|
Map of
cortical activity
|
Despite
the rapid development of computer controlled automation, the
capabilities of currently
employed in the fields
of robotics and smart instrumentation are far exceeded by the
mammalian brain. Consequently, the next generation of intelligent
devices will need to combine the best features of machines (i.e.
computational speed, and reliability) with those of brains (i.e.
exceptional perception abilities, high learning rates and capability
of generalization). In this research project, we are using a
multidisciplinary approach to investigate how the learning of
problem-solving strategies, defined
by the establishment of arbitrary mapping of sensory stimuli
into complex motor actions influences large-scale corticocortical
interactions in behaving primates. The investigation of this
fundamental component of intelligent behavior will combine new
technologies for chronic and simultaneous, multisite neural
ensemble recordings, VLSI design, and pattern recognition analysis.
To assist in interpreting the neuronal ensemble patterns of
activity, we are creating
creating a simulator
which models corticocortical interactions similar to those recorded
in our electrophysiological experiments. Our experiments and
cortical models will also be used to evaluate the ability of
primates to resolve conflicts between simultaneously presented
visual and tactile instructions that cue animals to perform
different arm movement. Ken Eaton, George Hugh
Funding
for this work comes in part from the National
Science Foundation
|
