Principles of brain evolution.

By G. Striedter. 2005

1,113 citations recorded by the source corpus.

Part 1: Evolutionary Neuroscience: This Book's Scope And Ambition The Book's Scope and Major Themes Part 2: A Historyof Comparative Neurobiology The Birth of Comparative Neuroanatomy Darwin's Time: The Owen-Huxley Debate The Era of Comparative Cytoarchitectonics Comparative Hodology and Histochemistry The Rise of Neurocladistics The Rejuvenation of Comparative Neuroembryology Conclusions Part 3: Conservation In Vertebrate Brains A "Who's Who" of Vertebrates Comparing Adult Brains Adult Brain Regions Adult Cell Types Neuron-typical Molecules Comparing Embryonic Brains The Neuromeric Model Criticisms of the Neuromeric Model Mapping Embryos onto Adults Conclusion Part 4: Evolutionary Changes In Overall Brain Size Changes in Relative Brain Size Mechanisms of Brain-body Scaling Functional Correlates of Relative Brain Size Changes in Absolute Brain Size Constraints and Compromises Conclusions Part 5: Evolutionary Changes In Brain Region Size Concerted versus Mosaic Evolution Concerted Evolution Mosaic Evolution Toward a Synthesis Functional Correlates of Brain Region Size The Principle of Proper Mass Absolute Size and Functional Capacity Proportional Size and Influence Relative Size and Adaptation Synthesis: The Avian Hippocampus Conclusions Part 6: Evolutionary Changes In Brain Region Structure Homology and Novelty Phylogenetic Conversion: Lamination Phylogenetic Proliferation: Segregation Phylogenetic Proliferation: Addition Conclusions Part 7: Evolution Of Neuronal Connectivity Epigenetic Population Matching and Cascades The Parcellation Hypothesis Connectional invasion and Its Consequences General Principles of Network Design Synthesis and Conclusions Part 8: What's Special About Mammal Brains? Early Mammals and their Brains The Phylogenetic History of Neocortex Beyond the Neocortex Conclusion Part 9: What's Special About Human Brains? Primate Behavior and Overall Brain Size Evolutionary Changes in Primate Brain Organization Hominin Behavior and Overall Brain Size Evolutionary Changes in Hominin Brain Organization Conclusions Part 10: What's Special About Human Brains? Explanatory Strategies in Evolutionary Neuroscience Steps Toward Synthesis Absolute and Relative Brain Size Conclusion

Subfields: Molecular Biology

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Citations in the corpus

Neural progenitors, neurogenesis and the evolution of the neocortex (2014, background): “The extent of neocorticalization is further increased in anthropoid primates, especially humans, in which the neocortex constitutes up to ∼80% of the brain mass (Stephan et al., 1981; Striedter, 2005).”
The evolutionary origins of modularity (2012, background): “has been suggested, but heretofore untested, which is that modularity evolves not because it conveys evolvability, but as a byproduct from selection to reduce connection costs in a network (figure 1) [9,16]. Such costs include manufacturing connections, maintaining them, the energy to transmit along”
AI-GAs: AI-generating algorithms, an alternate paradigm for producing general artificial intelligence (2019, background): “Instead, their architectures consist of many heterogenous modules wired together in a particular way, with the entire design carefully sculpted by evolution [175].”
Imitative and Direct Learning as Interacting Factors in Life History Evolution (2017, positive): “It is the balancing of those costs against the improved performance resulting from the bigger brain that leads to particular brain sizes emerging [6, 49, 59, 80].”
Web-based method for translating neurodevelopment from laboratory species to humans (2007, background): “A mathematical approach is possible because a primary biological mechanism coordinating mammalian neural development apparently arose early and was maintained across evolution (Finlay and Darlington, 1995; Beatty, 1997; Striedter, 2005).”
Evolution and ontogeny of stress response to social challenges in the human child (2006, background): “…(1969) and Gould (1977), in which all phases of the lifespan remain proportional when lifespan is altered, do not accord with recent comparative analyses that indicate these more speciWc mechanisms result in diverse and species-speciWc ontogenies (Leigh, 2004; Striedter, 2004; West-Eberhard, 2003).”
Direct-fit to nature: an evolutionary perspective on biological (and artificial) neural networks (2019, background): “In BNNs, the architecture of the neural circuitry is optimized by evolution and ranges from largely diffuse nerve nets in jellyﬁsh to series of ganglia in insects to the complex subcortical and cortical structures of mammals (Satterlie, 2011; Striedter, 2005).”
Cortical High-Density Counterstream Architectures (2013, background): “Given the high scalability of mammalian neocortex, accommodating five orders of magnitude range of brain weight (112), we examined the spatial location of cortical areas in the G29×29 subgraph and found that when magnitude of connections is taken into account, it shows an optimal placement of area p”
Nonoptimal Component Placement, but Short Processing Paths, due to Long-Distance Projections in Neural Systems (2006, background): “In addition to current ontogenetic constraints, the evolutionary history of a neural network might also conserve features of its predecessors, some of which may not be optimal for the present system [8].”
Extrapolating brain development from experimental species to humans. (2007, background): “…close similarities in the manner in which mammalian brains develop are well documented (Finlay and Darlington, 1995; Finlay et al., 1998, 2001; Striedter, 1999, 2005) and, when properly considered, can be used to equate developmental time across mammalian species with confidence (Clancy,…”
RETHINKING THE EMOTIONAL BRAIN (2012, positive): “Yet, in spite of these differences, there is a highly conserved organizational plan that is characteristic of all vertebrate brains (Nauta and Karten, 1970; Northcutt and Kaas, 1995; Swanson, 2002; Butler and Hodos, 2005; Striedter, 2005).”
On the Relationships between Generative Encodings, Regularity, and Learning Abilities when Evolving Plastic Artificial Neural Networks (2013, background): “The conﬂict between ﬂexibility and connection costs also echoes the debate about the modularity/non-modularity of the mammalian brain [6, 58, 59], since the minimization of connection costs has been linked with the evolution of modularity [51,60].”