Table 2.

Simulation results presented in a similar fashion to Bamford et al. (2010) for three cases, all of which incorporate synaptic plasticity.

Case	1	2	3
Synaptic rewiring	✓	✗	✓
Synaptic plasticity (STDP)	✓	✓	✓
Input correlations	✓	✓	✗
Target neuron mean spike rate	21.15 Hz	20.11 Hz	9.31 Hz
Final mean feedforward fan in / target neuron	15.91	N/A	11.87
Weight proportion of maximum	0.83	0.72	0.62
Mean σaff−init	2.35	2.35	2.35
Mean σaff−fin−conn−shuf	2.33	N/A	2.31
Mean σaff−fin−conn	1.62	2.35	1.85
p(WSR σaff−fin−conn vs. σaff−fin−conn−shuf)	2.80 × 10−43	N/A	3.65 × 10−27
Mean σaff−fin−weight−shuf	1.61	2.32	1.78
Mean σaff−fin−weight	1.49	1.92	1.57
p(WSR σaff−fin−weight vs. σaff−fin−weight−shuf)	4.03 × 10−33	4.02 × 10−43	1.44 × 10−21
Mean ADinit	0.81	0.81	0.81
Mean ADfin−conn−shuf	0.82	N/A	1.09
Mean ADfin−conn	0.77	0.81	0.91
p(WSR ADfin−conn vs. ADfin−conn−shuf)	0.39	N/A	0.002
Mean ADfin−weight−shuf	0.79	0.92	1.04
Mean ADfin−weight	0.85	0.79	1.07
p(WSR ADfin−weight vs. ADfin−weight−shuf)	0.0002	0.0001	0.58

Case 1 consists of a network in which both synaptic rewiring and input correlations are present. Case 2 does not integrate synaptic rewiring, but still has input correlations. Case 3 relies on synaptic rewiring and STDP to generate sensible topographic maps in the absence of input correlations.