  We explore the spectra and localization properties of the N-site banded
one-dimensional non-Hermitian random matrices that arise naturally in sparse
neural networks. Approximately equal numbers of random excitatory and
inhibitory connections lead to spatially localized eigenfunctions, and an
intricate eigenvalue spectrum in the complex plane that controls the
spontaneous activity and induced response. A finite fraction of the eigenvalues
condense onto the real or imaginary axes. For large N, the spectrum has
remarkable symmetries not only with respect to reflections across the real and
imaginary axes, but also with respect to 90 degree rotations, with an unusual
anisotropic divergence in the localization length near the origin. When chains
with periodic boundary conditions become directed, with a systematic
directional bias superimposed on the randomness, a hole centered on the origin
opens up in the density-of-states in the complex plane. All states are extended
on the rim of this hole, while the localized eigenvalues outside the hole are
unchanged. The bias dependent shape of this hole tracks the bias independent
contours of constant localization length. We treat the large-N limit by a
combination of direct numerical diagonalization and using transfer matrices, an
approach that allows us to exploit an electrostatic analogy connecting the
"charges" embodied in the eigenvalue distribution with the contours of constant
localization length. We show that similar results are obtained for more
realistic neural networks that obey "Dale's Law" (each site is purely
excitatory or inhibitory), and conclude with perturbation theory results that
describe the limit of large bias g, when all states are extended. Related
problems arise in random ecological networks and in chains of artificial cells
with randomly coupled gene expression patterns.
