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Neural substrates and models of omission responses and predictive processes

MPG-Autoren
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Braga,  Alessandro
Faculty of Life Sciences, University of Leipzig, Germany;
International Max Planck Research School on Neuroscience of Communication: Function, Structure, and Plasticity, MPI for Human Cognitive and Brain Sciences, Max Planck Society;

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Zitation

Braga, A., & Schönwiesner, M. (2022). Neural substrates and models of omission responses and predictive processes. Frontiers in Neural Circuits, 16: 799581. doi:10.3389/fncir.2022.799581.


Zitierlink: https://hdl.handle.net/21.11116/0000-000A-0DC8-7
Zusammenfassung
Predictive coding theories argue that deviance detection phenomena, such as mismatch responses and omission responses, are generated by predictive processes with possibly overlapping neural substrates. Molecular imaging and electrophysiology studies of mismatch responses and corollary discharge in the rodent model allowed the development of mechanistic and computational models of these phenomena. These models enable translation between human and non-human animal research and help to uncover fundamental features of change-processing microcircuitry in the neocortex. This microcircuitry is characterized by stimulus-specific adaptation and feedforward inhibition of stimulus-selective populations of pyramidal neurons and interneurons, with specific contributions from different interneuron types. The overlap of the substrates of different types of responses to deviant stimuli remains to be understood. Omission responses, which are observed both in corollary discharge and mismatch response protocols in humans, are underutilized in animal research and may be pivotal in uncovering the substrates of predictive processes. Omission studies comprise a range of methods centered on the withholding of an expected stimulus. This review aims to provide an overview of omission protocols and showcase their potential to integrate and complement the different models and procedures employed to study prediction and deviance detection.This approach may reveal the biological foundations of core concepts of predictive coding, and allow an empirical test of the framework’s promise to unify theoretical models of attention and perception.