Wiring optimization in cortical circuits.

By Dmitri "Mitya" Chklovskii, T. Schikorski, C. Stevens. Neuron, 2002

498 citations recorded by the source corpus.

Subfields: Cellular and Molecular Neuroscience, Cognitive Neuroscience, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/6f4ab9ff39559bd5e2d6735c954702ba9027b788

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Cellular and Molecular Neuroscience

In Cognitive Neuroscience

In Electrical and Electronic Engineering

Citations in the corpus

Complex brain networks: graph theoretical analysis of structural and functional systems (2009, background): “It has been suggested that the spatial layout of neurons or brain regions 34,35 is economically arranged to minimize axonal volume.”
Efficiency and Cost of Economical Brain Functional Networks (2007, background): “There is evidence that many aspects of brain anatomy can be explained in terms of minimising axonal wiring or metabolic running costs [26,27].”
A Structured Model of Video Reproduces Primary Visual Cortical Organisation (2009, background): “Since the responses of related neurons are highly dependent given a visual stimulus, this may reflect a computationally efficient solution that minimises wiring length [11,52].”
Motifs in Brain Networks (2004, background): “Most of the rules governing the evolutionary process toward more complex brains are still unknown, although the central roles of modularization (Kaas 2000), conservation of wiring length (Cherniak 1994; Chklovskii et al. 2002), and of the elaboration of network connectivity (Laughlin and Sejnowski 2”
The evolutionary origins of modularity (2012, background): “…minimize connection costs comes from the vascular system [20] and from nervous systems, including the brain, where multiple studies suggest that the summed length of the wiring diagram has been minimized, either by reducing long connections or by optimizing the placement of neurons [9,17–19,21–23].”
Nonoptimal Component Placement, but Short Processing Paths, due to Long-Distance Projections in Neural Systems (2006, background): “Our findings, which are based on the analysis of extended and refined spatial representations of neural networks, are in apparent contrast to the widely reported optimal component placement in neural systems ([3,5,6,13], but see [20,21]).”
The Importance of Neurophysiological Constraints for Modelling the Emergence of Modularity (methodology): “Striedter (2005, ch. 4 & 5) provides a good review of evolutionary changes in the sizes of brains and brain regions.”
Understanding the Emergence of Modularity in Neural Systems (2007, methodology): “The two simple cases shown in Figure 9 are representative of the two patterns of results that emerge for networks with 200 hidden units.”
Toward a Complementary Neuroscience: Metastable Coordination Dynamics of the Brain (2009, background): “However, phylogenesis promoted specialized activity of local populations of neurons [30], [46], [47], [48], [49].”
Are bigger brains better? (2009, background): “In the future, a better integration of the literature on minimal wiring solutions [48,88–90] and neural networks [80,81,83] with neurobiological work on learning and memory in insects [7,91] is desirable.”
Monkey to human comparative anatomy of the frontal lobe association tracts. (2012, background): “…phylogeny scale is also accompanied by changes in other anatomical features, such as increase in the gyrification index (Zilles et al., 1988, 1989), cortical volume (Hofman, 1985, 1988) and density of synapses (Rockel et al., 1980; Chklovskii et al., 2002; DeFelipe et al., 2002; Emes et al., 2008).”
Interneuron Diversity series: Circuit complexity and axon wiring economy of cortical interneurons. (2004, background): “Nevertheless, these models illustrate that complex hierarchies can be described quantitatively and that costly distant wiring can be minimized by appropriately designed architectural rules [13, 18 ].”
Network hubs in the human brain. (2013, background): “The ‘richness’ of hubs Both theoretical and empirical observations have led to the hypothesis that the architecture of neural systems is shaped by a trade-off between the optimization of wiring cost and the efficiency of neural communication [112,146,155,156].”
On the Relationships between Generative Encodings, Regularity, and Learning Abilities when Evolving Plastic Artificial Neural Networks (2013, background): “These results are in opposition to the general tendency of neural networks to minimize connection costs [51, 53–55] because they show that ﬂexible behaviors require maintaining many “useless” connections.”
Can structure predict function in the human brain? (2010, unknown): “Conservation of wiring length and volume (Chklovskii et al., 2002; Buzsáki et al., 2004), the efficiency of short average path lengths (Kaiser and Hilgetag, 2006), and the value of an explicit topographic neural representation of behavioral repertoire (Graziano”
High-cost, high-capacity backbone for global brain communication (2012, background): “This definition of cost assumes that cost is proportional to axonal volume (24, 26), here estimated as the product of streamline density and length.”