A paradox in neuroscience is the large number of oscillations of small neural networks compared with the few oscillations observed in the conscious brain. It remains unclear what is the maximum number of synchronized oscillations a network can support and whether all or some of these oscillations would survive in noisy heterogenous circuits. Here, we attempt to answer these questions through a comprehensive study of local inhibitory networks of Hodgkin-Huxley neurons. We use a neuromorphic platform combining electronic noise and device-specific heterogeneity with tuneable extrinsic noise, tuneable network connectivity, and controlled initial conditions. As in the brain, stimuli are instantaneously integrated by analog circuits. This gives us the computing power needed to map the network dynamics over the entire phase space and demonstrate the full complement of limit cycles, basins of attraction, and dependence on network parameters. Our main finding is that the maximum number of limit cycles is equal to the combinatorial number of activation pathways through the network, allowing for coincident action potentials, and that all limit cycles are equally robust to noise and mild heterogeneity but highly dependent on inhibition delay and the timing of stimuli. We established the robustness of individual limit cycles by computing the detailed balance of bifurcations between attractors. This accounts for all transitions in a system where Lyapunov exponents are both positive and negative depending on phase space coordinates and noise intensity. Another interesting finding is the unexpected resilience of limit cycles to mild network heterogeneity. This occurs as stochastic processes recruit quiescent neurons whose subthreshold periodic oscillations help maintain the synchronization of limit cycles against heterogeneity.