Abstract
Recent experimental work has begun to characterize activity in local
cortical networks containing thousands of neurons. There has also
been an explosion of work on connectivity in networks of all types.
It would seem natural then to explore the influence of connectivity
on dynamics at the local network level. In this chapter, we will
give an overview of this emerging area. After a brief introduction,
we will first review early neural network models and show how they
suggested attractor dynamics of spatial activity patterns, based
on recurrent connectivity. Second, we will review physiological reports
of repeating spatial activity patterns that have been influenced by
this initial concept of attractors. Third, we will introduce tools
from dynamical systems theory that will allow us to precisely quantify
neural network dynamics. Fourth, we will apply these tools to simple
network models where connectivity can be tuned. We will conclude
with a summary and a discussion of future prospects
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