1. Statistical connectivity provides a
sufficient foundation for specific
functional connectivity in
neocortical neural microcircuits
Sean L. Hill, Yun Wang, Imad Riachi, Felix Schürmann, and Henry Markram

2. Contents
• Short story
• Long story
• Meaning
• Meaning?
• Conclusion
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4. Long story
1. Functional Synaptic Connectome
2. Statistical Structural Connectome
3. Statistical Structural Connectivity Predicts
Functional Synaptic Connectivity
4. More about Statistical Structural
Connectome
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5. 1. Functional Synaptic Connectome
• Locations of putative pre- and postsynaptic
synapses
• Distributions were measured:
– For presynaptic neurons and postsynaptic neurons
– In form of histograms
• Connectomes will be compared according to
these measurements in next steps
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6. Branch order histogram
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7. Path distant histogram
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8. 2. Statistical Structural Connectome
• 298 neocortical neurons (pyramidal cells and
interneurons). The neurons were obtained from
different experiments and animals, but they were
always from the same species, region of the
brain, and age range (rat, somato-sensory area
S1, and P12 – P16)
• The final dataset included at least seven
exemplars for each type of neuron morphology
– [pyramidal cells (PCs), nest basket cells (NBCs), small basket cells
(SBCs), large basket cells (LBCs), bitufted cells (BTCs), and Martinotti
cells (MCs)]
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9. 2. Statistical Structural Connectome
• Randomly select neurons from this pool
position their somata in a 3D volume
orresponding to the size of a functional
neocortical column.
• Identity close appositions between neurons
using a collision detection algorithm running
on a supercomputer.
• Compute distribution measurements.
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10. 3. Statistical SC Predicts Functional SC
• The accuracy of the
predicted synaptic
locations was tested
using histogram
intersection (HI)
• Significant match of
innervation patterns is
tested using the
Kolmogorov – Smirnov
Overlap according to path distance (left) and (KS) test (α < 0.05)
overlap according to branch order (right)
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11. 3. Statistical SC Predicts Functional SC
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12. 3. Statistical SC Predicts Functional SC
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13. 4. More about Statistical Structural
Connectome
• For the sake of completeness…
• The sensitivity of the structural connectome to
the precise configuration of the model
microcircuit (positions, rotations, densities).
• Low variance of statistical connectivity regardless
of the number of morphology variants.
• The distribution of appositions can be computed
from volume density representations of
individual and average dendrites and axons rather
than the collision detection process.
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14. Meaning
• The relative roles of statistical and
chemospecific mechanisms in the formation
of the connectome.
– Exact positioning within a layer is not critical for
achieving cell type-specific patterning of synapses.
– The role of these molecular processes is
nonspecific and therefore, not aimed at any
specific pair of neurons.
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15. Meaning
• The processes of arbor alignment and synapse
formation are separate.
• The diverse morphologies of the different
classes of pyramidal and interneurons found
in neocortex serve to ensure a robust and
invariant set of distributed inputs and outputs
between specific pre- and postsynaptic
populations of neurons
• And an easier way to model the brain!
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16. Meaning?
• Small sample size:
– Number of neurons: 298 << 1011 (100 billion)
– Number of synaptic connections: 90 << 1014 (100
trillion)
• Uncertainty: A putative synapse is not
necessarily a functional synapse although a
functional synapse is a putative one.
• Measurement: histogram and path distance
tell nothing about graph topology
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17. Meaning?
Different topologies having the same histograms
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18. Conclusion
• The idea of statistical connectivity may
provide an important base for research in
neuroanatomy.
• The ability of approximating cortical columns
in computer helps move the Blue Brain Project
a big step forward.
• However, there are still some issues and
people should run more experiment to
confirm its result.
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19. THANK YOU!
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