Epistemic Autonomy: Self-supervised Learning in the Mammalian Hippocampus. Issue 7 (July 2021) - Santos-Pata, Diogo; Amil, Adrián F.; Raikov, Ivan Georgiev; Rennó-Costa, César; Mura, Anna; Soltesz, Ivan; Verschure, Paul F.M.J.
Abstract : 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. Highlights: Biological cognition is based on self-generated learning objectives. However, the mechanism by which this epistemic autonomy is realized by the neuronal substrate is not understood. Artificial neural networks based on error backpropagation lack epistemic autonomy because they are mostly trained in a supervised fashion. In this respect, they face the symbol grounding problem of artificial intelligence. We propose that the entorhinal–hippocampal complex, a brain structure located in the medial temporal lobe and central to memory, combines epistemic autonomy with intrinsically generated error gradients akin to error backpropagation. We present evidence supporting the hypothesis that the counter-current inhibitory projections of the entorhinal–hippocampal complex implement a continuous self-supervised error minimization between network input and output.