Sparse Coding and Temporal Pattern Learning Co-Mediated by Dual Spike-Timing-Dependent Plasticity in a Multilayer Excitatory–Inhibitory Spiking Network

Excitatory–inhibitory (E-I) local circuits play a central role in synaptic plasticity and neural coding, yet their multilayer learning dynamics remain poorly understood. We constructed a multilayer feedforward spiking neural network with intra-layer E-I connectivity, using Izhikevich neurons to model regular spiking (RS) and fast spiking (FS) cells, and examined cooperative learning under excitatory and inhibitory spike-timing-dependent plasticity (eSTDP and iSTDP). FS-mediated lateral inhibition alleviates the long-term depression bias arising from RS firing rate adaptation via winner-take-all competition, promoting heterogeneous E→E weight differentiation while preserving mean synaptic strength. A 12×12 parameter grid scan shows that iSTDP expands the stable learning region in the E-I parameter space and reveals a sustained cooperative co-evolution of eSTDP and iSTDP during training. For sparse coding, RS adaptation is the primary driver of Lifetime Sparseness, with FS inhibition acting as a cooperative enhancer; the network exhibits low sparseness at the input layer, a rapid increase at the second layer, and a stable plateau in deeper layers. For temporal pattern learning, the selectivity index d′ improved substantially after training, reaching approximately 1.90 times that of the FS-absent condition; both interval sensitivity and pattern generalization tests confirmed that this advantage is robust across biologically plausible inter-group delays and preserved under small temporal jitter. Mutual information analysis reveals a consistent tendency for intra-layer FS circuits to maintain higher stimulus-related information across deep layers, consistent with FS-mediated suppression of non-specific responses. These findings provide computational evidence, within the scope of the present model, for understanding cortical E-I cooperative plasticity and inform design principles for neuromorphic systems with adaptive inhibitory regulation.