List of strategies

AllToAll

This strategy connects each presynaptic neuron to all the postsynaptic neurons. It therefore creates one connection for each unique pair of neuron.

FixedIndegree

This strategy connects to each postsynaptic neuron, a fixed number of uniform randomly selected presynaptic neurons.

• indegree: Number of neuron to connect for each postsynaptic neuron.

JSON

"connectivity": {
"A_to_B": {
    "strategy": "bsb.connectivity.FixedIndegree",
    "presynaptic": {
            "cell_types": ["A"]
    },
    "postsynaptic": {
            "cell_types": ["B"]
    },
    "indegree": 2
}

PYTHON

config.connectivity.add(
  "A_to_B",
  strategy="bsb.connectivity.FixedIndegree",
  presynaptic=dict(cell_types=["A"]),
  postsynaptic=dict(cell_types=["type_B"]),
  indegree= 2
)

Note

In this example every cell of type B is connected to two cells of type A.

FixedOutdegree

This strategy connects to each presynaptic neuron, a fixed number of uniform randomly selected postsynaptic neurons.

• outdegree: Number of neuron to connect for each presynaptic neuron.

VoxelIntersection

This strategy voxelizes morphologies into collections of cubes, thereby reducing the spatial specificity of the provided traced morphologies by grouping multiple compartments into larger cubic voxels. Intersections are found not between the separate compartments but between the voxels and random compartments of matching voxels are connected to each other. This means that the connections that are made are less specific to the exact morphology and can be very useful when only 1 or a few morphologies are available to represent each cell type.

• affinity: A fraction between 1 and 0 which indicates the tendency of cells to form connections with other cells with whom their voxels intersect. This can be used to downregulate the amount of cells that any cell connects with.

• contacts: A number or distribution determining the amount of synaptic contacts one cell will form on another after they have selected eachother as connection partners.

Note

The affinity only affects the number of cells that are contacted, not the number of synaptic contacts formed with each cell.

JSON

{
  "A_to_B": {
    "strategy": "bsb.connectivity.VoxelIntersection",
    "presynaptic": {
      "cell_types": [
        "A"
      ],
    },
    "postsynaptic": {
      "cell_types": [
        "B"
      ],
    },
    "affinity": 0.5,
    "contacts": 1
  }
}

PYTHON

config.connectivity.add(
  "A_to_B",
   strategy="bsb.connectivity.VoxelIntersection",
   presynaptic=dict(cell_types=["A"]),
   postsynaptic=dict(cell_types=["type_B"]),
   affinity= 0.5,
   contacts= 1
)

The previous example demonstrates a strategy to connect cells of type A with cells of type B, where only half of the computed overlaps are considered, and one synapse is placed for each connection. It is also possible to define the number of synapse per connection with a distribution:

JSON

    {
  "A_to_B": {
    "strategy": "bsb.connectivity.VoxelIntersection",
    "presynaptic": {
      "cell_types": [
        "A"
      ],
    },
    "postsynaptic": {
      "cell_types": [
        "B"
      ],
    },
    "affinity": 0.5,
    "contacts": {
      "distribution": "norm",
      "loc": 10,
      "scale": 2
    }
  }
}

PYTHON

config.connectivity.add(
  "A_to_B",
  strategy="bsb.connectivity.VoxelIntersection",
  presynaptic=dict(cell_types=["A"]),
  postsynaptic=dict(cell_types=["type_B"]),
  affinity= 0.5,
  contacts= dict(
    distribution="norm",loc=10,scale=2
  )
)

In this case, the number of synapses is randomly drawn from a normal distribution with a mean of 10 and a standard deviation of 2.

Note

Normal distribution is just one option but all the distributions available in your scipy package can be used.