  An adaptive observer is proposed to estimate the synaptic distribution
between neurons asymptotically from the measurement of a part of the neuronal
activity and a delayed neural field evolution model. The convergence of the
observer is proved under a persistency of excitation condition. Then, the
observer is used to derive a feedback law ensuring asymptotic stabilization of
the neural fields. Finally, the feedback law is modified to ensure
simultaneously practical stabilization of the neural fields and asymptotic
convergence of the observer under additional restrictions on the system.
Numerical simulations confirm the relevance of the approach.
