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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. - PowerPoint PPT Presentation
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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
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.