Rat Cerebellum volume

This Use Case places cerebellar neurons (different types: specific simplified geometric features with anisotropic properties and specific density) into a layered-volume of the cerebellum. This Use Case can be found in Online Use Cases/Circuit Building/Cells placement/Rat cerebellum volume.

../../../../_images/schema1.png ../../../../_images/cerebellum.png ../../../../_images/placement_flow_chart.png

Approach: the desired number of cells are progressively placed following a random direction from the previous cell with a distance step guaranteeing that they do not overlap. A “reset” starting point occurs when the entire surrounding is occupied. This algorithm is computationally efficient, which is fundamental for high-density volumes, but still keeps a strong random component to achieve a realistic distribution of the pairwise inter-neuron distances. The PC Layer is almost a planar grid in-between GRL and ML, with an inter-soma distance along the x-axis constrained by the requirement that adjacent dendritic trees must not overlap.

Inputs: by a simple GUI, the basic parameters can be entered by the user

  • Base sizes (x and z) of the cerebellar volume to be built
  • Plot option enabling
  • Save option enabling

Expert users can modify more parameters from scaffold_params.py (in /storage):

  • Neuron types (with ID)
  • Simplified geometric features for each neuron type: radius of the soma, and eventually dendritic field extensions (direction-dependent) if the constraints of not-overlapping cells are taken into account
  • Density for each neuron type, and eventually the ratio of the density values when compagin different types.

Output:

  • hdf5 matrix with 5 columns (saved in /storage)
  • Neuron ID (unique)
  • Neuron type ID (from 1 to 7)
  • 3D coordinates (soma center) of each neuron (x, y, and z)

Monitoring: sparseness in the subvolume by computing the distribution of pairwise distances (monitoring_positioning.py in /storage)

Moreover, a 3D basic visualization is depicted (somas of each neuron, using a different color for each neuron type).

Additional information:

  • The whole Use Case should take about 10 minutes for a volume base of 400 x 400 µm.
  • No log in to any other computer required.

EXAMPLE

  • x = 400 µm, z = 400 µm (→ DCN 200 x 200 µm)
  • y = 930 µm (600+ 150+30+150 µm), i.e. thickness DCN + GRL+ PCL + ML

TOT #NEURONS: 96.887

Glomeruli (N=7073, radius =1.5 µm, in GRL excluded the upper 10 µm)

  • 3D dist = 206 ± 89 µm - Gaussian
  • Min = 4; max =558 µm

Granule cells (N=88229, radius = 2.5 µm, in the whole GRL)

  • 3D dist = 210 ± 90 µm- Gaussian
  • Min = ; max = µm

Golgi cells (N=219, radius = 8 µm, in GRL excluded the bottom 10 µm)

  • 3D dist = 214 ± 91 µm – Gaussian
  • Min = 26; max =504 µm

Purkinje cells (N=78, radius = 7.5 µm, in PCL, planar grid)

  • 3D dist = 250 ± 121 µm
  • Min = 15; max =544 µm

Basket cells (N=603, radius = 6 µm, in the ML lower half)

  • 3D dist = 208 ± 96 µm - Gaussian
  • Min = 13; max = 534 µm

Stellate cells (N=603, radius = 4 µm, in the ML upper half)

  • 3D dist = 201 ±94 µm - Gaussian
  • Min = 9; max = 534 µm

Deep Cerebellar projection Neurons (N=12, radius=10 µm, in the Deep Nucleus)

  • 3D dist = 269 ± 155 µm
  • Min = 44; max = 566 µm
../../../../_images/golgi_placement.png ../../../../_images/gloms_placement.png ../../../../_images/pc_placement.png ../../../../_images/basket_placement.png ../../../../_images/stellate_placement.png ../../../../_images/dcn_placement.png