Locally adaptive cellular automata for goal-oriented self-organization

• URL: http://arxiv.org/abs/2306.07067v1
• Date: Mon, 12 Jun 2023 12:32:23 GMT
• Title: Locally adaptive cellular automata for goal-oriented self-organization
• Authors: Sina Khajehabdollahi, Emmanouil Giannakakis, Victor Buendia, Georg Martius, Anna Levina
• Abstract summary: We propose a new model class of adaptive cellular automata that allows for the generation of scalable and expressive models. We show how to implement adaptation by coupling the update rule of the cellular automaton with itself and the system state in a localized way.
• Score: 14.059479351946386
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The essential ingredient for studying the phenomena of emergence is the ability to generate and manipulate emergent systems that span large scales. Cellular automata are the model class particularly known for their effective scalability but are also typically constrained by fixed local rules. In this paper, we propose a new model class of adaptive cellular automata that allows for the generation of scalable and expressive models. We show how to implement computation-effective adaptation by coupling the update rule of the cellular automaton with itself and the system state in a localized way. To demonstrate the applications of this approach, we implement two different emergent models: a self-organizing Ising model and two types of plastic neural networks, a rate and spiking model. With the Ising model, we show how coupling local/global temperatures to local/global measurements can tune the model to stay in the vicinity of the critical temperature. With the neural models, we reproduce a classical balanced state in large recurrent neuronal networks with excitatory and inhibitory neurons and various plasticity mechanisms. Our study opens multiple directions for studying collective behavior and emergence.

Related papers

• Harnessing Neural Unit Dynamics for Effective and Scalable Class-Incremental Learning [38.09011520275557]
  Class-incremental learning (CIL) aims to train a model to learn new classes from non-stationary data streams without forgetting old ones. We propose a new kind of connectionist model by tailoring neural unit dynamics that adapt the behavior of neural networks for CIL.
  arXiv  Detail & Related papers  (2024-06-04T15:47:03Z)
• On the Generalization and Adaption Performance of Causal Models [99.64022680811281]
  Differentiable causal discovery has proposed to factorize the data generating process into a set of modules. We study the generalization and adaption performance of such modular neural causal models. Our analysis shows that the modular neural causal models outperform other models on both zero and few-shot adaptation in low data regimes.
  arXiv  Detail & Related papers  (2022-06-09T17:12:32Z)
• Growing Isotropic Neural Cellular Automata [63.91346650159648]
  We argue that the original Growing NCA model has an important limitation: anisotropy of the learned update rule. We demonstrate that cell systems can be trained to grow accurate asymmetrical patterns through either of two methods.
  arXiv  Detail & Related papers  (2022-05-03T11:34:22Z)
• Generative Adversarial Neural Cellular Automata [13.850929935840659]
  We introduce a concept using different initial environments as input while using a single Neural Cellular Automata to produce several outputs. We also introduce GANCA, a novel algorithm that combines Neural Cellular Automata with Generative Adrial Networks.
  arXiv  Detail & Related papers  (2021-07-19T06:23:11Z)
• Towards self-organized control: Using neural cellular automata to robustly control a cart-pole agent [62.997667081978825]
  We use neural cellular automata to control a cart-pole agent. We trained the model using deep-Q learning, where the states of the output cells were used as the Q-value estimates to be optimized.
  arXiv  Detail & Related papers  (2021-06-29T10:49:42Z)
• Continuous Learning and Adaptation with Membrane Potential and Activation Threshold Homeostasis [91.3755431537592]
  This paper presents the Membrane Potential and Activation Threshold Homeostasis (MPATH) neuron model. The model allows neurons to maintain a form of dynamic equilibrium by automatically regulating their activity when presented with input. Experiments demonstrate the model's ability to adapt to and continually learn from its input.
  arXiv  Detail & Related papers  (2021-04-22T04:01:32Z)
• A model-based framework for learning transparent swarm behaviors [6.310689648471231]
  This paper proposes a model-based framework to design understandable and verifiable behaviors for swarms of robots. The framework is tested on four case studies, featuring aggregation and foraging tasks.
  arXiv  Detail & Related papers  (2021-03-09T10:45:57Z)
• The Neural Coding Framework for Learning Generative Models [91.0357317238509]
  We propose a novel neural generative model inspired by the theory of predictive processing in the brain. In a similar way, artificial neurons in our generative model predict what neighboring neurons will do, and adjust their parameters based on how well the predictions matched reality.
  arXiv  Detail & Related papers  (2020-12-07T01:20:38Z)
• Towards an Automatic Analysis of CHO-K1 Suspension Growth in Microfluidic Single-cell Cultivation [63.94623495501023]
  We propose a novel Machine Learning architecture, which allows us to infuse a neural deep network with human-powered abstraction on the level of data. Specifically, we train a generative model simultaneously on natural and synthetic data, so that it learns a shared representation, from which a target variable, such as the cell count, can be reliably estimated.
  arXiv  Detail & Related papers  (2020-10-20T08:36:51Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.