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
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:
Official implementation
none