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unidesign.spatial

geometry of neural activity computational neuroanatomy microcircuitry functional geometry the geometry of activation see Rodolf Llinas Hellers Lecture

Focus: * Fiji/TrakEM https://github.com/acardona/Fiji-TrakEM2-scripts * http://code.google.com/p/treestoolbox/ * http://www.treestoolbox.org/index.html

From Molecular Dynamics

http://pymol.org/ http://www2.molmovdb.org/wiki/info/index.php/Macromolecular_Geometry

Geometer:http://en.wikipedia.org/wiki/Harold_Scott_MacDonald_Coxeter http://en.wikipedia.org/wiki/Harold_Scott_MacDonald_Coxeter

http://paulbourke.net/geometry/ http://en.wikipedia.org/wiki/Spatial_query

For example, mapping of variables and geometry specs are things that will need to be worked out in order to do a proposed ML for compartmental chemistry and compartmental neuronal modeling.

A common issue in multiscale models is the valuable capability of being able to switch between scales of representation.

Consider the case where a channel is spread over a dendrite. We want to provide a mapping between channel conductance in each location , and the level of some molecule in the same location. Alternatively, we may want to deliver a stimulus at a specific location along the deendrite, and so need to specify which receptor instances to activate.

Goal: want compartmental chemistry and compartmental neuronal modelling

The future models of axonal guidance will probably be similar to existent modelf of cell chemotaxis. (standard reaction-diffusion models) Nomenclature ============ * tree, treeline, skeleton * segment, section, line * connector * treenode, vertex, node * positions, points, locations, vertices

Motivation

** http://home.earthlink.net/~perlewitz/sftwr.html#morphology * https://github.com/tfoutz99/Neuron3D * Figure 4 in http://www.ini.uzh.ch/~acardona/papers/Cardona_2010_lineage_identification.pdf

Design Goals

  • Keep it simple. (Reduction of the conceptual complexity affords adoption)
  • Keep it open to interface. (Embedd in the software ecosystem: simulators, visualization, ontologies, internet, (molecular biology)
  • Design towards modular hierarchical structure, toward multi-scale. (Components are themselves complex entities with their own internal dynamics.)
  • Keep the temporal domain in mind.
  • Design toward collaborative process of exploration
  • Scalable data analysis capabilities
  • "Thin" classes to underlying data from NeuroHDF
  • Analogy (Desktop Publishing): The paper (the Region), the objects (Tree, ...), groups of objects, operations on groups of objects

Basic Questions

  • Storage of circuitry local (with individual self-contained elements) or global (as a big array with labels for indexing)
    • this questions are pertaining to: the data format, the data object model, the visualization object model
    • if global, want to extract one arborization (e.g. make it local), and then do analysis
  • fiber bundle format: time slice at the topmost level. what is the most efficient? how far does it depend on the data and required operations?
  • importance of the ability to select/deselect, group elements for easier interactivity
  • backend storage using neurohdf or ORM and database, e.g. with sqlalchemy
  • cell type X responsible to make cell type Y tuned to F. X and Y relationship? spatial overlap, synaptic connectivity, temporal correlations

References

  • H.B.M. Uylings, A. Ruiz-Marcos, J. van Pelt, The metric analysis of three-dimensional dendritic tree patterns: a methodological review, Journal of Neuroscience Methods, Volume 18, Issues 1-2, October 1986, Pages 127-151, ISSN 0165-0270, DOI: 10.1016/0165-0270(86)90116-0.
  • The DIADEM Metric: Comparing Multiple Reconstructions of the Same Neuron

What spatio-conceptual queries for visualization do you want to do? - Show the skeleton and in-out connectivity with id X - Show the arbor types (axon, dendrite, soma, pre, post) colored - Show the axonal arbors of the complete spatial volume with different colors (depending on X) - Show the dendrictic arbors ... " - Show loop motifs... - Show "excitatory"/"inhibitory" cells... - Show connectors as sphere, colored by their type, radius scaled with their volume

Circuits * http://www.igi.tugraz.at/ * http://www.lsm.tugraz.at/download/index.html

A personal view of the early development of computational neuroscience in the USA require extensive, quantitative descriptions of neuronal properties: (1) the ion channel types, their densities and their distributions throughout each neuron; (2) the types and properties of intracellular calcium ion buffers, and their effect on intracellular

spatial and temporal profiles of calcium concentration;
  1. neurotransmitters, their release mechanisms, and modulation of release by activity;
  2. neurotransmitter receptor types, properties, and sensitivity to modulation by other transmitters, hormones or activity; and
  3. intracellular chemical reactions and signaling pathways which may, in turn, affect the release of transmitters and the sensitivity of receptors