Neurons as autonomous agents: A biologically inspired framework for cognitive architectures in artificial intelligence

Abstract

Despite impressive recent advances in artificial intelligence (AI), current deep neural networks still lack the adaptability and energy efficiency inherent to biological systems. Here we suggest that this problem may be overcome by taking inspiration from the brain where neurons operate as autonomous agents, each capable of adjusting its synaptic connections and internal states based on local information. Currently, typical artificial neurons are static nodes, which is in striking contrast to the rich, dynamic computations performed by biological neurons. In this review, we propose redesigning artificial neurons as self-regulating, agent-like units, making actions to maximize future energy/reward. Similarly, as single-celled organisms which can autonomously navigate in complex environments in search for food, neurons can also be viewed as autonomous decision-makers, seeking to maximize their own energy resources. Thus, neurons could be operating similarly like reinforcement learning (RL) agents, which make actions to obtain maximum future reward. Here first we review literature illustrating that biological neurons perform complex computations and employ local, predictive learning rules to anticipate future activity to maximize metabolic energy. Next, we provide examples of recent biologically inspired learning algorithms where artificial neurons are empowered with computational flexibility, similarly to autonomous agents. Networks with neurons using such local learning rules can in some examples outperform current AI algorithms. We also discuss how this can improve scalability of current multi-agent systems (MAS) and energy efficiency. Therefore, designing neurons as autonomous agents may provide an important step toward building human-like cognition.