The field of neuromorphic engineering is moving towards the development of more robust and adaptive systems, with a focus on mimicking the computational principles of biological brains. Recent research has explored the use of neuromodulable current-mode silicon neurons, which can adapt their input response and spiking pattern to context through neuromodulation, achieving high degrees of robustness and energy efficiency. In the area of temporal modeling, new architectures such as the Parallel Delayed Memory Unit (PDMU) have been proposed to enhance short-term temporal state interactions and memory efficiency in audio and bioacoustic signal analysis. Additionally, there is a growing interest in the development of low-complexity dendrite-inspired neurons for temporal prediction tasks, which can capture temporal dependencies and achieve high accuracy with reduced computational complexity. Noteworthy papers in this area include the proposal of a novel current-mode neuron design that supports robust neuromodulation, and the introduction of the PDMU, which has shown significant performance gains in audio and biomedical benchmarks. The development of sleep modulation technologies is also shifting towards closed-loop systems, which can provide individual adaptation and modulation accuracy. Furthermore, research on sketch representation learning has highlighted the importance of temporality in understanding the quality of these representations.