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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 The L-Neuron Team Neuroscience Computer Science Giorgio Ascoli Bob Burke Steve Senft Jeff Krichmar

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slide1

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

slide2

The L-Neuron Team

Neuroscience

Computer Science

Giorgio Ascoli

Bob Burke

Steve Senft

Jeff Krichmar

Slawek Nasuto

Roger Scorcioni

URL: www.krasnow.gmu.edu/L-Neuron

slide4

Structure

Function

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…?

L-Neuron is a computational tool to generate

anatomically accurate neuronal models

slide11

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
slide13

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

slide14

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 soma
  • 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?
  • Basal Dendritic input: Glu 2000 CA3 pc (200,000) on spines GABA 2400 CA3 oi (4000) on shaft AcCh SHP on spines?

References:

Freund and Buzsaki (1996)

Patton and McNaughton (1995)

Bernard and Wheal (1994)

slide18

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
slide19

Spatial distribution of cells from system-level neuroanatomical data

  • mMRI data (e.g. David Lester’s)
  • 3D atlas from serial reconstruction
slide25

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.

slide26

Giorgio Ascoli

ascoli@gmu.edu

Ph. (703)993-4383

www.krasnow.gmu.edu/ascoli