<｜begin▁of▁sentence｜>### Instruction:
Imagine you're conducting a neuroscience study to investigate the flow of information within a simplified model of neuronal connections in the brain. Your model comprises neurons labeled 0-5, with synaptic connections represented as edges: [(0, 1), (0, 2), (1, 3), (2, 3), (2, 4), (3, 5), (4, 5)]. In this context, you're particularly interested in understanding the central pathways through which information flows from a specific set of source neurons [0, 1] to target neurons [4, 5], as this could shed light on potential mechanisms underlying cognitive processes or the propagation of neural signals in certain brain disorders.

To achieve this, you plan to utilize a method analogous to the 'current_flow_betweenness_centrality_subset' function from NetworkX -- a tool often employed in network analysis -- which allows for the calculation of current-flow betweenness centrality for subsets of nodes within a network. By applying this method, you aim to quantify the centrality of pathways within your neuronal model, specifically focusing on how crucial each neuron and synaptic connection is for information transfer from the source neurons to the target neurons, thereby providing insights into the functional architecture of the simplified neural network.

Could you translate this theoretical scenario into a practical research question, employing the given neurons and their connections, while maintaining the emphasis on the investigation of information flow within the system using the outlined NetworkX function?
### Response:
import networkx as nx

# Create a graph
G = nx.Graph()

# Add nodes to the graph
G.add_nodes_from(range(6))

# Add edges to the graph
G.add_edges_from([(0, 1), (0, 2), (1, 3), (2, 3), (2, 4), (3, 5), (4, 5)])

# Define the list of sources and targets for the subsets
sources = [0, 1]
targets = [4, 5]

# Calculate current-flow betweenness centrality for the subsets of nodes
betweenness = nx.current_flow_betweenness_centrality_subset(
    G,
    sources=sources,
    targets=targets,
    normalized=True,
    weight=None,
    dtype=float,
    solver='lu'
)

# Print the current flow betweenness centrality of the nodes
print(betweenness)
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