Hierarchy and dynamics of neural networks
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Structural and functional imaging studies in network neuroscience show that the brain entails a multiscale hierarchical organisation characterised by repeated encapsulation of smaller neural elements in larger ones.
“The HMM resonates with structural and functional imaging studies in network neuroscience, which show that the brain entails a multiscale hierarchical organisation characterised by the repeated encapsulation of smaller neural elements in larger ones ( [71,72]; Fig. 4, panel A).”
From Answering Schrödinger's question: A free-energy formulation (2018) · cites this paper as [71]
Systems at criticality have optimal information-processing capacities.
“Since systems at criticality have optimal information-processing capacities, a structure that extends this critical region is likely to be naturally selected [76].”
From The depressed brain: an evolutionary systems theory (2017) · cites this paper as [76]
A structure that widens the critical region is probably favoured by natural selection.
“Since systems at criticality have optimal information-processing capacities, a structure that extends this critical region is likely to be naturally selected [76].”
From The depressed brain: an evolutionary systems theory (2017) · cites this paper as [76]
The probability to have two giant connected components is vanishingly small.
“Notice that the probability to have 2 GCC is vanishingly small [18] and thus, with high probability”
From On the origins of hierarchy in complex networks (2013) · cites this paper as [18]
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The Reward of Musical Emotions and Expectations
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Text Classification via Learning Semantic Dependency and Association
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Anomalous Lifshitz dimension in hierarchical networks of brain connectivity
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The Role of Hierarchy in Learning to Categorize Images
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Modeling hierarchy using symbolic regression
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- Authors
- Kaiser, M.
- Affiliations
- Newcastle University (GB)
- Corresponding author
- Kaiser, Marcus
- ORCID
- Kaiser, Marcus
- Venue
- Frontiers in Neuroinformatics
- Publisher
- Frontiers Media SA
- Published
- 2010
- Volume
- 4
- ISSN
- 1662-5196 (ISSN-L)
- PMC
- PMC2938979
- OpenAlex
- W2084790688
- Cited by
- 136
- References
- 25
- Citations by year
- 2025: 1 · 2024: 6 · 2023: 7 · 2022: 7 · 2021: 7 · 2020: 35 · 2019: 8 · 2018: 4 · 2017: 10 · 2016: 7 · 2015: 10 · 2014: 10 · 2013: 8 · 2012: 7
- FWCI
- 4.66
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- cc-by
- Locations
- Frontiers in Neuroinformatics (publishedVersion, cc-by)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- OpenBU (Boston University) (submittedVersion, other-oa)
- Radboud Repository (Radboud University) (submittedVersion)
- Radboud Repository (Radboud University) (submittedVersion, other-oa)
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Neural dynamics and brain function
- Physical Sciences › Computer Science › Artificial Intelligence › Neural Networks and Applications
- Physical Sciences › Physics and Astronomy › Statistical and Nonlinear Physics › Complex Network Analysis Techniques
- Keywords
- artificial neural networks, hierarchy, dynamics, network dynamics
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