  A central issue in neural recording is that of distinguishing the activities
of many neurons. Here, we develop a framework, based on Fisher information, to
quantify how separable a neuron's activity is from the activities of nearby
neurons. We (1) apply this framework to model information flow and spatial
distinguishability for several electrical and optical neural recording methods,
(2) provide analytic expressions for information content, and (3) demonstrate
potential applications of the approach. This method generalizes to many
recording devices that resolve objects in space and thus may be useful in the
design of next-generation scalable neural recording systems.
