Open AccessPrediction-error neurons in circuits with multiple neuron types: Formation, refinement, and functional implications
Author(s) -
Loreen Hertäg,
Claudia Clopath
Publication year - 2022
Publication title -
proceedings of the national academy of sciences of the united states of america
Language(s) - English
Resource type - Journals
eISSN - 1091-6490
pISSN - 0027-8424
DOI - 10.1073/pnas.2115699119
Subject(s) - mean squared prediction error , robustness (evolution) , computer science , biological neural network , artificial neural network , neuronal circuits , error bar , sensory system , electronic circuit , artificial intelligence , neuroscience , biological system , machine learning , biology , mathematics , physics , biochemistry , statistics , quantum mechanics , gene
Significance An influential idea in neuroscience is that neural circuits do not only passively process sensory information but rather actively compare them with predictions thereof. A core element of this comparison is prediction-error neurons, the activity of which only changes upon mismatches between actual and predicted sensory stimuli. While it has been shown that these prediction-error neurons come in different variants, it is largely unresolved how they are simultaneously formed and shaped by highly interconnected neural networks. By using a computational model, we study the circuit-level mechanisms that give rise to different variants of prediction-error neurons. Our results shed light on the formation, refinement, and robustness of prediction-error circuits, an important step toward a better understanding of predictive processing.