We study a network of $N$ identical leaky integrate-and-fire model neurons coupled by $\alpha$-function pulses, weighted by a coupling parameter $K$. Studies of the dynamics of this system have mostly focused on the stability of the fully synchronized and the fully asynchronous splay states, which naturally depends on the sign of $K$, i.e., excitation vs inhibition. We find that there is also a rich set of attractors consisting of clusters of fully synchronized oscillators, such as fixed $(N-1,1)$ states, which have synchronized clusters of sizes $N-1$ and 1, as well as splay states of clusters with equal sizes greater than 1. Additionally, we find limit cycles that clarify the stability of previously observed quasiperiodic behavior. Our framework exploits the neutrality of the dynamics for $K=0$ which allows us to implement a dimensional reduction strategy that simplifies the dynamics to a continuous flow on a codimension 3 subspace with the sign of $K$ determining the flow direction. This reduction framework naturally incorporates a hierarchy of partially synchronized subspaces in which the new attracting states lie. Using high-precision numerical simulations, we describe completely the sequence of bifurcations and the stability of all fixed points and limit cycles for $N=2\text{–}4$. The set of possible attracting states can be used to distinguish different classes of neuron models. For instance from our previous work [Chaos 24, 013114 (2014)] we know that of the types of partially synchronized states discussed here, only the $(N-1,1)$ states can be stable in systems of identical coupled sinusoidal (i.e., Kuramoto type) oscillators, such as $\theta$-neuron models. Upon introducing a small variation in individual neuron parameters, the attracting fixed points we discuss here generalize to equivalent fixed points in which neurons need not fire coincidently.

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• Bifurcations
• Coupled oscillators
• Neuronal dynamics
• Synchronization