  Recent progress in artificial intelligence (AI) has been driven by insights
from neuroscience, particularly with the development of artificial neural
networks (ANNs). This has significantly enhanced the replication of complex
cognitive tasks such as vision and natural language processing. Despite these
advances, ANNs struggle with continual learning, adaptable knowledge transfer,
robustness, and resource efficiency - capabilities that biological systems
handle seamlessly. Specifically, ANNs often overlook the functional and
morphological diversity of the brain, hindering their computational
capabilities. Furthermore, incorporating cell-type specific neuromodulatory
effects into ANNs with neuronal heterogeneity could enable learning at two
spatial scales: spiking behavior at the neuronal level, and synaptic plasticity
at the circuit level, thereby potentially enhancing their learning abilities.
In this article, we summarize recent bio-inspired models, learning rules and
architectures and propose a biologically-informed framework for enhancing ANNs.
Our proposed dual-framework approach highlights the potential of spiking neural
networks (SNNs) for emulating diverse spiking behaviors and dendritic
compartments to simulate morphological and functional diversity of neuronal
computations. Finally, we outline how the proposed approach integrates
brain-inspired compartmental models and task-driven SNNs, balances
bioinspiration and complexity, and provides scalable solutions for pressing AI
challenges, such as continual learning, adaptability, robustness, and
resource-efficiency.
