Neural systems are subject to significant amounts of noise. Therefore, any analysis of such systems must account for the effects of noise.
There are numerous sources of noise in any physical system, and neurobiological systems are no exception (see section 2.2.1). As a result, and despite recent contentions to the contrary (van Gelder and Port 1995), neural systems can be understood as essentially finite (Eliasmith 2001). This is important, though not surprising, because it means that information theory is applicable to analyzing such systems. This ubiquity of noise also suggests that knowing the limits of neural processing is important for understanding that processing. For instance, we would not expect neurons to transmit information at a rate of 10 or 20 bits per spike if the usual sources of noise limited the signal-to-noise ratio to 10:1, because that would waste valuable resources. Instead, we would expect information transmission rates of about 3 bits per spike given that signal-to-noise ratio as is found in many neurobiological systems (see section 4.4.2).
These kinds of limits prove to be very useful for determining how good we can expect a systems performance to be, and for constraining hypotheses about what a particular neural system is for. For example, if we choose to model a system with about 100 neurons (such as the horizontal neural integrator in the goldfish), and we know that the variance of the noise in the system is about 10%, we can expect a root-mean-squared (RMS) error of about 2% in that systems representation (see section 2.3). Conversely, we might know the errors typically observed in a systems behavior and the nature of the relevant signals, and use this knowledge to guide hypotheses about which subsystems are involved in which functions. Either way, information regarding implementational constraints, like noise, can help us learn something new about the system in which we are interested.