# [2006.14178] Predictive coding in balanced neural networks with noise, chaos and delaysopen searchopen navigation menucontact arXivsubscribe to arXiv mailings

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 $1/\sqrt{N}$. 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 understoo

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Date: 2020/06/26 14:21

## Referring Tweets

@SuryaGanguli Cool new work lead by @kadmonj and @jontimcheck on a general mean field theory of the accuracy of predictive coding in recurrent neural networks in the simultaneous presence of nonlinearity, balance, noise, weight disorder, chaos, & synaptic delays t.co/C4u6AjlFWJ thread: t.co/TbhzjUL8ck
@kadmonj New preprint! a collaboration with @jontimcheck and @SuryaGanguli. How can neuronal networks carry reliable computations in the face of intrinsic stochasticity, imprecisely specified connectivity, and nonnegligible delays in synaptic transmission? 1/8 t.co/q1maoadova t.co/IHCbg8vZ7R

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