Lenia and Expanded Universe

• URL: http://arxiv.org/abs/2005.03742v1
• Date: Thu, 7 May 2020 20:41:13 GMT
• Title: Lenia and Expanded Universe
• Authors: Bert Wang-Chak Chan
• Abstract summary: We report experimental extensions of Lenia, a continuous cellular automata family capable of producing lifelike self-organizing autonomous patterns. We discover new phenomena like polyhedral symmetries, individuality, self-replication, emission, growth by ingestion, and saw the emergence of "virtual eukaryotes" that possess internal division of labor and type differentiation.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We report experimental extensions of Lenia, a continuous cellular automata family capable of producing lifelike self-organizing autonomous patterns. The rule of Lenia was generalized into higher dimensions, multiple kernels, and multiple channels. The final architecture approaches what can be seen as a recurrent convolutional neural network. Using semi-automatic search e.g. genetic algorithm, we discovered new phenomena like polyhedral symmetries, individuality, self-replication, emission, growth by ingestion, and saw the emergence of "virtual eukaryotes" that possess internal division of labor and type differentiation. We discuss the results in the contexts of biology, artificial life, and artificial intelligence.

Related papers

• Don't Cut Corners: Exact Conditions for Modularity in Biologically Inspired Representations [52.48094670415497]
  We develop a theory of when biologically inspired representations modularise with respect to source variables (sources) We derive necessary and sufficient conditions on a sample of sources that determine whether the neurons in an optimal biologically-inspired linear autoencoder modularise. Our theory applies to any dataset, extending far beyond the case of statistical independence studied in previous work.
  arXiv  Detail & Related papers  (2024-10-08T17:41:37Z)
• Engineering morphogenesis of cell clusters with differentiable programming [2.0690546196799042]
  We discover local interaction rules and genetic networks that yield emergent, systems-level characteristics in a model of development. We show that one can simultaneously learn parameters governing the cell interactions and the genetic network for complex developmental scenarios.
  arXiv  Detail & Related papers  (2024-07-08T18:05:11Z)
• Toward Artificial Open-Ended Evolution within Lenia using Quality-Diversity [5.380545611878407]
  We show that Quality-Diversity is an effective framework for the automatic discovery of diverse self-organizing patterns in complex systems. Our framework, called Leniabreeder, can leverage both manually defined diversity criteria and unsupervised measures of diversity to broaden the scope of discoverable patterns.
  arXiv  Detail & Related papers  (2024-06-06T16:35:27Z)
• Discovering Sensorimotor Agency in Cellular Automata using Diversity Search [17.898087201326483]
  In cellular automata (CA), a key open-question has been whether it is possible to find environment rules that self-organize. We show that this approach enables to find systematically environmental conditions in CA leading to self-organization. We show that the discovered agents have surprisingly robust capabilities to move, maintain their body integrity and navigate among various obstacles.
  arXiv  Detail & Related papers  (2024-02-14T14:30:42Z)
• Brain-Inspired Machine Intelligence: A Survey of Neurobiologically-Plausible Credit Assignment [65.268245109828]
  We examine algorithms for conducting credit assignment in artificial neural networks that are inspired or motivated by neurobiology. We organize the ever-growing set of brain-inspired learning schemes into six general families and consider these in the context of backpropagation of errors. The results of this review are meant to encourage future developments in neuro-mimetic systems and their constituent learning processes.
  arXiv  Detail & Related papers  (2023-12-01T05:20:57Z)
• Causal machine learning for single-cell genomics [94.28105176231739]
  We discuss the application of machine learning techniques to single-cell genomics and their challenges. We first present the model that underlies most of current causal approaches to single-cell biology. We then identify open problems in the application of causal approaches to single-cell data.
  arXiv  Detail & Related papers  (2023-10-23T13:35:24Z)
• A Neuro-mimetic Realization of the Common Model of Cognition via Hebbian Learning and Free Energy Minimization [55.11642177631929]
  Large neural generative models are capable of synthesizing semantically rich passages of text or producing complex images. We discuss the COGnitive Neural GENerative system, such an architecture that casts the Common Model of Cognition.
  arXiv  Detail & Related papers  (2023-10-14T23:28:48Z)
• Towards Large-Scale Simulations of Open-Ended Evolution in Continuous Cellular Automata [0.0]
  We build large-scale evolutionary simulations using parallel computing framework JAX. We report a number of system design choices, including implicit implementation of genetic operators. We propose several factors that may further facilitate open-ended evolution.
  arXiv  Detail & Related papers  (2023-04-12T06:40:11Z)
• 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)
• DeepRetinotopy: Predicting the Functional Organization of Human Visual Cortex from Structural MRI Data using Geometric Deep Learning [125.99533416395765]
  We developed a deep learning model capable of exploiting the structure of the cortex to learn the complex relationship between brain function and anatomy from structural and functional MRI data. Our model was able to predict the functional organization of human visual cortex from anatomical properties alone, and it was also able to predict nuanced variations across individuals.
  arXiv  Detail & Related papers  (2020-05-26T04:54:31Z)
• Brain-inspired self-organization with cellular neuromorphic computing for multimodal unsupervised learning [0.0]
  We propose a brain-inspired neural system based on the reentry theory using Self-Organizing Maps and Hebbian-like learning. We show the gain of the so-called hardware plasticity induced by the ReSOM, where the system's topology is not fixed by the user but learned along the system's experience through self-organization.
  arXiv  Detail & Related papers  (2020-04-11T21:02:45Z)

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.