The free energy principle induces neuromorphic development

被引:9
作者
Fields, Chris [1 ,2 ]
Friston, Karl [3 ]
Glazebrook, James F. [4 ,5 ]
Levin, Michael [2 ,6 ]
Marciano, Antonino [7 ,8 ,9 ]
机构
[1] 23 Rue Lavandieres, F-11160 Caunes Minervois, France
[2] Tufts Univ, Allen Discovery Ctr, Medford, MA 02155 USA
[3] UCL, Wellcome Ctr Human Neuroimaging, London WC1N 3AR, England
[4] Eastern Illinois Univ, Dept Math & Comp Sci, Charleston, IL 61920 USA
[5] Univ Illinois, Adjunct Fac, Dept Math, Urbana, IL 61801 USA
[6] Harvard Univ, Wyss Inst Biolog Inspired Engn, Wyss Inst Biolog Inspired Engn, Boston, MA 02115 USA
[7] Fudan Univ, Ctr Field Theory & Particle Phys, Dept Phys, Shanghai, Peoples R China
[8] Lab Nazl Frascati INFN, Rome, Italy
[9] INFN Sez Roma Tor Vergata, I-00133 Rome, Italy
来源
NEUROMORPHIC COMPUTING AND ENGINEERING | 2022年 / 2卷 / 04期
基金
欧盟地平线“2020”;
关键词
Bayesian active inference; generative model; quantum reference frame; tomographic measurement; topological quantum neural network; STATE-SUM INVARIANTS; GRID CELLS; INFORMATION; COMPUTATION; HIPPOCAMPUS; SEQUENCES; MEMORY; GROWTH; EMERGENCE; COGNITION;
D O I
10.1088/2634-4386/aca7de
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
We show how any finite physical system with morphological, i.e. three-dimensional embedding or shape, degrees of freedom and locally limited free energy will, under the constraints of the free energy principle, evolve over time towards a neuromorphic morphology that supports hierarchical computations in which each 'level' of the hierarchy enacts a coarse-graining of its inputs, and dually, a fine-graining of its outputs. Such hierarchies occur throughout biology, from the architectures of intracellular signal transduction pathways to the large-scale organization of perception and action cycles in the mammalian brain. The close formal connections between cone-cocone diagrams (CCCD) as models of quantum reference frames on the one hand, and between CCCDs and topological quantum field theories on the other, allow the representation of such computations in the fully-general quantum-computational framework of topological quantum neural networks.
引用
收藏
页数:23
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