ReviewPhilosophical transactions of the Royal Society of London. Series B, Biological sciences2010
Neuronal network analyses: premises, promises and uncertainties.
Review in Philosophical transactions of the Royal Society of London. Series B, Biological sciences, 2010. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
16 citing papers in PubMed.
- Optogenetics for light control of biological systems.Nature reviews. Methods primers · 2022Article
- Neurobiological reduction: From cellular explanations of behavior to interventions.Frontiers in psychology · 2022Review
- Vocabulary Repetition Following Multisensory Instruction Is Ineffective on L2 Sentence Comprehension: Evidence From the N400.Frontiers in psychology · 2022Article
- Multiple Two-Photon Targeted Whole-Cell Patch-Clamp Recordings From Monosynaptically Connected NeuronsFrontiers in synaptic neuroscience · 2019Article
- Neuromodulator interactions and spinal cord injury in lamprey.Neural regeneration research · 2018Article
- General Principles of Neuronal Co-transmission: Insights From Multiple Model Systems.Frontiers in neural circuits · 2018Review
- Kuhnian revolutions in neuroscience: the role of tool development.Biology & philosophy · 2018Article
- Evolutionary convergence and biologically embodied cognition.Interface focus · 2017Review
- The Lesioned Spinal Cord Is a "New" Spinal Cord: Evidence from Functional Changes after Spinal Injury in Lamprey.Frontiers in neural circuits · 2017Review
- Anatomical recovery of the spinal glutamatergic system following a complete spinal cord injury in lampreys.Scientific reports · 2016Article
- Short-Term Synaptic Plasticity at Interneuronal Synapses Could Sculpt Rhythmic Motor Patterns.Frontiers in neural circuits · 2016Article
- Changes in functional properties and 5-HT modulation above and below a spinal transection in lamprey.Frontiers in neural circuits · 2014Article
- Dynamic systems approaches and levels of analysis in the nervous system.Frontiers in physiology · 2013Article
- Neural tissue engineering using embryonic and induced pluripotent stem cells.Stem cell research & therapy · 2011Review
- Shining light into the black box of spinal locomotor networks.Philosophical transactions of the Royal Society of London. Series B, Biological sciences · 2010Article
- Moving and sensing without input and output: early nervous systems and the origins of the animal sensorimotor organization.Biology & philosophyArticle
Corrections and comments
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Authors and funding
1 author.
Funding
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Abstract
Neuronal networks assemble the cellular components needed for sensory, motor and cognitive functions. Any rational intervention in the nervous system will thus require an understanding of network function. Obtaining this understanding is widely considered to be one of the major tasks facing neuroscience today. Network analyses have been performed for some years in relatively simple systems. In addition to the direct insights these systems have provided, they also illustrate some of the difficulties of understanding network function. Nevertheless, in more complex systems (including human), claims are made that the cellular bases of behaviour are, or will shortly be, understood. While the discussion is necessarily limited, this issue will examine these claims and highlight some traditional and novel aspects of network analyses and their difficulties. This introduction discusses the criteria that need to be satisfied for network understanding, and how they relate to traditional and novel approaches being applied to addressing network function.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.