Cognition, 8,
1-44.
Grossberg,
S. (2003). How does the cerebral cortex work? Development,
learning,
attention, and 3D vision by laminar
circuits of visual cortex. Behavioral and Cognitive Neuroscience
Reviews,
2, 47-76.
Gossberg,
S. (2007). Consciousness CLEARS the
mind. Neural Networks, 20: 1040-1053.
Grossberg,
S. and Hong, S. (2006). A neural model of surface perception:
Lightness,
anchoring, and filling-in. Spatial
Vision, 19, 263-321.
Grossberg,
S., Kuhlmann, L., and Mingolla, E. (2007). A neural model of 3D
shape-from-texture: Multiple-scale filtering, boundary grouping, and
surface
filling-in. Vision Research, 47,
634-672.
Grossberg,
S., and Swaminathan, G. (2004). A laminar cortical model for 3D
perception of
slanted and curved surfaces and of 2D
images: development, attention and bistability. Vision Research,
44, 1147-1187.
Grossberg, S. and Versace, M.
(2008). Spikes,
synchrony, and attentive learning by laminar thalamocortical circuits. Brain
Research, 1218, 278-312.
Grossberg,
S., Yazdanbakhsh, A., Cao, Y., and Swaminathan, G. (2008). How does
binocular
rivalry emerge from cortical mechanisms
of 3-D vision? Vision Research, 48,
2232-2250.
Kelly,
F.J. and Grossberg, S. (2000). Neural dynamics of 3-D surface
perception:
Figure-ground separation and lightness
perception. Perception &
Psychophysics, 62,
1596-1619.