Enhanced accuracy in first-spike coding using current-based adaptive LIF neuron

First spike timings are crucial for decision-making in spiking neural networks (SNNs). A recently introduced first-spike (FS) coding method demonstrates comparable accuracy to firing-rate (FR) coding in processing complex temporal information through supervised learning. However, its performance still falls behind advanced approaches. In order to explore the potential of FS coding, we enhance the capability of SNNs in classifying auditory datasets by improving neural dynamics. We propose a current-based adaptive LIF neuron (CuAdLIF) with delayed responses and membrane potential adaptation to enhance temporal correlations and preserve long short-memory. Furthermore, we introduce strategies to minimize delays in decision-making and enable adaptive training for FS coding. Results show that the CuAdLIF neuron enhances the extraction of temporal features and significantly improves FS coding accuracy. In addition, our strategies effectively reduce output time delays.