Santos-Pata, D., Amil, A. F., Raikov, I. G., Rennó-Costa, C., Mura, A., Soltesz, I., & Verschure, P. F.
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.
Santos-Pata, D., Amil, A. F., Raikov, I. G., Rennó-Costa, C., Mura, A., Soltesz, I., & Verschure, P. F. (2021). Epistemic Autonomy: Self-supervised Learning in the Mammalian Hippocampus. Trends in Cognitive Sciences. https://doi.org/10.1016/j.tics.2021.03.016