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    <title>Pinboard (mraginsky)</title>
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    <description>recent bookmarks from mraginsky</description>
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      <rdf:Seq>	<rdf:li rdf:resource="https://www.quantamagazine.org/why-do-we-tell-ourselves-scary-stories-about-ai-20260410/"/>
	<rdf:li rdf:resource="https://arxiv.org/abs/2602.23268"/>
	<rdf:li rdf:resource="https://link.springer.com/article/10.1007/s11084-016-9494-1"/>
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	<rdf:li rdf:resource="https://www.nature.com/articles/s41586-023-06728-8"/>
	<rdf:li rdf:resource="https://meson.press/books/uexkulls-surroundings/"/>
	<rdf:li rdf:resource="https://arxiv.org/abs/2406.04239"/>
	<rdf:li rdf:resource="https://link.springer.com/article/10.1007/BF02458575"/>
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	<rdf:li rdf:resource="https://link.springer.com/article/10.1007/s10867-010-9195-3"/>
	<rdf:li rdf:resource="https://arxiv.org/abs/2102.09204"/>
	<rdf:li rdf:resource="https://www.goodai.com/a-conversation-with-michael-levin/"/>
	<rdf:li rdf:resource="https://www.nybooks.com/articles/1978/10/12/the-illusion-of-sociobiology/"/>
	<rdf:li rdf:resource="https://www.pnas.org/content/116/21/10537"/>
	<rdf:li rdf:resource="https://www.pnas.org/content/70/10/2974"/>
	<rdf:li rdf:resource="https://sites.lsa.umich.edu/horowitz-lab/"/>
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	<rdf:li rdf:resource="http://www.springerlink.com/content/t7q7220241232886/"/>
	<rdf:li rdf:resource="http://arxiv.org/abs/1012.4863"/>
	<rdf:li rdf:resource="http://octavia.zoology.washington.edu/"/>
	<rdf:li rdf:resource="http://pandasthumb.org/archives/2005/08/shannon-informa.html"/>
	<rdf:li rdf:resource="http://chronicle.uchicago.edu/071004/limited-beings.shtml"/>
	<rdf:li rdf:resource="http://www.ncbi.nlm.nih.gov/geo/"/>
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	<rdf:li rdf:resource="http://www.sciam.com/article.cfm?id=are-aliens-among-us"/>
	<rdf:li rdf:resource="http://uanews.org/node/17028"/>
	<rdf:li rdf:resource="http://www.reason.com/news/show/123608.html"/>
	<rdf:li rdf:resource="http://scienceblogs.com/pharyngula/2006/02/an_updated_book_list_for_evolu.php"/>
	<rdf:li rdf:resource="http://gsp.tamu.edu/People/dougherty.html"/>
	<rdf:li rdf:resource="http://scienceblogs.com/developingintelligence/2007/03/why_the_brain_is_not_like_a_co.php"/>
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  </channel><item rdf:about="https://www.quantamagazine.org/why-do-we-tell-ourselves-scary-stories-about-ai-20260410/">
    <title>Why Do We Tell Ourselves Scary Stories About AI? | Quanta Magazine</title>
    <dc:date>2026-04-18T21:16:59+00:00</dc:date>
    <link>https://www.quantamagazine.org/why-do-we-tell-ourselves-scary-stories-about-ai-20260410/</link>
    <dc:creator>mraginsky</dc:creator><description><![CDATA[Our tales of AI developing the will to survive, commandeer resources, and manipulate people say more about us than they do about language models. ]]></description>
<dc:subject>have-read ai technology psychology enactivism biology evolution</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:mraginsky/b:7cff480427f6/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:have-read"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:ai"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:technology"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:psychology"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:enactivism"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:biology"/>
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<item rdf:about="https://arxiv.org/abs/2602.23268">
    <title>[2602.23268] The selfish ribosome</title>
    <dc:date>2026-03-04T19:58:55+00:00</dc:date>
    <link>https://arxiv.org/abs/2602.23268</link>
    <dc:creator>mraginsky</dc:creator><description><![CDATA[The ribosome is responsible for protein synthesis in all cells, and is the largest energy consumer in the cell. We propose that the ribosome originated as a mutualistic symbiont of an RNA-dependent RNA polymerase ribozyme, supplying peptides that enhanced replication. As life transitioned from the RNA to the RNA-protein world, autonomous replicators became irreversibly addicted to the ribosome for producing replication proteins. Subsequent evolution is construed as a ribosomal takeover, whereby the ribosome evolved to consume most of the resources of the cell, while other cellular componentry ensured the propagation of the ribosome. Under this perspective, the ribosome is the ultimate biological selfish element. ]]></description>
<dc:subject>papers to-read biology</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:mraginsky/b:458e18545cd4/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:papers"/>
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<item rdf:about="https://link.springer.com/article/10.1007/s11084-016-9494-1">
    <title>The Logic of Life | Discover Life</title>
    <dc:date>2026-01-08T21:32:48+00:00</dc:date>
    <link>https://link.springer.com/article/10.1007/s11084-016-9494-1</link>
    <dc:creator>mraginsky</dc:creator><description><![CDATA[In this paper we propose a logical connection between the physical and biological worlds, one resting on a broader understanding of the stability concept. We propose that stability manifests two facets - time and energy, and that stability’s time facet, expressed as persistence, is more general than its energy facet. That insight leads to the logical formulation of the Persistence Principle, which describes the general direction of material change in the universe, and which can be stated most simply as: nature seeks persistent forms. Significantly, the principle is found to express itself in two mathematically distinct ways: in the replicative world through Malthusian exponential growth, and in the ‘regular’ physical/chemical world through Boltzmann’s probabilistic considerations. By encompassing both ‘regular’ and replicative worlds, the principle appears to be able to help reconcile two of the major scientific theories of the 19th century – the Second Law of Thermodynamics and Darwin’s theory of evolution – within a single conceptual framework.]]></description>
<dc:subject>papers to-read biology evolution physics thermodynamics statistical-physics</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:mraginsky/b:2248c2f5d970/</dc:identifier>
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	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:evolution"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:physics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:thermodynamics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:statistical-physics"/>
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<item rdf:about="https://www.biorxiv.org/content/10.1101/2020.09.19.304584v1">
    <title>On the Mathematics of RNA Velocity I: Theoretical Analysis | bioRxiv</title>
    <dc:date>2024-12-09T19:23:32+00:00</dc:date>
    <link>https://www.biorxiv.org/content/10.1101/2020.09.19.304584v1</link>
    <dc:creator>mraginsky</dc:creator><description><![CDATA[The RNA velocity provides a new avenue to study the stemness and lineage of cells in the development in scRNA-seq data analysis. Some promising extensions of it are proposed and the community is experiencing a fast developing period. However, in this stage, it is of prime importance to revisit the whole process of RNA velocity analysis from the mathematical point of view, which will help to understand the rationale and drawbacks of different proposals. The current paper is devoted to this purpose. We present a thorough mathematical study on the RNA velocity model from dynamics to downstream data analysis. We derived the analytical solution of the RNA velocity model from both deterministic and stochastic point of view. We presented the parameter inference framework based on the maximum likelihood estimate. We also derived the continuum limit of different downstream analysis methods, which provides insights on the construction of transition probability matrix, root and endingcells identification, and the development routes finding. The overall analysis aims at providing a mathematical basis for more advanced design and development of RNA velocity type methods in the future.]]></description>
<dc:subject>papers to-read biology systems-biology cells genomics dynamical-systems</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:mraginsky/b:5e24c4613977/</dc:identifier>
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	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:genomics"/>
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<item rdf:about="https://www.nature.com/articles/s41586-023-06728-8">
    <title>Illuminating protein space with a programmable generative model | Nature</title>
    <dc:date>2024-08-12T19:53:05+00:00</dc:date>
    <link>https://www.nature.com/articles/s41586-023-06728-8</link>
    <dc:creator>mraginsky</dc:creator><description><![CDATA[Three billion years of evolution has produced a tremendous diversity of protein molecules1, but the full potential of proteins is likely to be much greater. Accessing this potential has been challenging for both computation and experiments because the space of possible protein molecules is much larger than the space of those likely to have functions. Here we introduce Chroma, a generative model for proteins and protein complexes that can directly sample novel protein structures and sequences, and that can be conditioned to steer the generative process towards desired properties and functions. To enable this, we introduce a diffusion process that respects the conformational statistics of polymer ensembles, an efficient neural architecture for molecular systems that enables long-range reasoning with sub-quadratic scaling, layers for efficiently synthesizing three-dimensional structures of proteins from predicted inter-residue geometries and a general low-temperature sampling algorithm for diffusion models. Chroma achieves protein design as Bayesian inference under external constraints, which can involve symmetries, substructure, shape, semantics and even natural-language prompts. The experimental characterization of 310 proteins shows that sampling from Chroma results in proteins that are highly expressed, fold and have favourable biophysical properties. The crystal structures of two designed proteins exhibit atomistic agreement with Chroma samples (a backbone root-mean-square deviation of around 1.0 Å). With this unified approach to protein design, we hope to accelerate the programming of protein matter to benefit human health, materials science and synthetic biology.]]></description>
<dc:subject>papers to-read generative-models diffusions proteins machine-learning biology</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:mraginsky/b:d0ca86cac042/</dc:identifier>
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	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:diffusions"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:proteins"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:machine-learning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:biology"/>
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<item rdf:about="https://meson.press/books/uexkulls-surroundings/">
    <title>Uexküll's Surroundings › meson press</title>
    <dc:date>2024-07-10T13:47:14+00:00</dc:date>
    <link>https://meson.press/books/uexkulls-surroundings/</link>
    <dc:creator>mraginsky</dc:creator><description><![CDATA[With its diversity of possible Umwelten or environments for living things, Jakob von Uexküll’s Umwelt theory has been hailed by many readers as the first step toward an innovative, pluralistic conception of nonhuman life. But what is generally ignored is its structural conservatism, its identitarian logic in which everything should remain in its place and nothing should mix, and its proximity to Nazi ideology and politics. By turning the spotlight on these neglected aspects, Uexküll’s Surroundings opens up a new perspective on Uexküll’s Umwelt theory.]]></description>
<dc:subject>books to-read politics philosophy biology history_of_ideas</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:mraginsky/b:2138fb100e9c/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:books"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:to-read"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:politics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:philosophy"/>
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	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:history_of_ideas"/>
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<item rdf:about="https://arxiv.org/abs/2406.04239">
    <title>[2406.04239] Solving Inverse Problems in Protein Space Using Diffusion-Based Priors</title>
    <dc:date>2024-07-06T02:46:01+00:00</dc:date>
    <link>https://arxiv.org/abs/2406.04239</link>
    <dc:creator>mraginsky</dc:creator><description><![CDATA[The interaction of a protein with its environment can be understood and controlled via its 3D structure. Experimental methods for protein structure determination, such as X-ray crystallography or cryogenic electron microscopy, shed light on biological processes but introduce challenging inverse problems. Learning-based approaches have emerged as accurate and efficient methods to solve these inverse problems for 3D structure determination, but are specialized for a predefined type of measurement. Here, we introduce a versatile framework to turn raw biophysical measurements of varying types into 3D atomic models. Our method combines a physics-based forward model of the measurement process with a pretrained generative model providing a task-agnostic, data-driven prior. Our method outperforms posterior sampling baselines on both linear and non-linear inverse problems. In particular, it is the first diffusion-based method for refining atomic models from cryo-EM density maps. ]]></description>
<dc:subject>papers to-read generative-models machine-learning diffusions proteins biology</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:mraginsky/b:0d00a0b54ed5/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:papers"/>
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	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:generative-models"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:machine-learning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:diffusions"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:proteins"/>
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<item rdf:about="https://link.springer.com/article/10.1007/BF02458575">
    <title>The cellular computer DNA: Program or data | SpringerLink</title>
    <dc:date>2023-05-07T20:07:53+00:00</dc:date>
    <link>https://link.springer.com/article/10.1007/BF02458575</link>
    <dc:creator>mraginsky</dc:creator><description><![CDATA[The classical metaphor of the genetic program written in the DNA nucleotidic sequences is reconsidered. Recent works on algorithmic complexity and logical properties of computer programs and data are used to question the explanatory value of that metaphor. Structural properties of strings are looked for which would be necessary to apply to DNA sequences if the metaphor is to be taken literally. The notion of sophistication is used to quantify meaningful complexity and to distinguish it from classical computational complexity. In this context, the distinction between program and data becomes relevant and an alternative metaphor of DNA as data to a parallel computing network embedded in the global geometrical and biochemical structure of the cell is discussed. An intermediate picture of an evolving network emerges as the most likely where the output of the cellular computing network can produce, at a different time scale, changes in the structure of the network itself by means of changes in the DNA activity patterns.]]></description>
<dc:subject>papers to-read biology genetics complexity computation</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:mraginsky/b:1520e370e1a6/</dc:identifier>
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	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:biology"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:genetics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:complexity"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:computation"/>
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</item>
<item rdf:about="https://www.sciencedirect.com/science/article/pii/0010027789900164">
    <title>Evolution, selection and cognition: From “learning” to parameter setting in biology and in the study of language - ScienceDirect</title>
    <dc:date>2022-08-21T14:17:19+00:00</dc:date>
    <link>https://www.sciencedirect.com/science/article/pii/0010027789900164</link>
    <dc:creator>mraginsky</dc:creator><description><![CDATA[Most biologists and some cognitive scientists have independently reached the conclusion that there is no such thing as learning in the traditional “instructive” sense. This is, admittedly, a somewhat extreme thesis, but I defend it herein the light of data and theories jointly extracted from biology, especially from evolutionary theory and immunology, and from modern generative grammar. I also point out that the general demise of learning is uncontroversial in the biological sciences, while a similar consensus has not yet been reached in psychology and in linguistics at large. Since many arguments presently offered in defense of learning and in defense of “general intelligence” are often based on a distorted picture of human biological evolution, I devote some sections of this paper to a critique of “adaptationism,” providing also a sketch of a better evolutionary theory (one based on “exaptation”). Moreover, since certain standard arguments presented today as “knock-down” in psychology, in linguistics and in artificial intelligence are a perfect replica of those once voiced]]></description>
<dc:subject>papers to-read cognitive-science biology evolution ai</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:mraginsky/b:f6757a8b64a4/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:papers"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:to-read"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:cognitive-science"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:biology"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:evolution"/>
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</item>
<item rdf:about="https://link.springer.com/article/10.1007/s10867-010-9195-3">
    <title>Information processing, computation, and cognition | SpringerLink</title>
    <dc:date>2022-08-03T17:15:40+00:00</dc:date>
    <link>https://link.springer.com/article/10.1007/s10867-010-9195-3</link>
    <dc:creator>mraginsky</dc:creator><description><![CDATA[Computation and information processing are among the most fundamental notions in cognitive science. They are also among the most imprecisely discussed. Many cognitive scientists take it for granted that cognition involves computation, information processing, or both – although others disagree vehemently. Yet different cognitive scientists use ‘computation’ and ‘information processing’ to mean different things, sometimes without realizing that they do. In addition, computation and information processing are surrounded by several myths; first and foremost, that they are the same thing. In this paper, we address this unsatisfactory state of affairs by presenting a general and theory-neutral account of computation and information processing. We also apply our framework by analyzing the relations between computation and information processing on one hand and classicism, connectionism, and computational neuroscience on the other. We defend the relevance to cognitive science of both computation, at least in a generic sense, and information processing, in three important senses of the term. Our account advances several foundational debates in cognitive science by untangling some of their conceptual knots in a theory-neutral way. By leveling the playing field, we pave the way for the future resolution of the debates’ empirical aspects.]]></description>
<dc:subject>papers to-read information-theory cognition computation biology neuroscience</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:mraginsky/b:580f27a1f05d/</dc:identifier>
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	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:information-theory"/>
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<item rdf:about="https://arxiv.org/abs/2102.09204">
    <title>[2102.09204] Towards a mathematical theory of trajectory inference</title>
    <dc:date>2022-08-02T16:28:29+00:00</dc:date>
    <link>https://arxiv.org/abs/2102.09204</link>
    <dc:creator>mraginsky</dc:creator><description><![CDATA[We devise a theoretical framework and a numerical method to infer trajectories of a stochastic process from snapshots of its temporal marginals. This problem arises in the analysis of single cell RNA-sequencing data, which provide high dimensional measurements of cell states but cannot track the trajectories of the cells over time. We prove that for a class of stochastic processes it is possible to recover the ground truth trajectories from limited samples of the temporal marginals at each time-point, and provide an efficient algorithm to do so in practice. The method we develop, Global Waddington-OT (gWOT), boils down to a smooth convex optimization problem posed globally over all time-points involving entropy-regularized optimal transport. We demonstrate that this problem can be solved efficiently in practice and yields good reconstructions, as we show on several synthetic and real datasets. ]]></description>
<dc:subject>papers to-read SDEs inference biology biophysics</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:mraginsky/b:745b4d3a4e47/</dc:identifier>
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	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:biology"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:biophysics"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://www.goodai.com/a-conversation-with-michael-levin/">
    <title>A Conversation with Michael Levin | GoodAI</title>
    <dc:date>2022-08-02T16:03:13+00:00</dc:date>
    <link>https://www.goodai.com/a-conversation-with-michael-levin/</link>
    <dc:creator>mraginsky</dc:creator><dc:subject>to-read blogs biology cybernetics ai complex-systems computation adaptive-systems agency</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:mraginsky/b:9de62de6e858/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:to-read"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:blogs"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:biology"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:cybernetics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:ai"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:complex-systems"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:computation"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:adaptive-systems"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:agency"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://www.nybooks.com/articles/1978/10/12/the-illusion-of-sociobiology/">
    <title>The Illusion of Sociobiology | Stuart Hampshire | The New York Review of Books</title>
    <dc:date>2022-08-02T15:55:24+00:00</dc:date>
    <link>https://www.nybooks.com/articles/1978/10/12/the-illusion-of-sociobiology/</link>
    <dc:creator>mraginsky</dc:creator><dc:subject>have-read book-reviews biology sociology anthropology philosophy-of-science epistemology</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:mraginsky/b:b5a910b7ce89/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:have-read"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:book-reviews"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:biology"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:sociology"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:anthropology"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:philosophy-of-science"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:epistemology"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://www.pnas.org/content/116/21/10537">
    <title>Fundamental bounds on learning performance in neural circuits | PNAS</title>
    <dc:date>2021-04-14T15:36:43+00:00</dc:date>
    <link>https://www.pnas.org/content/116/21/10537</link>
    <dc:creator>mraginsky</dc:creator><description><![CDATA[How does the size of a neural circuit influence its learning performance? Larger brains tend to be found in species with higher cognitive function and learning ability. Intuitively, we expect the learning capacity of a neural circuit to grow with the number of neurons and synapses. We show how adding apparently redundant neurons and connections to a network can make a task more learnable. Consequently, large neural circuits can either devote connectivity to generating complex behaviors or exploit this connectivity to achieve faster and more precise learning of simpler behaviors. However, we show that in a biologically relevant setting where synapses introduce an unavoidable amount of noise, there is an optimal size of network for a given task. Above the optimal network size, the addition of neurons and synaptic connections starts to impede learning performance. This suggests that the size of brain circuits may be constrained by the need to learn efficiently with unreliable synapses and provides a hypothesis for why some neurological learning deficits are associated with hyperconnectivity. Our analysis is independent of specific learning rules and uncovers fundamental relationships between learning rate, task performance, network size, and intrinsic noise in neural circuits.]]></description>
<dc:subject>papers to-read neuroscience neural-networks learning control-theory dynamical-systems biology</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:mraginsky/b:05d5179c4894/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:papers"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:to-read"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:neuroscience"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:neural-networks"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:learning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:control-theory"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:dynamical-systems"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:biology"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://www.pnas.org/content/70/10/2974">
    <title>A Theory of the Epigenesis of Neuronal Networks by Selective Stabilization of Synapses | PNAS</title>
    <dc:date>2021-04-14T15:13:41+00:00</dc:date>
    <link>https://www.pnas.org/content/70/10/2974</link>
    <dc:creator>mraginsky</dc:creator><description><![CDATA[A formalism is introduced to represent the connective organization of an evolving neuronal network and the effects of environment on this organization by stabilization or degeneration of labile synapses associated with functioning. Learning, or the acquisition of an associative property, is related to a characteristic variability of the connective organization: the interaction of the environment with the genetic program is printed as a particular pattern of such organization through neuronal functioning. An application of the theory to the development of the neuromuscular junction is proposed and the basic selective aspect of learning emphasized.]]></description>
<dc:subject>papers to-read neuroscience biology learning control-theory dynamical-systems neural-networks</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:mraginsky/b:0715924cac17/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:papers"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:to-read"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:neuroscience"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:biology"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:learning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:control-theory"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:dynamical-systems"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:neural-networks"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://sites.lsa.umich.edu/horowitz-lab/">
    <title>sites.lsa.umich.edu/horowitz-lab/</title>
    <dc:date>2021-01-02T04:23:09+00:00</dc:date>
    <link>https://sites.lsa.umich.edu/horowitz-lab/</link>
    <dc:creator>mraginsky</dc:creator><dc:subject>people homepages physics physics-of-information biology biophysics evolution</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:mraginsky/b:5ad387f372ee/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:people"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:homepages"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:physics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:physics-of-information"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:biology"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:biophysics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:evolution"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://www.frontiersin.org/articles/10.3389/fphys.2020.00200/full">
    <title>Frontiers | Homeostasis: The Underappreciated and Far Too Often Ignored Central Organizing Principle of Physiology | Physiology</title>
    <dc:date>2021-01-02T02:48:56+00:00</dc:date>
    <link>https://www.frontiersin.org/articles/10.3389/fphys.2020.00200/full</link>
    <dc:creator>mraginsky</dc:creator><description><![CDATA[The grand challenge to physiology, as was first described in an essay published in the inaugural issue of Frontiers in Physiology in 2010, remains to integrate function from molecules to intact organisms. In order to make sense of the vast volume of information derived from, and increasingly dependent upon, reductionist approaches, a greater emphasis must be placed on the traditional integrated and more holistic approaches developed by the scientists who gave birth to physiology as an intellectual discipline. Our understanding of physiological regulation has evolved over time from the Greek idea of body humors, through Claude Bernard’s “milieu intérieur,” to Walter Cannon’s formulation of the concept of “homeostasis” and the application of control theory (feedback and feedforward regulation) to explain how a constant internal environment is achieved. Homeostasis has become the central unifying concept of physiology and is defined as a self-regulating process by which an organism can maintain internal stability while adjusting to changing external conditions. Homeostasis is not static and unvarying; it is a dynamic process that can change internal conditions as required to survive external challenges. It is also important to note that homeostatic regulation is not merely the product of a single negative feedback cycle but reflects the complex interaction of multiple feedback systems that can be modified by higher control centers. This hierarchical control and feedback redundancy results in a finer level of control and a greater flexibility that enables the organism to adapt to changing environmental conditions. The health and vitality of the organism can be said to be the end result of homeostatic regulation. An understanding of normal physiology is not possible without an appreciation of this concept. Conversely, it follows that disruption of homeostatic mechanisms is what leads to disease, and effective therapy must be directed toward re-establishing these homeostatic conditions. Therefore, it is the purpose of this essay to describe the evolution of our understanding of homeostasis and the role of physiological regulation and dysregulation in health and disease.
]]></description>
<dc:subject>to-read papers cybernetics physiology biology control-theory dynamical-systems</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:mraginsky/b:70dfcf02ea25/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:to-read"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:papers"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:cybernetics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:physiology"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:biology"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:control-theory"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:dynamical-systems"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://www.springerlink.com/content/t7q7220241232886/">
    <title>The Value of Information for Populations in Varying Environments (Olivier Rivoire and Stanislas Leibler)</title>
    <dc:date>2011-06-10T17:41:04+00:00</dc:date>
    <link>http://www.springerlink.com/content/t7q7220241232886/</link>
    <dc:creator>mraginsky</dc:creator><description><![CDATA[the journal version
]]></description>
<dc:subject>papers have-read information-theory biology decision-making evolution dynamical-systems control-theory</dc:subject>
<dc:identifier>https://pinboard.in/u:mraginsky/b:f3e2fa6afc5e/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:papers"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:have-read"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:information-theory"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:biology"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:decision-making"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:evolution"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:dynamical-systems"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:control-theory"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/1012.4863">
    <title>[1012.4863] Dynamical quorum-sensing and synchronization of nonlinear oscillators coupled through an external medium</title>
    <dc:date>2011-01-03T23:30:00+00:00</dc:date>
    <link>http://arxiv.org/abs/1012.4863</link>
    <dc:creator>mraginsky</dc:creator><description><![CDATA["Many biological and physical systems exhibit population-density dependent transitions to synchronized oscillations in a process often termed "dynamical quorum sensing". Synchronization frequently arises through chemical communication via signaling molecules distributed through an external media. We study a simple theoretical model for dynamical quorum sensing: a heterogenous population of limit-cycle oscillators diffusively coupled through a common media. We show that this model exhibits a rich phase diagram with four qualitatively distinct mechanisms fueling population-dependent transitions to global oscillations, including a new type of transition we term "dynamic death". We derive a single pair of analytic equations that allows us to calculate all phase boundaries as a function of population density and show that the model reproduces many of the qualitative features of recent experiments of BZ catalytic particles as well as synthetically engineered bacteria."
]]></description>
<dc:subject>papers to-read biology dynamical-systems cells control-theory feedback</dc:subject>
<dc:identifier>https://pinboard.in/u:mraginsky/b:9f7d470791f6/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:papers"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:to-read"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:biology"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:dynamical-systems"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:cells"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:control-theory"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:feedback"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://octavia.zoology.washington.edu/">
    <title>Carl T. Bergstrom - University of Washington</title>
    <dc:date>2010-08-15T15:57:57+00:00</dc:date>
    <link>http://octavia.zoology.washington.edu/</link>
    <dc:creator>mraginsky</dc:creator><description><![CDATA["Information in biological systems. How do living organisms acquire, store, and make use of information? How and why does communication evolve? How does information flow through biological or social networks?"
]]></description>
<dc:subject>people homepages research information-theory biology theoretical-biology communication social-networks</dc:subject>
<dc:identifier>https://pinboard.in/u:mraginsky/b:96c0824d03b5/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:people"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:homepages"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:research"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:information-theory"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:biology"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:theoretical-biology"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:communication"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:social-networks"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://pandasthumb.org/archives/2005/08/shannon-informa.html">
    <title>Shannon Information and Biological Fitness - The Panda's Thumb</title>
    <dc:date>2009-09-07T17:05:37+00:00</dc:date>
    <link>http://pandasthumb.org/archives/2005/08/shannon-informa.html</link>
    <dc:creator>mraginsky</dc:creator><description><![CDATA[Panda's Thumb says: "The reason why I am excited about these findings is that they tie together: scale free networks, Shannon information, criticality and evolution in a theoretic foundation." As an information theorist, I'm skeptical about this: to us, mutual information is just a number, unless you attach to it an operational meaning and have a coding theorem to go with it. But I will take a look.
]]></description>
<dc:subject>to-read biology evolution information-theory blogs</dc:subject>
<dc:identifier>https://pinboard.in/u:mraginsky/b:1f77cd8713fc/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:to-read"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:biology"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:evolution"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:information-theory"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:blogs"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://chronicle.uchicago.edu/071004/limited-beings.shtml">
    <title>Philosophy for ‘limited beings’ accommodates approximations</title>
    <dc:date>2008-07-28T14:54:01+00:00</dc:date>
    <link>http://chronicle.uchicago.edu/071004/limited-beings.shtml</link>
    <dc:creator>mraginsky</dc:creator><description><![CDATA[Review of Re-engineering Philosophy for Limited Beings: Piecewise Approximations to Reality by William Wimsatt (Harvard Press, 2007)
]]></description>
<dc:subject>books philosophy science interesting book-reviews to-read epistemology biology evolution</dc:subject>
<dc:identifier>https://pinboard.in/u:mraginsky/b:56e500b98b4c/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:books"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:philosophy"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:science"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:interesting"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:book-reviews"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:to-read"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:epistemology"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:biology"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:evolution"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://www.ncbi.nlm.nih.gov/geo/">
    <title>Gene Expression Omnibus (GEO) Main page</title>
    <dc:date>2008-06-23T17:59:17+00:00</dc:date>
    <link>http://www.ncbi.nlm.nih.gov/geo/</link>
    <dc:creator>mraginsky</dc:creator><dc:subject>biology genomics statistics data-sets</dc:subject>
<dc:identifier>https://pinboard.in/u:mraginsky/b:354916229fd2/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:biology"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:genomics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:data-sets"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://www.tnr.com/story_print.html?id=d8731cf4-e87b-4d88-b7e7-f5059cd0bfbd">
    <title>The Stupidity of Dignity</title>
    <dc:date>2008-05-14T04:42:24+00:00</dc:date>
    <link>http://www.tnr.com/story_print.html?id=d8731cf4-e87b-4d88-b7e7-f5059cd0bfbd</link>
    <dc:creator>mraginsky</dc:creator><description><![CDATA[Steven Pinker on the theocons in the President's Council on Bioethics
]]></description>
<dc:subject>biology politics religion science polemics philosophy bioethics</dc:subject>
<dc:identifier>https://pinboard.in/u:mraginsky/b:1a13ffc29ead/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:biology"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:politics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:religion"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:science"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:polemics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:philosophy"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:bioethics"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://www.sciam.com/article.cfm?id=are-aliens-among-us">
    <title>Are Aliens Among Us?: Scientific American</title>
    <dc:date>2007-11-24T22:18:57+00:00</dc:date>
    <link>http://www.sciam.com/article.cfm?id=are-aliens-among-us</link>
    <dc:creator>mraginsky</dc:creator><dc:subject>biology evolution science interesting speculation</dc:subject>
<dc:identifier>https://pinboard.in/u:mraginsky/b:0f4672772125/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:biology"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:evolution"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:science"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:interesting"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:speculation"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://uanews.org/node/17028">
    <title>'Speed of Thought' Guides Brain Memory Consolidation | UANews</title>
    <dc:date>2007-11-23T15:00:27+00:00</dc:date>
    <link>http://uanews.org/node/17028</link>
    <dc:creator>mraginsky</dc:creator><description><![CDATA[UA researchers find that the brain processes memories six to seven times faster than real time.
]]></description>
<dc:subject>biology interesting research science neuroscience</dc:subject>
<dc:identifier>https://pinboard.in/u:mraginsky/b:2f9ade2c9c73/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:biology"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:interesting"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:research"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:science"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:neuroscience"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://www.reason.com/news/show/123608.html">
    <title>The Theory of Moral Neuroscience</title>
    <dc:date>2007-11-23T03:29:44+00:00</dc:date>
    <link>http://www.reason.com/news/show/123608.html</link>
    <dc:creator>mraginsky</dc:creator><dc:subject>biology neuroscience research science interesting sociology</dc:subject>
<dc:identifier>https://pinboard.in/u:mraginsky/b:1ed5986c7270/</dc:identifier>
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