The Combinatorial Threshold Linear Network (CTLN) models neural activity as a network of nodes where activity is determined by binary synapses. The research presented here used a software implementation of the CTLN to analyze long run neural activity resulting from a variety of initial activation states.  The results demonstrate the variability of attractive states that can be triggered in network structures having the same degree sequences.