Order-Sensitive Fast-Synapse Limits in Sparse Excitatory-Inhibitory Threshold-Reset Networks
When neurons fire depends on which signals arrive first, not just their strength
A neuron's response to mixed excitatory and inhibitory signals depends critically on which type arrives first—a detail that weak mathematical convergence alone cannot capture. Two neural network models with identical overall signal strength but opposite arrival orders can produce opposite firing outcomes, and this order-dependent effect persists even in large, sparse networks.
Current mathematical models of neural networks often treat incoming signals as interchangeable if their total strength converges properly. This work shows that assumption breaks down for threshold-based neurons: the timing order of excitation versus inhibition fundamentally changes the network's behavior. Understanding this could improve how neuroscientists and engineers predict neural circuit responses and design more accurate models of brain computation.