Biomedical subjects

C Cherniak

Publications and source records attributed to C Cherniak.

5 recordsLinked to original sources

PubMed2000

Modeling the large-scale geometry of human coronary arteries.

Two principles suffice to model the large-scale geometry of normal human coronary arterial networks. The first principle states that artery diameters are set to minimize the power required to distribute blood through the network. The second principle states that arterial tree geometries are set to globally minimize the lumen volume. Given only the coordinates of an arterial tree's source and "leaves", the model predicts the nature of the network connecting the source to the leaves. Measurements were made of the actual geometries of arterial trees from postmortem healthy human coronary arteriograms. The tree geometries predicted by the model look qualitatively similar to the actual tree geometries and have volumes that are within a few percent of those of the actual tree geometries. Human coronary arteries are therefore within a few percent of perfect global volume optimality. A possible mechanism for this near-perfect global volume optimality is suggested. Also, the model performs best under the assumption that the flow is not entirely steady and laminar.

Coronary Vessels↗

PubMed1999

Large-scale optimization of neuron arbors.

At the global as well as local scales, some of the geometry of types of neuron arbors-both dendrites and axons-appears to be self-organizing: Their morphogenesis behaves like flowing water, that is, fluid dynamically; waterflow in branching networks in turn acts like a tree composed of cords under tension, that is, vector mechanically. Branch diameters and angles and junction sites conform significantly to this model. The result is that such neuron tree samples globally minimize their total volume-rather than, for example, surface area or branch length. In addition, the arbors perform well at generating the cheapest topology interconnecting their terminals: their large-scale layouts are among the best of all such possible connecting patterns, approaching 5% of optimum. This model also applies comparably to arterial and river networks.

Axons↗

PubMed1995

Neural component placement.

A range of neuroanatomical results supports the idea that 'save wire' is an organizing principle of brain structure: that the theory of combinatorial optimization of networks applies to the anatomy of the nervous system. In particular, optimization of the placement of components operates at several hierarchical levels in the nervous system, from gross to microscopic anatomy, and from invertebrates to primates. That is, when anatomical positioning of interconnected neural components is treated like a problem of wire minimization in microchip layout, a hypothesis of 'best of all possible brains' is consistent with the observed siting of brains, ganglia, and even somata of individual neurons that minimizes the length of interconnections. In the case of the positioning of ganglia of Caenorhabditis elegans, optimization predictions of one-in-a-million precision can be verified.

Animals↗

PubMed1994

Component placement optimization in the brain.

This computational neuroanatomy study evaluates how well some formalisms derived from combinatorial network optimization theory fit as models for brain structure. At multiple hierarchical levels--brain, ganglion, individual cell--physical placement of neural components appears consistent with a single, simple goal: minimize cost of connections among the components. The most dramatic instance of this "save wire" organizing principle is reported for adjacencies among ganglia in the nematode nervous system; among about 40,000,000 alternative layout orderings, the actual ganglion placement in fact requires the least total connection length. In addition, evidence supports a component placement optimization hypothesis for positioning of individual neurons in the nematode, and also for positioning of mammalian cortical areas.

Adult↗

PubMed1992

Local optimization of neuron arbors.

How parsimoniously is brain wiring laid out, that is, how well does a neuron minimize costs of connections among its synapses? Neural optimization of dendritic and axonic arbors can be evaluated using a generalization of the Steiner tree concept from combinatorial network optimization theory. Local branch-junction geometry of neuronal connecting structures fits a volume minimization model well. In addition, volume of the arborizations at this neighborhood level is significantly more strongly minimized than their length, signal propagation speed, or surface area. The mechanism of this local volume optimization resembles those involved in formation of nonliving tree structures such as river junctions and electric-discharge patterns, and appears to govern initial nerve growth-cone behavior through vector-mechanical energy minimization.

Animals↗