PRESENTATION TITLE AND DESCRIPTION
Rolf Kotter: Exploring
structure-function coupling in cerebral cortical networks.
The cerebral cortex is a complicated network composed
of many specific areas, which communicate through a dense network
of axonal fibre projections. Crudely, connectivity defines the
flow of information, whereas area characteristics help define what
the components do with it and what the intrinsic computations are.
Equipped with the global wiring diagram and with simple areas models
we use multivariate statistics and computer simulations to explore
the coupling of structure and function in cortical networks. Both
matches and apparent mismatches between different empirical data
sets provide clues to cortical mechanisms.
Karl Friston: The nature
of representations and the role of non-linear coupling.
I will compare and contrast two perspectives on
how representations in the brain are constructed. These perspectives
are a constructive perspective where representations are assembled
in a bottom-up fashion through successive non-linear transformations
of sensory inputs. The alternative view is provided by generative
models where the sensory input is predicted or generated by high
level representations of their underlying cause. These models imply
fundamentally different neuronal architectures and respective roles
for non-linear transformations and coupling among cortical areas.
The generative model fits much more comfortably with known anatomy
and physiology. In particular it relies upon backwards connections
that emulate the non-linear mixing of real sensory causes to produce
sensory input. If valid, the generative or predictive perspective
means that the latencies of evoked neuronal responses, and their
temporal dynamics, should show a paradoxical inversion where late
components of early sensory responses are contingent on evoked transients
in higher cortical levels. Furthermore, the generative framework
suggests that non-linearity is a feature of backward as opposed
to forward connections. These conclusions rest upon an integration
of generative models in unsupervised learning and non-linear dynamics.
Alex Martin: Semantic
primitives and the representation of object concepts.
Object concepts are represented by networks of
semantic primitives. These primitives are stored within the processing
systems active when objects are perceived and manipulated (i. e.,
during learning). They include information about features and attributes
such as an object's typical shape, motion, and use-associated motor
movements. These representations are distributed in that: 1) They
involve multiple regions (e.g., ventral occipitotemporal, lateral
temporal, premotor cortices), and, 2) Within a region, different
object categories elicit complex and overlapping patterns of activity.
Thus, there are no category-specific" areas. These feature-based
representations are "semantic" in that they are associated
with the object concept, independent of stimulus format (pictures,
words, mental images, etc.). This type of feature-based model can
provide the combinatorial power needed to represent an infinite
variety of object concepts, and a Foundation for more abstract conceptualizations
(e.g., social interaction).
Robert Desimone: Translation
of cellular responses to mechanisms of attention.
from recent work
Steve Bressler: The
emergence of cognitive function from the operation of large-scale
Humans are able to rapidly and flexibly adapt to
an almost infinite variety of changing environments in a manner
that consistently integrates sensory, motor, and higher-order information
domains. A growing body of evidence from a number of fields indicates
that this dynamic adaptability derives from the ability of the cerebral
cortex to repeatedly change the state of coordination among its
constituent areas. It is now known that the evolution of cognitive
state on a sub-second time scale is characterized by a progression
of coordination states in the cortex, in which distributed sets
of cortical areas become coordinated by phase synchronization in
large-scale networks. Theoretical analysis suggests that the coordination
of large-scale networks is accompanied by the mutual constraint
of local activity patterns within the coordinated areas. The sum
of constraints imposed by the network on the pattern of activity
in any given area may dynamically create a variable local context
for its information processing. A mutual pattern constraint mechanism
in the cortex is proposed to satisfy large-scale processing demands
and direct behavior to specified goals. The versatility of this
mechanism may help to explain how the cortex overcomes the kind
of processing rigidity exemplified by many artificial network models.
Barry Horwitz: Neural
modeling: The interplay between data-fitting and simulation approaches.
Modern cognitive neuroscience methods produce vast
and exceedingly rich data sets at numerous spatial and temporal
scales - from single unit neuronal recordings to functional brain
imaging to neuropsychological investigations of behavior. The richness
and complexity of these data preclude easy understanding and engender
the need for equally rich computational approaches to data analysis,
and equally important, data interpretation. Data-fitting models
permit the extraction of conceptually defined parameters for the
spatiotemporal scale appropriate for each data set used. Simulation
neural models can be used to bridge spatiotemporal scales and thus
relate parameters obtained from different types of data to one another.
These points will be illustrated using functional brain imaging
John Gabrieli: Refining
memory theory with imaging.
Ideas about the functional neural architecture
of human memory used to depend on happenstance rare and complicated
lesions. Functional neuroimaging provides powerful new tools to
systematically delineate the anatomy and function of memory systems
of the human brain. Imaging research on memory, however, has often
either confirmed interpretations of the consequences of lesions
or provided unexpected but controversial findings. It is still
a challenge; therefore, to think about how imaging research may
truly inform theories of memory in a way that can sway fundamental
views in the field.
Martha Farah: What does
the brain have to say about the general kind of thing that the mind
Cognitive neuroscientists are hard at work finding
out whether we have specialized face processors, working memory
maintenance-only buffers, plasticity over this or that time frame,
etc. I'd like to call our attention to a more general set of questions
about the mind. Like the specific ones mentioned here, they are
empirical questions, for which neuroscience provides relevant data.
But unlike the specific ones mentioned here, these cannot be answered
by individual critical experiments, no matter how well designed.
Instead, they can only be answered by considering the overall pattern
of data that is accumulating in our field. These questions concern
the general kind of thing the mind is. Is it sensible to call it
computational, and what type of computational architecture is used?
Is it optimized for flexible, general-purpose problem-solving, or
specific problems? What is the balance of genetic preprogramming
versus experience-driven organization in the development of its
organization (be it general-purpose or specific mechanisms)? I
hope others will suggest more questions along these lines. As someone
who attended graduate school in the 70's, I can tell you how these
questions would have been answered then. Living history in your
midst! The answers are very different than today's, and as you'll
see, the change was driven mainly by neuroscience.
Consciousness integrated and differentiated.
A useful way of identifying the neural basis of
consciousness is to consider the kinds of neural processes that
could account for its most fundamental properties. Two fundamental
properties of consciousness are integration or unity, and differentiation
or complexity. Integration is evident in that each conscious state
is experienced as a whole and cannot be subdivided into independent
components. Differentiation is evidenced by our ability to access,
in a fraction of a second, any one out of countless numbers of conscious
states. To understand these properties of consciousness and their
neural substrates, a novel theory is developed that accounts at
the same time for the integration and the differentiation of conscious
experience. According to this theory, encapsulated in the dynamic
core hypothesis, consciousness does not arise as a property of brain
cells as such, but rather as a consequence of dynamic interactions
of a continually changing functional cluster of nerve cells in the
thalamus and cerebral cortex. The formulation of this theory has
required the development of new theoretical concepts and measures,
such as those for functional clustering and complexity, and the
construction of large-scale computer models of brain function.
A series of experiments using modern methods of magnetoencephalography
has shown that neural correlates of conscious experience are consistent
with the notion of a dynamic core and involve distributed brain
areas which are different in different individuals.
Mel Goodale: Visual Duplicity:
Representation versus action.
Visual systems first evolved not to enable animals
to see, but to provide distal sensory control of their movements.
Vision as 'sight' is a relative newcomer on the evolutionary landscape,
but its emergence has enabled animals to carry out complex cognitive
operations on perceptual representations of the world. In the more
ancient visuomotor systems, there is a basic isomorphism between
visual input and motor output. In representational vision, there
are many cognitive 'buffers' between input and output. Thus, the
relationship between what is on the retina and the behavior of the
organism cannot be understood without reference to other mental
states. The implications of all of this for the organization of
the visual pathways in the primate brain and the emergence of visual
experience will be discussed.
Scott Kelso: Self-organizing
systems and nonlinear dynamics.
Dr. Kelso's homepage: http://www.ccs.fau.edu/~kelso/
Randy McIntosh: Reflections
on the day
The purpose of this last presentation is to give
an overview (totally objective, of course) of the issues presented
during the day and suggest how these issue may or may not impact
on how we understand the relation between the brain and the mind.