Santos-Pata, D., Amil, A. F., Raikov, I. G., Rennó-Costa, C., Mura, A., Soltesz, I., & Verschure, P. F.

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.

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