paper:doi-10-1002-wsbm-1410Multiscale memory and bioelectric error correction in the cytoplasm–cytoskeleton‐membrane system
Original abstract (expand)
A fundamental aspect of life is the modification of anatomy, physiology, and behavior in the face of changing conditions. This is especially illustrated by the adaptive regulation of growth and form that underlies the ability of most organisms-from single cells to complex large metazoa-to develop, remodel, and regenerate to specific anatomical patterns. What is the relationship of the genome and other cellular components to the robust computations that underlie this remarkable pattern homeostasis? Here we examine the role of constraints defined at the cellular level, especially endogenous bioelectricity, in generating and propagating biological information. We review evidence that the genome is only one of several multi-generational biological memories. Focusing on the cell membrane and cytoplasm, which is physically continuous across all of life in evolutionary timeframes, we characterize the environment as an interstitial space through which messages are passed via bioelectric and biochemical codes. We argue that biological memory is a fundamental phenomenon that cannot be understood at any one scale, and suggest that functional studies of information propagated in non-genomic cellular structures will not only strongly impact evolutionary developmental biology, but will also have implications for regenerative medicine and synthetic bioengineering. WIREs Syst Biol Med 2018, 10:e1410. doi: 10.1002/wsbm.1410 This article is categorized under: Developmental Biology > Stem Cell Biology and Regeneration Physiology > Physiology of Model Organisms Models of Systems Properties and Processes > Cellular Models.
Related work— refs + corpus + external arXiv
Cited / in-corpus / arXiv badges show which signals surfaced each row. Multi-source rows weighted higher.
- Remapping and navigation of an embedding space via error minimization: a fundamental organizational principle of cognition in natural and artificial systemsL\'eo Pio-Lopez, Chris Fields, Michael Levin Benedikt Hartl2026≈ 74%
- Developmental Bioelectricity: the cognitive glue enabling evolutionary scaling from physiology to mindin corpus2023≈ 74%
- The computational boundary of a 'self': developmental bioelectricity drives multicellularity and scale-free cognitionin corpus2019≈ 74%
- Bioelectrical Interfaces Beyond Cellular Excitability: Cancer, Aging, and Gene Expression ReprogrammingMatthew Burgess, Catarina Franco Jones, Titouan Luciani, Marzia Iarossi, Manuel Schr\"oter, Nako Nakatsuka, Mustafa B. A. Djamgoz, Gil Gon\c{c}alves, Paola Sanju\'an-Alberte, Paula M. Mendes, Frankie J. Rawson, Malavika Nair, Michael Levin, Rosalia Moreddu Paolo Cadinu2025≈ 73%
- Learning without neurons in physical systemsin corpus2022≈ 73%
- AI-driven control of bioelectric signalling for real-time topological reorganization of cellsGon\c{c}alo Hora de Carvalho2025≈ 72%
- Collective intelligence: A unifying concept for integrating biology across scales and substratesin corpus2024≈ 72%
- State-dependent brain responsiveness, from local circuits to the whole brainJ Goldman, N. Tort-Colet, A. Roques, J. Fousek, S. Petkoski, V. Jirsa, O. David, M. Jedynak, C. Capone, C. De Luca, G. De Bonis, P.S. Paolucci, E. Mikulan, Pigorini, M Massimini, A. Galluzzi, A. Pazienti, M. Mattia, A. Arena, B. E. Juel, E. Hagen, J.F. Storm, E. Montagni, F. Resta, F. S. Pavone, A. L. Allegra Mascaro, A. Dwarakanath, T. I. Panagiotaropoulos, J. Senk, M. Diesmann, A. Camassa, L. Dalla Porta, A. Manasanch, M.V. Sanchez-Vives A. Destexhe2025≈ 72%
- Microelectronic Morphogenesis: Progress towards Artificial OrganismsDaniil Karnaushenko, Minshen Zhu and Oliver G. Schmidt John S. McCaskill2024≈ 72%
- A Kolmogorov metric embedding for live cell microscopy signaling patternsMark Winter, Marc DeCarlo, Agne Frismantiene, Yannick Blum, Paolo Armando Gagliardi, Olivier Pertz, Andrew R. Cohen Layton Aho2026≈ 71%
- The scaling of goals via homeostasis: an evolutionary simulation, experiment and analysisJohanna Bischof, Jennifer V. LaPalme, and Michael Levin Leo Pio-Lopez2022≈ 71%
- ≈ 71%
- Neural mechanisms of predictive processing: a collaborative community experiment through the OpenScope programNicholas Audette, Ryszard Auksztulewicz, Krzysztof Basi\'nski, Andr\'e M. Bastos, Michael Berry, Andres Canales-Johnson, Hannah Choi, Claudia Clopath, Uri Cohen, Rui Ponte Costa, Roberto De Filippo, Roman Doronin, S\'everine Durand, Steven P. Errington, Jeffrey P. Gavornik, Colleen J. Gillon, Arno Granier, Jordan P. Hamm, Loreen Hert\"ag, Henry Kennedy, Sandeep Kumar, Alexander Ladd, Hugo Ladret, J\'er\^ome A. Lecoq, Alexander Maier, Patrick McCarthy, Jie Mei, Jorge Mejias, John Hongyu Meng, Fabian Mikulasch, Noga Mudrik, Farzaneh Najafi, Kevin Nejad, Hamed Nejat, Karim Oweiss, Mihai A. Petrovici, Viola Priesemann, Lucas Rudelt, Sarah Ruediger, Simone Russo, Alessandro Salatiello, Walter Senn, Eli Sennesh, Sepehr Sima, Cem Uran, Anna Vasilevskaya, Julien Vezoli, Martin Vinck, Xiao-Jing Wang, Jacob A. Westerberg, Katharina Wilmes, Yihan Sophy Xiong Ido Aizenbud2026≈ 71%
- Bio-Inspired Artificial Neural Networks based on Predictive CodingCharlotte Frenkel, Justin Dauwels Davide Casnici2025≈ 71%
- Darwin's agential materials: evolutionary implications of multiscale competency in developmental biologyin corpus2023≈ 71%
- Modeling Bioelectric State Transitions in Glial Cells: An ASAL-Inspired Computational Approach to Glioblastoma InitiationWiktoria Agata Pawlak2025≈ 71%
- The biogenic approach to cognitionin corpus2005≈ 70%
- A minimal model of cognition based on oscillatory and current-based reinforcement processesYu Tian, and David J.T. Sumpter Linn\'ea Gyllingberg2024≈ 70%
- ≈ 70%
- The DIME Architecture: A Unified Operational Algorithm for Neural Representation, Dynamics, Control and IntegrationNicu Bizdoaca, Ionica Pirici, Tudor-Adrian Balseanu, Eduard Nicusor Bondoc Ionel Cristian Vladu2026≈ 70%
- ≈ 70%
- Model Alignment Searchin corpus2025≈ 70%
- Solving the Advection-Diffusion Equations in Biological Contexts using the Cellular Potts ModelChris Mueller, Kun Chen, James A. Glazier Debasis Dan2007≈ 70%
- ≈ 70%
- Self-Improvising Memory: A Perspective on Memories as Agential, Dynamically Reinterpreting Cognitive Gluein corpus2024≈ 69%
- ≈ 69%
- ≈ 69%
- ≈ 69%
- Active Inference: A Process Theoryin corpus2017≈ 69%
- ≈ 68%
Similar preprints — Semantic Scholar
Cited by (3)
- Darwin's agential materials: evolutionary implications of multiscale competency in developmental biology
Cellular collectives operating between the genotype and anatomical phenotype constitute an agential substrate that fundamentally reshapes the evolutionary search process—this is the central claim of L
- Technological Approach to Mind Everywhere: An Experimentally-Grounded Framework for Understanding Diverse Bodies and Minds
TAME—Technological Approach to Mind Everywhere—formalizes a non-binary, empirically grounded framework for recognizing, comparing, and manipulating cognition across radically diverse substrates, from
- Living Things Are Not (20th Century) Machines: Updating Mechanism Metaphors in Light of the Modern Science of Machine Behavior
Bongard and Levin argue that the longstanding debate over whether living things are machines has been conducted against a 20th-century, static definition of 'machine' that modern engineering has alrea