Weapplythisnovelformulation of inference as controlto the canonical problem of inferring the hidden scalar state of a linear dynamical system with Gaussian variability.

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. Overall, our work provides and solves a general theoretical framework for dissecting the differential contributions neural noise, synaptic disorder, chaos, synaptic delays, and balance to the fidelity of predictive neural codes, reveals the fundamental role that balance plays in achieving superclassical scaling, and unifies previously disparate models in theoretical neuroscience.

Sensory observations about the world are invariably ambiguous.

The uptake of Artificial Intelligence (AI) impacts the way we work, interact, do business, and conduct research. However, organizations struggle to apply AI successfully in industrial settings where the focus is on end-to-end operational processes. Here, we consider generative, predictive, and prescriptive AI and elaborate on the challenges of diagnosing and improving such processes. We show that AI needs to be grounded using Object-Centric Process Mining (OCPM). Process-related data are structured and organization-specific and, unlike text, processes are often highly dynamic. OCPM is the missing link connecting data and processes and enables different forms of AI. We use the term Process Intelligence (PI) to refer to the amalgamation of process-centric data-driven techniques able to deal with a variety of object and event types, enabling AI in an organizational context. This paper explains why AI requires PI to improve operational processes and highlights opportunities for successfully combining OCPM and generative, predictive, and prescriptive AI.

Predictive coding networks (PCNs) constitute a biologically inspired framework for understanding hierarchical computation in the brain, and offer an alternative to traditional feedforward neural networks in ML. This note serves as a quick, onboarding introduction to PCNs for machine learning practitioners. We cover the foundational network architecture, inference and learning update rules, and algorithmic implementation. A concrete image-classification task (CIFAR-10) is provided as a benchmark-smashing application, together with an accompanying Python notebook containing the PyTorch implementation.

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?

Re your article ('Dystopian' tool aims to predict murder, 9 April), the collection and automation of data has repeatedly led to the targeting of racialised and low-income communities, and must come to an end. This has been found by both Amnesty International in our Automated Racism report and by Statewatch in its findings on the "murder prediction" tool. For many years, successive governments have invested in data-driven and data-based systems, stating they will increase public safety – yet individual police forces and Home Office evaluations have found no compelling evidence that these systems have had any impact on reducing crime. Feedback loops are created by training these systems using historically discriminatory data, which leads to the same areas being targeted once again. These systems are neither revelatory nor objective.