1. 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

2. NeuroscienceComputer Science Giorgio Ascoli Bob Burke Steve Senft The L-Neuron Team URL: www.krasnow.gmu.edu/L-Neuron Jeff Krichmar Slawek Nasuto Roger Scorcioni

4. 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

11. 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

13. ID Tag X Y Z Diam pid 11 62 -18.9600 29.0599 -3.50000 0.779999 10 12 62 -21.2199 29.8299 -3.50000 0.779999 11 13 62 -23.8299 31.3599 -5.79999 0.779999 12 14 62 -26.2600 34.7999 -5.79999 0.779999 13 15 62 -29.5599 39.2000 -5.79999 0.779999 14 16 62 -29.5599 39.3900 -5.79999 0.779999 15 17 62 -31.6499 41.2999 -5.79999 0.779999 16 18 62 -32.0000 41.4900 -5.79999 0.779999 17 19 62 -34.9600 45.1300 -5.79999 0.779999 18 20 62 -34.9600 45.3200 -5.79999 0.779999 19 21 62 -35.1300 45.7000 -5.79999 0.779999 20 22 62 -38.6099 49.1400 -5.79999 0.779999 21 23 62 -45.5600 58.8900 -5.79999 0.779999 22 24 62 -45.7400 59.0799 -5.79999 0.779999 23 25 62 -49.3900 67.8799 -5.79999 0.779999 24 26 62 -50.7800 71.5100 -4.59999 0.779999 25 27 62 -50.9600 71.8900 -4.50000 0.779999 26 28 62 -51.4799 78.2000 -3.50000 0.779999 27 29 62 -51.6499 78.5900 -3.50000 0.779999 28 30 62 -52.8699 81.6500 -3.50000 0.779999 29 31 62 -52.8699 81.8400 -3.50000 0.779999 30 32 62 -55.2999 82.9800 -3.60000 0.609999 31

14. Public Morphological Archive: http://www.neuro.soton.ac.uk ~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:

18. 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

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

21. Senft’s ArborVitae

25. 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.

26. Giorgio Ascoli ascoli@gmu.edu Ph. (703)993-4383 www.krasnow.gmu.edu/ascoli