To record from a given neuron, a recording technology must be able to separate the
activity of that neuron from the activity of its neighbors. Here, we develop a Fisher
information based framework to determine the conditions under which this is feasible for a
given technology. This framework combines measurable point spread functions with
measurable noise distributions to produce theoretical bounds on the precision with which a
recording technology can localize neural activities. If there is sufficient information to
uniquely localize neural activities, then a technology will, from an information theoretic
perspective, be able to record from these neurons. We (1) describe this framework, and (2)
demonstrate its application in model experiments. This method generalizes to many
recording devices that resolve objects in space and should be useful in the design of
next-generation scalable neural recording systems.
