Biological cognition is based on the ability to autonomously acquire knowledge, or epistemic autonomy. Such self-supervision is largely absent in artificial neural networks (ANN) because they depend on externally set learning criteria. Yet training ANN using error backpropagation has created the current revolution in artificial intelligence, raising the question of whether the epistemic autonomy displayed in biological cognition can be achieved with error backpropagation-based learning. We present evidence suggesting that the entorhinal–hippocampal complex combines epistemic autonomy with error backpropagation. Specifically, we propose that the hippocampus minimizes the error between its input and output signals through a modulatory counter-current inhibitory network. We further discuss the computational emulation of this principle and analyze it in the context of autonomous cognitive systems.

生物认知基于自主获取知识或认知自主的能力。这种自我监督在人工神经网络（ANN）中基本上不存在，因为它们依赖于外部设定的学习标准。然而，使用误差反向传播训练人工神经网络引发了当前人工智能的革命，提出了一个问题：生物认知中表现出的认知自主性是否可以通过基于误差反向传播的学习来实现。我们提出的证据表明内嗅-海马复合体将认知自主性与误差反向传播结合起来。具体来说，我们建议海马体通过调制逆流抑制网络最小化其输入和输出信号之间的误差。我们进一步讨论该原理的计算仿真，并在自主认知系统的背景下对其进行分析。