This thesis integrates biologically-inspired mechanisms into machine learning to develop novel tuning algorithms, gradient abstractions for depth-wise parallelism, and an original bias neuron design. We introduce neuromodulatory tuning, which uses neurotransmitter-inspired bias adjustments to enhance transfer learning in spiking and non-spiking neural networks, significantly reducing parameter usage while maintaining performance. Additionally, we propose a novel approach that decouples the backward pass of backpropagation using layer abstractions, inspired by feedback loops in biological systems, enabling depth-wise training parallelization. We further extend neuromodulatory tuning by designing spiking bias neurons that mimic dopamine neuron mechanisms, leading to the development of volumetric tuning. This method enhances the fine-tuning of a small spiking neural network for EEG emotion classification, outperforming previous bias tuning methods. Overall, this thesis demonstrates the potential of leveraging neuroscience discoveries to improve machine learning.