↓ Skip to main content

Selectionist and Evolutionary Approaches to Brain Function: A Critical Appraisal

Overview of attention for article published in Frontiers in Computational Neuroscience, January 2012
Altmetric Badge

About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (91st percentile)
  • High Attention Score compared to outputs of the same age and source (81st percentile)

Mentioned by

blogs
1 blog
twitter
3 X users
wikipedia
1 Wikipedia page

Readers on

mendeley
128 Mendeley
citeulike
2 CiteULike
You are seeing a free-to-access but limited selection of the activity Altmetric has collected about this research output. Click here to find out more.
Title
Selectionist and Evolutionary Approaches to Brain Function: A Critical Appraisal
Published in
Frontiers in Computational Neuroscience, January 2012
DOI 10.3389/fncom.2012.00024
Pubmed ID
Authors

Chrisantha Fernando, Eörs Szathmáry, Phil Husbands

Abstract

We consider approaches to brain dynamics and function that have been claimed to be Darwinian. These include Edelman's theory of neuronal group selection, Changeux's theory of synaptic selection and selective stabilization of pre-representations, Seung's Darwinian synapse, Loewenstein's synaptic melioration, Adam's selfish synapse, and Calvin's replicating activity patterns. Except for the last two, the proposed mechanisms are selectionist but not truly Darwinian, because no replicators with information transfer to copies and hereditary variation can be identified in them. All of them fit, however, a generalized selectionist framework conforming to the picture of Price's covariance formulation, which deliberately was not specific even to selection in biology, and therefore does not imply an algorithmic picture of biological evolution. Bayesian models and reinforcement learning are formally in agreement with selection dynamics. A classification of search algorithms is shown to include Darwinian replicators (evolutionary units with multiplication, heredity, and variability) as the most powerful mechanism for search in a sparsely occupied search space. Examples are given of cases where parallel competitive search with information transfer among the units is more efficient than search without information transfer between units. Finally, we review our recent attempts to construct and analyze simple models of true Darwinian evolutionary units in the brain in terms of connectivity and activity copying of neuronal groups. Although none of the proposed neuronal replicators include miraculous mechanisms, their identification remains a challenge but also a great promise.

X Demographics

X Demographics

The data shown below were collected from the profiles of 3 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 128 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United Kingdom 4 3%
France 4 3%
Germany 2 2%
United States 2 2%
Canada 2 2%
Switzerland 1 <1%
Unknown 113 88%

Demographic breakdown

Readers by professional status Count As %
Researcher 25 20%
Student > Master 20 16%
Student > Ph. D. Student 18 14%
Professor > Associate Professor 14 11%
Student > Bachelor 14 11%
Other 23 18%
Unknown 14 11%
Readers by discipline Count As %
Computer Science 30 23%
Agricultural and Biological Sciences 19 15%
Neuroscience 15 12%
Psychology 14 11%
Physics and Astronomy 7 5%
Other 25 20%
Unknown 18 14%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 12. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 23 May 2022.
All research outputs
#2,791,845
of 23,822,306 outputs
Outputs from Frontiers in Computational Neuroscience
#119
of 1,384 outputs
Outputs of similar age
#22,033
of 248,954 outputs
Outputs of similar age from Frontiers in Computational Neuroscience
#14
of 70 outputs
Altmetric has tracked 23,822,306 research outputs across all sources so far. Compared to these this one has done well and is in the 88th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 1,384 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 6.7. This one has done particularly well, scoring higher than 91% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 248,954 tracked outputs that were published within six weeks on either side of this one in any source. This one has done particularly well, scoring higher than 91% of its contemporaries.
We're also able to compare this research output to 70 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 81% of its contemporaries.