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

Contents

• Short story

• Long story

• Meaning

• Meaning?

• Conclusion

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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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1. Functional Synaptic Connectome

• Locations of putative pre- and postsynapticsynapses

• 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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Branch order histogram

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Path distant histogram

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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 Martinotticells (MCs)]

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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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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 (KS) test (α < 0.05)Overlap according to path distance (left) and

overlap according to branch order (right)

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3. Statistical SC Predicts Functional SC

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3. Statistical SC Predicts Functional SC

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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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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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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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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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Meaning?

Different topologies having the same histograms

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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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THANK YOU!

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