2000 hbp spring meeting the l-neuron project: a progress report giorgio ascoli krasnow institute for...

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

Neuroscience Computer Science

Giorgio Ascoli

Bob Burke

Steve Senft

The L-Neuron Team

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

Jeff Krichmar

Slawek Nasuto

Roger Scorcioni

Morphological variability between neuronal classessuggests different functional properties.

Effect of dendritic morphology on cellular (electro)physiology?

Dendritic/axonal growth influence on synaptic connectivity?

Morphological variability within neuronal classes…?

Structure Function

L-Neuron is a computational tool to generate anatomically accurate neuronal models

LL--SSyysstteemmss

Axiom: aaa abaabaabaRule: a aba abaabbabaabaabbabaabaabbabaRule: b abb abaabbabaabaabbabbabaabbabaabaabbabaabaabbabbaba…

<Path> <Design> stop<Design> 4 <Arm><Arm> 4F 3 <Corner> F<Corner> 2F 3<Turn> <Turn> 90R F abcdefgh[ijklmnopq]rstuvwxyz

ijklmnopqabcdefgh

rstuvwxyz

Initial parameters: S and NDetermine Sn for each tree

Implement algorithm N times

Grow for lLTaper by A

S<T?

Grow for lLT

Taper by AT

Terminate

yes

no

Generate d1(0, dp)Generate ||Generate

Calculate d2=(dp

-d1

)

Branch into twodaughters

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

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

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?

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

ConclusionsConclusions

Stochastic and statistical algorithms are suitable to generate libraries of non-identical neurons within specific anatomicalfamilies 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 virtualneurons may provide sufficient data amplification and datacompression to establish, within a foreseeable future, amorphological database for an entire mammalian brain.

Computer graphics applied to neuroanatomy is an extremely useful tool for scientific visualization and education, even withcurrently available desktop computers.

Giorgio Ascoli

ascoli@gmu.edu

Ph. (703)993-4383

www.krasnow.gmu.edu/ascoli