This paper theoretically investigates and experimentally verifies properties of predictive coding network. Although limited to simple representations, the mathematical analysis has impressed the reviewers. The AC thus recommends acceptance of this work.

Additional Feedback: Minor comments: l. 87: "were" - "where" l.128: the relation to E-I balanced networks could be made more explicit. In some versions of those networks, there are also two independent effective parameters that scale separately the negative feedback and the variance of the connectivity (see e.g. Mastrogiuseppe and Ostojic 2017) l. 223 "the full solution for the chaotic system is highly involved" - the solution for adiabatic inputs seems to be available from Ref.23, but perhaps the situation here is different? My understanding is that we are here in the adiabatic limit, not in the case of Ref 38? In the adiabatic case, why does the (finite) correlation timescale of the noise matter for coding?

Biological neural networks face a formidable task: performing reliable computations in the face of intrinsic stochasticity in individual neurons, imprecisely specified synaptic connectivity, and nonnegligible delays in synaptic transmission. A common approach to combatting such biological heterogeneity involves averaging over large redundant networks of N neurons resulting in coding errors that decrease classically as the square root of N. Recent work demonstrated a novel mechanism whereby recurrent spiking networks could efficiently encode dynamic stimuli achieving a superclassical scaling in which coding errors decrease as 1/N. This specific mechanism involved two key ideas: predictive coding, and a tight balance, or cancellation between strong feedforward inputs and strong recurrent feedback. However, the theoretical principles governing the efficacy of balanced predictive coding and its robustness to noise, synaptic weight heterogeneity and communication delays remain poorly understood. To discover such principles, we introduce an analytically tractable model of balanced predictive coding, in which the degree of balance and the degree of weight disorder can be dissociated unlike in previous balanced network models, and we develop a mean-field theory of coding accuracy.