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We consider the typical asymptotic behaviour of cellular automata of higher dimension (greater than 2). That is, we take an initial configuration at random according to a Bernoulli (i.i.d) probability measure, iterate some cellular…

Dynamical Systems · Mathematics 2017-02-21 Martin Delacourt , Benjamin Hellouin de Menibus

We present a generalization of standard Turing machines based on allowing unusual tapes. We present a set of reasonable constraints on tape geometry and classify all tapes conforming to these constraints. Surprisingly, this generalization…

Logic · Mathematics 2010-05-18 Aubrey da Cunha

A computational tool for coarse-graining nonlinear systems of ordinary differential equations in time is discussed. Three illustrative model examples are worked out that demonstrate the range of capability of the method. This includes the…

Numerical Analysis · Mathematics 2017-11-23 Sabyasachi Chatterjee , Amit Acharya , Zvi Artstein

This note is a survey of examples and results about cellular automata with the purpose of recalling that there is no 'universal' way of being computationally universal. In particular, we show how some cellular automata can embed efficient…

Computational Complexity · Computer Science 2021-12-03 Guillaume Theyssier

Computational power can be measured by assigning an algebraic structure to a computational device. Here, we convert a small patch of Conway's Game of Life into a transformation semigroup. The conversion captures not only time evolution but…

Cellular Automata and Lattice Gases · Physics 2026-04-17 Attila Egri-Nagy , Chrystopher L. Nehaniv

Definable topological groups whose topologies are affine have definable $\mathcal C^r$ structures in d-minimal expansions of ordered fields, where $r$ is a positive integer. We prove this fact using a new notion called partition degree of a…

Logic · Mathematics 2024-07-24 Masato Fujita

We summarize a recent study of discrete (integer-valued) Hamiltonian cellular automata (CA) showing that their dynamics can only be consistently defined, if it is linear in the same sense as unitary evolution described by the Schr\"odinger…

Quantum Physics · Physics 2014-10-13 Hans-Thomas Elze

Quantum cellular automata consist in arrays of identical finite-dimensional quantum systems, evolving in discrete-time steps by iterating a unitary operator G. Moreover the global evolution G is required to be causal (it propagates…

Quantum Physics · Physics 2019-09-09 Pablo Arrighi

Quantum cellular automata (QCA) are reviewed, including early and more recent proposals. QCA are a generalization of (classical) cellular automata (CA) and in particular of reversible CA. The latter are reviewed shortly. An overview is…

Quantum Physics · Physics 2008-08-06 K. Wiesner

This talk advocates intrinsic universality as a notion to identify simple cellular automata with complex computational behavior. After an historical introduction and proper definitions of intrinsic universality, which is discussed with…

Computational Complexity · Computer Science 2009-06-18 Nicolas Ollinger

Reversible Cellular Automata (RCA) are a particular kind of shift-invariant transformations characterized by a dynamics composed only of disjoint cycles. They have many applications in the simulation of physical systems, cryptography and…

Neural and Evolutionary Computing · Computer Science 2021-05-26 Luca Mariot , Stjepan Picek , Domagoj Jakobovic , Alberto Leporati

Computational mechanics, an approach to structural complexity, defines a process's causal states and gives a procedure for finding them. We show that the causal-state representation--an $\epsilon$-machine--is the minimal one consistent with…

Statistical Mechanics · Physics 2022-02-17 Cosma Rohilla Shalizi , James P. Crutchfield

We extend in a natural way the operation of Turing machines to infinite ordinal time, and investigate the resulting supertask theory of computability and decidability on the reals. The resulting computability theory leads to a notion of…

Logic · Mathematics 2007-05-23 Joel David Hamkins , Andy Lewis

The architecture of neural Turing machines is differentiable end to end and is trainable with gradient descent methods. Due to their large unfolded depth Neural Turing Machines are hard to train and because of their linear access of…

Neural and Evolutionary Computing · Computer Science 2016-12-08 Janez Aleš

We present an extension to the $\mathtt{mathlib}$ library of the Lean theorem prover formalizing the foundations of computability theory. We use primitive recursive functions and partial recursive functions as the main objects of study, and…

Logic in Computer Science · Computer Science 2019-07-19 Mario Carneiro

The Church-Turing thesis asserts that if a partial strings-to-strings function is effectively computable then it is computable by a Turing machine. In the 1930s, when Church and Turing worked on their versions of the thesis, there was a…

Logic in Computer Science · Computer Science 2019-01-16 Yuri Gurevich

Many recent advancements in quantum computing leverage strong drives on nonlinear systems for state preparation, signal amplification, or gate operation. However, the interplay within such strongly driven system introduces multi-scale…

Quantum Physics · Physics 2024-08-02 Leon Bello , Wentao Fan , Aditya Gandotra , Hakan E. Türeci

This work exposes which mechanisms and procesess in the Nature of evolution compute a function not computable by Turing machine. The computer with intelligence that is not higher than one bacteria population could have, but with efficency…

Neural and Evolutionary Computing · Computer Science 2007-08-21 D. Roglic

Cellular automata and their differentiable counterparts, Neural Cellular Automata (NCA), are highly expressive and capable of surprisingly complex behaviors. This paper explores how NCAs perform when applied to tasks requiring precise…

Neural and Evolutionary Computing · Computer Science 2025-12-03 Kevin Xu , Risto Miikkulainen

We explore the use of class-conditional autoregressive (CA) models to perform image classification on MNIST-10. Autoregressive models assign probability to an entire input by combining probabilities from each individual feature; hence…

Machine Learning · Computer Science 2022-04-01 Nathan Elazar
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