2000 HBP SPRING MEETING The L-NEURON Project: A Progress Report Giorgio Ascoli Krasnow Institute for Advanced Study and Department of Psychology George.

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2000 HBP SPRING MEETING The L-NEURON Project: A Progress Report Giorgio Ascoli Krasnow Institute for Advanced Study and Department of Psychology George Mason University Fairfax, VA

NeuroscienceComputer Science Giorgio Ascoli Bob Burke Steve Senft The L-Neuron Team URL: Jeff Krichmar Slawek Nasuto Roger Scorcioni

Morphological variability between neuronal classes suggests different functional properties. Effect of dendritic morphology on cellular (electro)physiology? Dendritic/axonal growth influence on synaptic connectivity? Morphological variability within neuronal classes…? StructureFunction L-Neuron is a computational tool to generate anatomically accurate neuronal models

The Algorithms (this is a motoneuron) Hillman’s Algorithm: Calculate Diameters Measure Angles Tamori’s Algorithm: Calculate Diameters Calculate Angles Burke’s Algorithm: Measure Diameters Measure Angles

ID Tag X Y Z Diam pid

Public Morphological Archive: ~200 hippocampal neurons (pyramidal, chandelier, etc.) Axo-somatic input: GABA 290 CA3 cc x20 on axon and somaCA3 cc Apical Dendritic input: Glu EC (200,000) on distal spines (PP) Glu DG gc (1,000,000) on shaft (MF) Glu 2000 CA3 pc (200,000) on spines GABA 2400 CA3 ri (4000) on shaft AcCh SHP on spines?ECgcri Basal Dendritic input: Glu 2000 CA3 pc (200,000) on spines GABA 2400 CA3 oi (4000) on shaft AcCh SHP on spines?oi Freund and Buzsaki (1996) Patton and McNaughton (1995) Bernard and Wheal (1994) References:

Future perspective: Extensive morphological analysis Extension to different morphological classes First release of the database: 7/00 First release of L-Neuron executable: 12/00 From neurons to networks: Spatial distribution of neurons Connectivity data and axonal navigation Interaction with Senft’s ArborVitae

Spatial distribution of cells from system-level neuroanatomical data mMRI data (e.g. David Lester’s) 3D atlas from serial reconstruction

Senft’s ArborVitae

Conclusions Stochastic and statistical algorithms are suitable to generate libraries of non-identical neurons within specific anatomical families and neuritic interaction schemes.   Basic geometrical parameters (and connection rules) are available in the literature in an extremely dispersed fashion for many morphological classes and brain regions.   The algorithmic generation of anatomically accurate virtual neurons may provide sufficient data amplification and data compression to establish, within a foreseeable future, a morphological database for an entire mammalian brain.   Computer graphics applied to neuroanatomy is an extremely useful tool for scientific visualization and education, even with currently available desktop computers.

Giorgio Ascoli Ph. (703)