Scale free topology

[MartinuzziFrancesco]

Paper

https://doi.org/10.1063/1.4746765

Topology

From the paper:

Step 1: Growth

Start with an initially randomly connected network containing (m_0) neural units.
At every time step, introduce a new unit and connect it to (m) already-existing neural units, with (m \le m_0).
When the number of units reaches the total (N), the growth process stops.

Step 2: Preferential attachment

The probability (\Pi_i) that a new neural unit will be attached to unit (i) (one of the (m) already-existing units) depends on the degree (k_i) of unit (i), as follows:

$$\Pi_i = \frac{k_i}{\sum_{j \in \{1,2,\ldots,m\}} k_j}, \qquad i = m_0 + 1, \ldots, N.$$

Official implementation

none