scholarly journals ‘Hierarchy’ in the organization of brain networks

2020 ◽  
Vol 375 (1796) ◽  
pp. 20190319 ◽  
Author(s):  
Claus C. Hilgetag ◽  
Alexandros Goulas
Keyword(s):  
Biological Networks ◽  
Brain Networks ◽  
Theme Issue ◽  
Philosophical Foundations ◽  
Systems Neuroscience ◽  
Functional Implications ◽  
Different Dimensions ◽  
Cortical System ◽  
Visual Cortical

Concepts shape the interpretation of facts. One of the most popular concepts in systems neuroscience is that of ‘hierarchy’. However, this concept has been interpreted in many different ways, which are not well aligned. This observation suggests that the concept is ill defined. Using the example of the organization of the primate visual cortical system, we explore several contexts in which ‘hierarchy’ is currently used in the description of brain networks. We distinguish at least four different uses, specifically, ‘hierarchy’ as a topological sequence of projections, as a gradient of features, as a progression of scales, or as a sorting of laminar projection patterns. We discuss the interpretation and functional implications of the different notions of ‘hierarchy’ in these contexts and suggest that more specific terms than ‘hierarchy’ should be used for a deeper understanding of the different dimensions of the organization of brain networks. This article is part of the theme issue ‘Unifying the essential concepts of biological networks: biological insights and philosophical foundations’.

scholarly journals General theory of topological explanations and explanatory asymmetry

2020 ◽  
Vol 375 (1796) ◽  
pp. 20190321 ◽  
Author(s):  
Daniel Kostić
Keyword(s):  
Biological Sciences ◽  
General Theory ◽  
Biological Networks ◽  
Topological Property ◽  
Dynamical Property ◽  
Second Step ◽  
Theme Issue ◽  
Philosophical Foundations ◽  
The Third ◽  
System A

In this paper, I present a general theory of topological explanations, and illustrate its fruitfulness by showing how it accounts for explanatory asymmetry. My argument is developed in three steps. In the first step, I show what it is for some topological property A to explain some physical or dynamical property B . Based on that, I derive three key criteria of successful topological explanations: a criterion concerning the facticity of topological explanations, i.e. what makes it true of a particular system; a criterion for describing counterfactual dependencies in two explanatory modes, i.e. the vertical and the horizontal and, finally, a third perspectival one that tells us when to use the vertical and when to use the horizontal mode. In the second step, I show how this general theory of topological explanations accounts for explanatory asymmetry in both the vertical and horizontal explanatory modes. Finally, in the third step, I argue that this theory is universally applicable across biological sciences, which helps in unifying essential concepts of biological networks. This article is part of the theme issue ‘Unifying the essential concepts of biological networks: biological insights and philosophical foundations'.

scholarly journals Network explanations and explanatory directionality

2020 ◽  
Vol 375 (1796) ◽  
pp. 20190318 ◽  
Author(s):  
Lina Jansson
Keyword(s):  
Biological Networks ◽  
Scientific Explanation ◽  
Network Models ◽  
Causal Models ◽  
Topological Properties ◽  
Theme Issue ◽  
Causal Explanations ◽  
Philosophical Foundations ◽  
Direction Of Causation

Network explanations raise foundational questions about the nature of scientific explanation. The challenge discussed in this article comes from the fact that network explanations are often thought to be non-causal, i.e. they do not describe the dynamical or mechanistic interactions responsible for some behaviour, instead they appeal to topological properties of network models describing the system. These non-causal features are often thought to be valuable precisely because they do not invoke mechanistic or dynamical interactions and provide insights that are not available through causal explanations. Here, I address a central difficulty facing attempts to move away from causal models of explanation; namely, how to recover the directionality of explanation. Within causal models, the directionality of explanation is identified with the direction of causation. This solution is no longer available once we move to non-causal accounts of explanation. I will suggest a solution to this problem that emphasizes the role of conditions of application. In doing so, I will challenge the idea that sui generis mathematical dependencies are the key to understand non-causal explanations. The upshot is a conceptual account of explanation that accommodates the possibility of non-causal network explanations. It also provides guidance for how to evaluate such explanations. This article is part of the theme issue ‘Unifying the essential concepts of biological networks: biological insights and philosophical foundations’.

scholarly journals Hierarchy and levels: analysing networks to study mechanisms in molecular biology

2020 ◽  
Vol 375 (1796) ◽  
pp. 20190320 ◽  
Author(s):  
William Bechtel
Keyword(s):  
Biological Networks ◽  
Large Scale ◽  
Biological Knowledge ◽  
Theme Issue ◽  
Epistatic Interactions ◽  
Philosophical Foundations ◽  
High Throughput Data ◽  
Cell Components ◽  
Large Scale Networks ◽  
Network Studies

Network representations are flat while mechanisms are organized into a hierarchy of levels, suggesting that the two are fundamentally opposed. I challenge this opposition by focusing on two aspects of the ways in which large-scale networks constructed from high-throughput data are analysed in systems biology: identifying clusters of nodes that operate as modules or mechanisms and using bio-ontologies such as gene ontology (GO) to annotate nodes with information about where entities appear in cells and the biological functions in which they participate. Of particular importance, GO organizes biological knowledge about cell components and functions hierarchically. I illustrate how this supports mechanistic interpretation of networks with two examples of network studies, one using epistatic interactions among genes to identify mechanisms and their parts and the other using deep learning to predict phenotypes. As illustrated in these examples, when network research draws upon hierarchical information such as provided by GO, the results not only can be interpreted mechanistically but provide new mechanistic knowledge. This article is part of the theme issue ‘Unifying the essential concepts of biological networks: biological insights and philosophical foundations’.

scholarly journals Unifying the essential concepts of biological networks: biological insights and philosophical foundations

2020 ◽  
Vol 375 (1796) ◽  
pp. 20190314
Author(s):  
Daniel Kostić ◽  
Claus C. Hilgetag ◽  
Marc Tittgemeyer
Keyword(s):  
Biological Sciences ◽  
Molecular Biology ◽  
Biological Networks ◽  
Network Science ◽  
Biological Network ◽  
Explanatory Power ◽  
Theme Issue ◽  
Philosophical Foundations ◽  
Key Concepts ◽  
Methodological Aspects

Over the last decades, network-based approaches have become highly popular in diverse fields of biology, including neuroscience, ecology, molecular biology and genetics. While these approaches continue to grow very rapidly, some of their conceptual and methodological aspects still require a programmatic foundation. This challenge particularly concerns the question of whether a generalized account of explanatory, organizational and descriptive levels of networks can be applied universally across biological sciences. To this end, this highly interdisciplinary theme issue focuses on the definition, motivation and application of key concepts in biological network science, such as explanatory power of distinctively network explanations, network levels and network hierarchies. This article is part of the theme issue ‘Unifying the essential concepts of biological networks: biological insights and philosophical foundations’.

scholarly journals Network architectures supporting learnability

2020 ◽  
Vol 375 (1796) ◽  
pp. 20190323 ◽  
Author(s):  
Perry Zurn ◽  
Danielle S. Bassett
Keyword(s):  
Biological Networks ◽  
Statistical Physics ◽  
Knowledge Network ◽  
Network Architectures ◽  
Philosophical Discussion ◽  
Hierarchical Networks ◽  
Integrated Network ◽  
Physical Constraints ◽  
Philosophical Foundations ◽  
Relational Knowledge

Human learners acquire complex interconnected networks of relational knowledge. The capacity for such learning naturally depends on two factors: the architecture (or informational structure) of the knowledge network itself and the architecture of the computational unit—the brain—that encodes and processes the information. That is, learning is reliant on integrated network architectures at two levels: the epistemic and the computational, or the conceptual and the neural. Motivated by a wish to understand conventional human knowledge, here, we discuss emerging work assessing network constraints on the learnability of relational knowledge, and theories from statistical physics that instantiate the principles of thermodynamics and information theory to offer an explanatory model for such constraints. We then highlight similarities between those constraints on the learnability of relational networks, at one level, and the physical constraints on the development of interconnected patterns in neural systems, at another level, both leading to hierarchically modular networks. To support our discussion of these similarities, we employ an operational distinction between the modeller (e.g. the human brain), the model (e.g. a single human’s knowledge) and the modelled (e.g. the information present in our experiences). We then turn to a philosophical discussion of whether and how we can extend our observations to a claim regarding explanation and mechanism for knowledge acquisition. What relation between hierarchical networks, at the conceptual and neural levels, best facilitate learning? Are the architectures of optimally learnable networks a topological reflection of the architectures of comparably developed neural networks? Finally, we contribute to a unified approach to hierarchies and levels in biological networks by proposing several epistemological norms for analysing the computational brain and social epistemes, and for developing pedagogical principles conducive to curious thought. This article is part of the theme issue ‘Unifying the essential concepts of biological networks: biological insights and philosophical foundations’.

scholarly journals Analysing brain networks in population neuroscience: a case for the Bayesian philosophy

2020 ◽  
Vol 375 (1796) ◽  
pp. 20190661 ◽  
Author(s):  
Danilo Bzdok ◽  
Dorothea L. Floris ◽  
Andre F. Marquand
Keyword(s):  
Biological Networks ◽  
Network Connectivity ◽  
Autism Spectrum ◽  
Single Subject ◽  
Model Parameters ◽  
Philosophical Foundations ◽  
Probability Estimates ◽  
Rich Information ◽  
Epistemological Uncertainty ◽  
Coupling Phenomena

Network connectivity fingerprints are among today's best choices to obtain a faithful sampling of an individual's brain and cognition. Widely available MRI scanners can provide rich information tapping into network recruitment and reconfiguration that now scales to hundreds and thousands of humans. Here, we contemplate the advantages of analysing such connectome profiles using Bayesian strategies. These analysis techniques afford full probability estimates of the studied network coupling phenomena, provide analytical machinery to separate epistemological uncertainty and biological variability in a coherent manner, usher us towards avenues to go beyond binary statements on existence versus non-existence of an effect, and afford credibility estimates around all model parameters at play which thus enable single-subject predictions with rigorous uncertainty intervals. We illustrate the brittle boundary between healthy and diseased brain circuits by autism spectrum disorder as a recurring theme where, we argue, network-based approaches in neuroscience will require careful probabilistic answers. This article is part of the theme issue ‘Unifying the essential concepts of biological networks: biological insights and philosophical foundations’.

scholarly journals As soon as there was life, there was danger: the deep history of survival behaviours and the shallower history of consciousness

2021 ◽  
Vol 377 (1844) ◽  
Author(s):  
Joseph E. LeDoux
Keyword(s):  
Common Sense ◽  
Survival Function ◽  
Point Of View ◽  
Theme Issue ◽  
Nervous Systems ◽  
Systems Neuroscience ◽  
History Of ◽  
Physiological Adjustments ◽  
Fear And Anxiety ◽  
Practical Implications

It is often said that fear is a universal innate emotion that we humans have inherited from our mammalian ancestors by virtue of having inherited conserved features of their nervous systems. Contrary to this common sense-based scientific point of view, I have argued that what we have inherited from our mammalian ancestors, and they from their distal vertebrate ancestors, and they from their chordate ancestors, and so forth, is not a fear circuit. It is, instead, a defensive survival circuit that detects threats, and in response, initiates defensive survival behaviours and supporting physiological adjustments. Seen in this light, the defensive survival circuits of humans and other mammals can be conceptualized as manifestations of an ancient survival function—the ability to detect danger and respond to it—that may in fact predate animals and their nervous systems, and perhaps may go back to the beginning of life. Fear, on the other hand, from my perspective, is a product of cortical cognitive circuits. This conception is not just of academic interest. It also has practical implications, offering clues as to why efforts to treat problems related to fear and anxiety are not more effective, and what might make them better. This article is part of the theme issue ‘Systems neuroscience through the lens of evolutionary theory’.

scholarly journals Computational validity: using computation to translate behaviours across species

2021 ◽  
Vol 377 (1844) ◽  
Author(s):  
A. David Redish ◽  
Adam Kepecs ◽  
Lisa M. Anderson ◽  
Olivia L. Calvin ◽  
Nicola M. Grissom ◽  
...  
Keyword(s):  
Information Processing ◽  
Psychiatric Disorders ◽  
Individual Variability ◽  
Point Of View ◽  
Theme Issue ◽  
Individual Level ◽  
Systems Neuroscience ◽  
Parallel Tasks ◽  
Superficial Similarity ◽  
Task Level

We propose a new conceptual framework (computational validity) for translation across species and populations based on the computational similarity between the information processing underlying parallel tasks. Translating between species depends not on the superficial similarity of the tasks presented, but rather on the computational similarity of the strategies and mechanisms that underlie those behaviours. Computational validity goes beyond construct validity by directly addressing questions of information processing. Computational validity interacts with circuit validity as computation depends on circuits, but similar computations could be accomplished by different circuits. Because different individuals may use different computations to accomplish a given task, computational validity suggests that behaviour should be understood through the subject's point of view; thus, behaviour should be characterized on an individual level rather than a task level. Tasks can constrain the computational algorithms available to a subject and the observed subtleties of that behaviour can provide information about the computations used by each individual. Computational validity has especially high relevance for the study of psychiatric disorders, given the new views of psychiatry as identifying and mediating information processing dysfunctions that may show high inter-individual variability, as well as for animal models investigating aspects of human psychiatric disorders. This article is part of the theme issue ‘Systems neuroscience through the lens of evolutionary theory’.

scholarly journals The neural bases of vertebrate motor behaviour through the lens of evolution

2021 ◽  
Vol 377 (1844) ◽  
Author(s):  
Shreyas M. Suryanarayana ◽  
Brita Robertson ◽  
Sten Grillner
Keyword(s):  
Central Pattern Generators ◽  
Phylogenetic Position ◽  
Neural Basis ◽  
Theme Issue ◽  
Systems Neuroscience ◽  
Vertebrate Brain ◽  
Primary Driver ◽  
Neural Bases ◽  
Pattern Generators ◽  
Ancestral Vertebrate

The primary driver of the evolution of the vertebrate nervous system has been the necessity to move, along with the requirement of controlling the plethora of motor behavioural repertoires seen among the vast and diverse vertebrate species. Understanding the neural basis of motor control through the perspective of evolution, mandates thorough examinations of the nervous systems of species in critical phylogenetic positions. We present here, a broad review of studies on the neural motor infrastructure of the lamprey, a basal and ancient vertebrate, which enjoys a unique phylogenetic position as being an extant representative of the earliest group of vertebrates. From the central pattern generators in the spinal cord to the microcircuits of the pallial cortex, work on the lamprey brain over the years, has provided detailed insights into the basic organization (a bauplan ) of the ancestral vertebrate brain, and narrates a compelling account of common ancestry of fundamental aspects of the neural bases for motion control, maintained through half a billion years of vertebrate evolution. This article is part of the theme issue ‘Systems neuroscience through the lens of evolutionary theory’.

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