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| | == 背景== | | == 背景== |
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| − | The notion that self-organising biological systems – like a cell or brain – can be understood as minimising variational free energy is based upon Helmholtz’s work on unconscious inference and subsequent treatments in psychology and machine learning. Variational free energy is a function of observations and a probability density over their hidden causes. This variational density is defined in relation to a probabilistic model that generates predicted observations from hypothesized causes. In this setting, free energy provides an approximation to Bayesian model evidence. Therefore, its minimisation can be seen as a Bayesian inference process. When a system actively makes observations to minimise free energy, it implicitly performs active inference and maximises the evidence for its model of the world.
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| − | 自我组织的生物系统——比如细胞或大脑——可以被理解为最小化变分自由能的概念，是基于亥姆霍兹在无意识推理以及随后的心理学和机器学习治疗方面的工作。变分自由能是观测值及其隐含原因的概率密度的函数。这个变分密度的定义关系到一个概率模型，从假设的原因产生预测观测。在这种情况下，自由能提供了一个近似贝叶斯模型的证据。因此，它的最小化可以被看作是一个贝叶斯推断过程。当一个系统积极地进行观测以最小化自由能时，它隐含地进行了积极推理并最大化其世界模型的证据。
| + | 自我组织的生物系统——比如细胞或大脑——可以被理解为最小化变分自由能的概念，是基于亥姆霍兹在无意识推理<ref name="Helmholtz">Helmholtz, H. (1866/1962). Concerning the perceptions in general. In Treatise on physiological optics (J. Southall, Trans., 3rd ed., Vol. III). New York: Dover.</ref>以及随后的心理学<ref>{{cite journal | title=Perceptions as hypotheses | journal=Philosophical Transactions of the Royal Society of London. B, Biological Sciences | publisher=The Royal Society | volume=290 | issue=1038 | date=1980-07-08 | issn=0080-4622 | doi=10.1098/rstb.1980.0090 | pmid=6106237 | bibcode=1980RSPTB.290..181G | pages=181–197|jstor=2395424| last1=Gregory | first1=R. L. | doi-access=free }}</ref>和机器学习<ref name="Dayan">{{cite journal | last1=Dayan | first1=Peter | last2=Hinton | first2=Geoffrey E. | last3=Neal | first3=Radford M. | last4=Zemel | first4=Richard S. | title=The Helmholtz Machine | journal=Neural Computation | publisher=MIT Press - Journals | volume=7 | issue=5 | year=1995 | issn=0899-7667 | doi=10.1162/neco.1995.7.5.889 | pmid=7584891 | pages=889–904| s2cid=1890561 |url=http://www.gatsby.ucl.ac.uk/~dayan/papers/hm95.pdf}}</ref>治疗方面的工作。变分自由能是观测值及其隐含原因的概率密度的函数。这个变分密度的定义关系到一个概率模型，从假设的原因产生预测观测。在这种情况下，自由能提供了一个近似贝叶斯模型<ref>Beal, M. J. (2003). [http://www.cse.buffalo.edu/faculty/mbeal/papers/beal03.pdf Variational Algorithms for Approximate Bayesian Inference]. Ph.D. Thesis, University College London.</ref>的证据。因此，它的最小化可以被看作是一个贝叶斯推断过程。当一个系统积极地进行观测以最小化自由能时，它隐含地进行了积极推理并最大化其世界模型的证据。 |
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| | + | 然而，自由能也是结果自信息的一个上限，长期的平均值是熵。这意味着，如果一个系统采取行动来最小化自由能，它将隐含地放置一个熵的结果-或感官状态-它的样本上限。<ref name="Friston">{{cite journal | last=Karl | first=Friston | title=A Free Energy Principle for Biological Systems | journal=Entropy | publisher=MDPI AG | volume=14 | issue=11 | date=2012-10-31 | issn=1099-4300 | doi=10.3390/e14112100 | pmid=23204829 | bibcode=2012Entrp..14.2100K | pages=2100–2121| pmc=3510653 |url=http://www.fil.ion.ucl.ac.uk/~karl/A%20Free%20Energy%20Principle%20for%20Biological%20Systems.pdf|doi-access=free}}</ref><ref>{{cite journal | last1=Colombo | first1=Matteo | last2=Wright | first2=Cory | title=First principles in the life sciences: the free-energy principle, organicism, and mechanism | journal=Synthese | publisher=Springer Science and Business Media LLC | date=2018-09-10 | issn=0039-7857 | doi=10.1007/s11229-018-01932-w | page=|doi-access=free}}</ref>{{Better source|date=February 2020|reason=MDPI is a questionable source}} |
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| − | The notion that [[self-organisation|self-organising]] biological systems – like a cell or brain – can be understood as minimising variational free energy is based upon [[Hermann von Helmholtz|Helmholtz]]’s work on [[unconscious inference]]<ref name="Helmholtz">Helmholtz, H. (1866/1962). Concerning the perceptions in general. In Treatise on physiological optics (J. Southall, Trans., 3rd ed., Vol. III). New York: Dover.</ref> and subsequent treatments in psychology<ref>{{cite journal | title=Perceptions as hypotheses | journal=Philosophical Transactions of the Royal Society of London. B, Biological Sciences | publisher=The Royal Society | volume=290 | issue=1038 | date=1980-07-08 | issn=0080-4622 | doi=10.1098/rstb.1980.0090 | pmid=6106237 | bibcode=1980RSPTB.290..181G | pages=181–197|jstor=2395424| last1=Gregory | first1=R. L. | doi-access=free }}</ref> and machine learning.<ref name="Dayan">{{cite journal | last1=Dayan | first1=Peter | last2=Hinton | first2=Geoffrey E. | last3=Neal | first3=Radford M. | last4=Zemel | first4=Richard S. | title=The Helmholtz Machine | journal=Neural Computation | publisher=MIT Press - Journals | volume=7 | issue=5 | year=1995 | issn=0899-7667 | doi=10.1162/neco.1995.7.5.889 | pmid=7584891 | pages=889–904| s2cid=1890561 |url=http://www.gatsby.ucl.ac.uk/~dayan/papers/hm95.pdf}}</ref> Variational free energy is a function of observations and a probability density over their hidden causes. This [[Calculus of variations|variational]] density is defined in relation to a probabilistic model that generates predicted observations from hypothesized causes. In this setting, free energy provides an approximation to [[Marginal likelihood|Bayesian model evidence]].<ref>Beal, M. J. (2003). [http://www.cse.buffalo.edu/faculty/mbeal/papers/beal03.pdf Variational Algorithms for Approximate Bayesian Inference]. Ph.D. Thesis, University College London.</ref> Therefore, its minimisation can be seen as a Bayesian inference process. When a system actively makes observations to minimise free energy, it implicitly performs active inference and maximises the evidence for its model of the world.
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| − | However, free energy is also an upper bound on the self-information of outcomes, where the long-term average of surprise is entropy. This means that if a system acts to minimise free energy, it will implicitly place an upper bound on the entropy of the outcomes – or sensory states – it samples.
| + | === 与其他理论的关系=== |
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| − | 然而，自由能也是结果自信息的一个上限，长期的平均值是熵。这意味着，如果一个系统采取行动来最小化自由能，它将隐含地放置一个熵的结果-或感官状态-它的样本上限。
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| − | However, free energy is also an upper bound on the [[self-information]] of outcomes, where the long-term average of [[Self-information|surprise]] is entropy. This means that if a system acts to minimise free energy, it will implicitly place an upper bound on the entropy of the outcomes – or sensory states – it samples.<ref name="Friston">{{cite journal | last=Karl | first=Friston | title=A Free Energy Principle for Biological Systems | journal=Entropy | publisher=MDPI AG | volume=14 | issue=11 | date=2012-10-31 | issn=1099-4300 | doi=10.3390/e14112100 | pmid=23204829 | bibcode=2012Entrp..14.2100K | pages=2100–2121| pmc=3510653 |url=http://www.fil.ion.ucl.ac.uk/~karl/A%20Free%20Energy%20Principle%20for%20Biological%20Systems.pdf|doi-access=free}}</ref><ref>{{cite journal | last1=Colombo | first1=Matteo | last2=Wright | first2=Cory | title=First principles in the life sciences: the free-energy principle, organicism, and mechanism | journal=Synthese | publisher=Springer Science and Business Media LLC | date=2018-09-10 | issn=0039-7857 | doi=10.1007/s11229-018-01932-w | page=|doi-access=free}}</ref>{{Better source|date=February 2020|reason=MDPI is a questionable source}}
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| − | === Relationship to other theories 与其他理论的关系===
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| | Active inference is closely related to the good regulator theorem and related accounts of self-organisation, such as self-assembly, pattern formation, autopoiesis and practopoiesis. It addresses the themes considered in cybernetics, synergetics and embodied cognition. Because free energy can be expressed as the expected energy of observations under the variational density minus its entropy, it is also related to the maximum entropy principle. Finally, because the time average of energy is action, the principle of minimum variational free energy is a principle of least action. | | Active inference is closely related to the good regulator theorem and related accounts of self-organisation, such as self-assembly, pattern formation, autopoiesis and practopoiesis. It addresses the themes considered in cybernetics, synergetics and embodied cognition. Because free energy can be expressed as the expected energy of observations under the variational density minus its entropy, it is also related to the maximum entropy principle. Finally, because the time average of energy is action, the principle of minimum variational free energy is a principle of least action. |