Deep Reinforcement Learning Algorithms in Intelligent Infrastructure

Intelligent infrastructure, including smart cities and intelligent buildings, must learn and adapt to the variable needs and requirements of users, owners and operators in order to be future proof and to provide a return on investment based on Operational Expenditure (OPEX) and Capital Expenditure (CAPEX). To address this challenge, this article presents a biological algorithm based on neural networks and deep reinforcement learning that enables infrastructure to be intelligent by making predictions about its different variables. In addition, the proposed method makes decisions based on real time data. Intelligent infrastructure must be able to proactively monitor, protect and repair itself: this includes independent components and assets working the same way any autonomous biological organisms would. Neurons of artificial neural networks are associated with a prediction or decision layer based on a deep reinforcement learning algorithm that takes into consideration all of its previous learning. The proposed method was validated against an intelligent infrastructure dataset with outstanding results: the intelligent infrastructure was able to learn, predict and adapt to its variables, and components could make relevant decisions autonomously, emulating a living biological organism in which data flow exhaustively.

Healthy oscillatory coordination is bounded by single-unit computation

Oscillations can improve neural coding by grouping action potentials into synchronous windows of activity, but this same effect can harm coding when action potentials become over-synchronized. Diseases ranging from Parkinson’s to epilepsy suggest that oversynchronization leads to pathology, but the precise boundary separating healthy from pathological synchrony remains an open theoretical problem. Here we study a simple model that shows how error in individual cells’ computations is traded for population-level synchronization. To put the in biological terms accessible to the cell we conceive of a “voltage budget” where instantaneous moments of membrane voltage can be partitioned into oscillatory and computational terms. By comparing these budget terms we derive a new set of biologically measurable inequalities that bound healthy from pathological synchrony. Finally, we derive an optimal non-biological algorithm for exchanging computational error with population synchrony.

Solving the Maximum Weighted Clique Problem Based on Parallel Biological Computing Model

The maximum weighted clique (MWC) problem, as a typical NP-complete problem, is difficult to be solved by the electronic computer algorithm. The aim of the problem is to seek a vertex clique with maximal weight sum in a given undirected graph. It is an extremely important problem in the field of optimal engineering scheme and control with numerous practical applications. From the point of view of practice, we give a parallel biological algorithm to solve the MWC problem. For the maximum weighted clique problem withmedges andnvertices, we use fixed length DNA strands to represent different vertices and edges, fully conduct biochemical reaction, and find the solution to the MVC problem in certain length range withO(n2)time complexity, comparing to the exponential time level by previous computer algorithms. We expand the applied scope of parallel biological computation and reduce computational complexity of practical engineering problems. Meanwhile, we provide a meaningful reference for solving other complex problems.