AI Bibliography
AI Bibliography |
|
 |
 |
 |
|
Santos-Pata, D., Amil, A. F., Raikov, I. G., Renn'o-Costa, C., Mura, A., & Soltesz, I., et al.. (2021). Epistemic autonomy: Self-supervised learning in the mammalian hippocampus. Trends in cognitive sciences. |
| Resource type: Journal Article BibTeX citation key: SantosPata2021 View all bibliographic details  |
Categories: Artificial Intelligence, Cognitive Science, Computer Science, General, Neuroscience Subcategories: Autonomous systems, Deep learning, Machine intelligence, Machine learning, Neurosymbolic, Synthetic intelligence Creators: Amil, Mura, Raikov, Renn'o-Costa, Santos-Pata, Soltesz, Verschure Publisher: Collection: Trends in cognitive sciences |
| Attachments |
| Abstract |
|
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. |