20 papers · ranked by Valyu relevance
Benedikt Hartl, Michael Levin, Léo Pio‐Lopez
Neural Cellular Automata (NCA) represent a powerful framework for modeling biological self-organization, extending classical rule-based systems with trainable, differentiable (or evolvable) update rules that capture the adaptive self-regulatory dynamics of living matter. By embedding Artificial Neural Networks (ANNs)…
Alex D. Richardson, Tibor Antal, Richard A. Blythe, Linus J. Schumacher + 1 more
'Linus J. Schumacher' 'Julio R. Banga'] Neural Cellular Automata (NCA) are a powerful combination of machine learning and mechanistic modelling. We train NCA to learn complex dynamics from time series of images and Partial Differential Equation (PDE) trajectories. Our method is designed to identify underlying local…
James Stovold
Neural Cellular Automata (NCAs) are a model of morphogenesis, capable of growing two-dimensional artificial organisms from a single seed cell. In this paper, we show that NCAs can be trained to respond to signals. Two types of signal are used: internal (genomically-coded) signals, and external (environmental) signals.…
Lana Sinapayen
This paper reports on patterns exhibiting self-replication with spontaneous, inheritable mutations and exponential genetic drift in Neural Cellular Automata. Despite the models not being explicitly trained for mutation or inheritability, the descendant patterns exponentially drift away from ancestral patterns, even…
Zhibai Jia
This essay discusses the connections and differences between two emerging paradigms in deep learning, namely Neural Cellular Automata and Deep Equilibrium Models, and train a simple Deep Equilibrium Convolutional model to demonstrate the inherent similarity of NCA and DEQ based methods. Finally, this essay speculates…
Alejandro Hernandez Ruiz, Armand Vilalta, Francesc Moreno-Noguer
Very recently, the Neural Cellular Automata (NCA) has been proposed to simulate the morphogenesis process with deep networks. NCA learns to grow an image starting from a fixed single pixel. In this work, we show that the neural network (NN) architecture of the NCA can be encapsulated in a larger NN. This allows us to…
Mia-Katrin Kvalsund, Kai Olav Ellefsen, Kyrre Glette, Sidney Pontes-Filho + 1 more
The brain’s distributed architecture has inspired numerous artificial intelligence (AI) systems, particularly through its neocortical organization. However, current AI approaches largely over-look a crucial aspect of biological intelligence: active sensing – the deliberate movement of sensory organs to explore the…
Maxence Faldor, Antoine Cully
Cellular automata have become a cornerstone for investigating emergence and self-organization across diverse scientific disciplines, spanning neuroscience, artificial life, and theoretical physics. However, the absence of a hardware-accelerated cellular automata library limits the exploration of new research…
Reinier Xander A. Ramos, Jacqueline C. Dominguez, Johnrob Y. Bantang
Realistic single-cell neuronal dynamics are typically obtained by solving models that involve solving a set of differential equations similar to the Hodgkin-Huxley (HH) system. However, realistic simulations of neuronal tissue dynamics -especially at the organ level, the brain- can become intractable due to an…
Vahid Pashaei Rad, Vahid Azimi Rad, Saleh Valizadeh Sotubadi
In this paper, a new method called SCLA which stands for Spiking based Cellular Learning Automata is proposed for a mobile robot to get to the target from any random initial point. The proposed method is a result of the integration of both cellular automata and spiking neural networks. The environment consists of…
Alexandru Agapie, Anca Andreica, Camelia Chira, Marius Giuclea + 1 more
'Jesus Gomez-Gardenes'] Modelled as finite homogeneous Markov chains, probabilistic cellular automata with local transition probabilities in (0, 1) always posses a stationary distribution. This result alone is not very helpful when it comes to predicting the final configuration; one needs also a formula connecting the…
Sidney Pontes-Filho, Pedro Lind, Anis Yazidi, Jianhua Zhang + 5 more
'Hugo Hammer' 'Gustavo B. M. Mello' 'Ioanna Sandvig' 'Gunnar Tufte' 'Stefano Nichele'] Although deep learning has recently increased in popularity, it suffers from various problems including high computational complexity, energy greedy computation, and lack of scalability, to mention a few. In this paper, we…
Robert M Caudle
Background Recent work has indicated an increasingly complex role for astrocytes in the central nervous system. Astrocytes are now known to exchange information with neurons at synaptic junctions and to alter the information processing capabilities of the neurons. As an extension of this trend a hypothesis was proposed…
Eduardo P. Olimpio, Hyun Youk
How living systems generate order from disorder is a fundamental question^1-5^. Metrics and ideas from physical systems have elucidated order-generating collective dynamics of mechanical, motile, and electrical living systems such as bird flocks and neuronal networks^6-8^. But suitable metrics and principles remain…
Kui Tuo, Shengfeng Deng, Yuxiang Yang, Yanyang Wang + 4 more
The local rules of elementary cellular automata (ECA) with one-dimensional three-cell neighborhoods are represented by eight-bit binary numbers that encode deterministic update rules. This class of systems is also commonly referred to as the Wolfram cellular automata. These automata are widely utilized to investigate…
Lars Koopmans, Hyun Youk
To celebrate Hans Frauenfelder’s achievements, we examine energy(-like) “landscapes” for complex living systems. Energy landscapes summarize all possible dynamics of some physical systems. Energy(-like) landscapes can explain some biomolecular processes, including gene expression and, as Frauenfelder showed, protein…
Alexis Dubreuil, Arthur Leblois, Rémi Monasson
Elaborated temporal structures in behavior have been recognized as a hall-mark of high-level cognitive processes, such as planning or natural language. Systems neuroscience experiments in behaving animals have validated cell-assemblies as a fundamental piece of neural circuitry underlying lower-level cognitive…
Luryane F. Souza, Hernane B. de B. Pereira, Tarcisio M. da Rocha Filho, Bruna A. S. Machado + 1 more
One of the first steps in protein sequence analysis is comparing sequences to look for similarities. We propose an information theoretical distance to compare cellular automata representing protein sequences, and determine similarities. Our approach relies in a stationary Hamming distance for the evolution of the…
V. L. Kalmykov, L. V. Kalmykov
Mathematical black box models, which hide the structure and behavior of the subsystems, currently dominate science. Errors and paradoxes, such as the biodiversity paradox and the limiting similarity hypothesis, often arise from subjective interpretations of these hidden mechanisms. To address these problems, we have…
Authors not listed
Real-world datasets in chemical engineering and bioengineering processes--such as those from catalytic reactors, multiphase flows, polymerization reactors, bioreactors, and clinical trials--can often be unlabelled or disorganized, rendering the training of existing supervised learning models ineffective at learning the…