Active inference, morphogenesis, and computational psychiatry

Pio-Lopez, Léo; Kuchling, Franz; Tung, Angela; Pezzulo, Giovanni; Levin, Michael

doi:10.3389/fncom.2022.988977

Cited by 22 publications

(16 citation statements)

References 87 publications

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“…Thus, in our scheme, evolution used the communication system as a stress system that was activated when the memory of past events was not effective for predicting the future. This relates to the active inference framework and the free-energy principle, both of which have recently been developed to integrate homeostasis and allostasis [97], as applied to morphogenetic systems [98,99].…”

Section: Discussionmentioning

confidence: 99%

The scaling of goals from cellular to anatomical homeostasis: an evolutionary simulation, experiment and analysis

et al. 2023

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Complex living agents consist of cells, which are themselves competent sub-agents navigating physiological and metabolic spaces. Behaviour science, evolutionary developmental biology and the field of machine intelligence all seek to understand the scaling of biological cognition: what enables individual cells to integrate their activities to result in the emergence of a novel, higher-level intelligence with large-scale goals and competencies that belong to it and not to its parts? Here, we report the results of simulations based on the TAME framework, which proposes that evolution pivoted the collective intelligence of cells during morphogenesis of the body into traditional behavioural intelligence by scaling up homeostatic competencies of cells in metabolic space. In this article, we created a minimal in silico system (two-dimensional neural cellular automata) and tested the hypothesis that evolutionary dynamics are sufficient for low-level setpoints of metabolic homeostasis in individual cells to scale up to tissue-level emergent behaviour. Our system showed the evolution of the much more complex setpoints of cell collectives (tissues) that solve a problem in morphospace: the organization of a body-wide positional information axis (the classic French flag problem in developmental biology). We found that these emergent morphogenetic agents exhibit a number of predicted features, including the use of stress propagation dynamics to achieve the target morphology as well as the ability to recover from perturbation (robustness) and long-term stability (even though neither of these was directly selected for). Moreover, we observed an unexpected behaviour of sudden remodelling long after the system stabilizes. We tested this prediction in a biological system—regenerating planaria—and observed a very similar phenomenon. We propose that this system is a first step towards a quantitative understanding of how evolution scales minimal goal-directed behaviour (homeostatic loops) into higher-level problem-solving agents in morphogenetic and other spaces.

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Section: Discussionmentioning

confidence: 99%

The scaling of goals from cellular to anatomical homeostasis: an evolutionary simulation, experiment and analysis

et al. 2023

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“…That is, the internal states of such a system could be shown to be predicting the external causes of the sensory states and at the same time actively causing the external states through action. This formalism has already begun to be used to understand developmental patterning [150][151][152][153] . Our model is a first step towards a research program that quantitatively casts the embryonic neural plate-ectoderm circuit as an active inference system, where the discriminator genes infer the voltage pattern of the tissue via a layer of "blanket" genes (Fig.…”

Section: Discussionmentioning

confidence: 99%

Information integration during bioelectric regulation of morphogenesis in the embryonic frog brain

Manicka

Pai

Levin

2023

Preprint

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Spatiotemporal bioelectric states regulate multiple aspects of embryogenesis. A key open question concerns how specific multicellular voltage potential distributions differentially activate distinct downstream genes required for organogenesis. To understand the information processing mechanisms underlying the relationship between spatial bioelectric patterns, genetics, and morphology, we focused on a specific spatiotemporal bioelectric pattern in the Xenopus ectoderm that regulates embryonic brain patterning. We used machine learning to design a minimal but scalable bioelectric-genetic dynamical network model of embryonic brain morphogenesis that qualitatively recapitulated previous experimental observations. A causal integration analysis of the model revealed a simple higher-order spatiotemporal information integration mechanism relating the spatial bioelectric and gene expression patterns. Specific aspects of this mechanism include causal apportioning (certain cell positions are more important for collective decision making), informational asymmetry (depolarized cells are more influential than hyperpolarized cells), long distance influence (genes in a cell are variably sensitive to voltage of faraway cells), and division of labor (different genes are sensitive to different aspects of voltage pattern). The asymmetric information-processing character of the mechanism led the model to predict an unexpected degree of plasticity and robustness in the bioelectric prepattern that regulates normal embryonic brain development. Our in vivo experiments verified these predictions via molecular manipulations in Xenopus embryos. This work shows the power of using a minimal in silico approach to drastically reduce the parameter space in vivo, making hard biological questions tractable. These results provide insight into the collective decision-making process of cells in interpreting bioelectric pattens that guide large-scale morphogenesis, suggesting novel applications for biomedical interventions and new tools for synthetic bioengineering.

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“…Interestingly, these ideas are more familiar concepts in neuroscience; consistently with the recent advances in basal cognition [100][101][102][103][104], these concepts apply long before complex brains appear. My hope is that the following analyses extend an active research program on these ideas in regenerative medicine [58,[105][106][107][108][109] and synthetic bioengineering [37,101,[110][111][112][113] to also impact evolutionary biology and engineering of artificial systems via evolutionary algorithms [96,[114][115][116][117][118][119].…”

Section: Intelligence and Evolution: The Impact Of Competency At New ...mentioning

confidence: 99%

Darwin’s Agential Materials: evolutionary implications of multi-scale competency in developmental biology

Levin¹

2023

Preprint

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A critical aspect of evolution is the layer of developmental physiology that operates between the genotype and the anatomical phenotype. While much work has addressed the evolution of developmental mechanisms and the evolvability of specific genetic architectures with emergent complexity, one aspect has not been sufficiently explored: the implications of morphogenetic problem-solving competencies for the evolutionary process itself. The cells that evolution works with are not passive components: rather, they have numerous capabilities for behavior because they derive from ancestral unicellular organisms with rich repertoires. In multicellular organisms, these capabilities must be tamed, and can be exploited, by the evolutionary process. Specifically, biological structures have a multi-scale competency architecture where cells, tissues, and organs exhibit regulative plasticity - the ability to adjust to perturbations such as external injury or internal modifications and still accomplish specific adaptive tasks across metabolic, transcriptional, physiological, and anatomical problem spaces. Here, I review examples illustrating how physiological circuits guiding cellular collective behavior impart computational properties to the agential material that serves as substrate for the evolutionary process. I then explore the ways in which the collective intelligence of cells during morphogenesis affect evolution, providing a new perspective on the evolutionary search process. This key feature of the physiological software of life helps explain the remarkable speed and robustness of biological evolution, and sheds new light on the relationship between genomes and functional anatomical phenotypes.

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Active inference, morphogenesis, and computational psychiatry

Cited by 22 publications

References 87 publications

The scaling of goals from cellular to anatomical homeostasis: an evolutionary simulation, experiment and analysis

The scaling of goals from cellular to anatomical homeostasis: an evolutionary simulation, experiment and analysis

Information integration during bioelectric regulation of morphogenesis in the embryonic frog brain

Darwin’s Agential Materials: evolutionary implications of multi-scale competency in developmental biology

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