Change detection in the primate auditory cortex through feedback of prediction error signals

Keitaro Obara, Teppei Ebina, Shin-Ichiro Terada, Takanori Uka, Misako Komatsu, Masafumi Takaji, Akiya Watakabe, Kenta Kobayashi, Yoshito Masamizu, Hiroaki Mizukami, Tetsuo Yamamori, Kiyoto Kasai and Masanori Matsuzaki ()
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Keitaro Obara: The University of Tokyo
Teppei Ebina: The University of Tokyo
Shin-Ichiro Terada: The University of Tokyo
Takanori Uka: University of Yamanashi
Misako Komatsu: RIKEN Center for Brain Science
Masafumi Takaji: RIKEN Center for Brain Science
Akiya Watakabe: RIKEN Center for Brain Science
Kenta Kobayashi: Section of Viral Vector Development, National Institute for Physiological Sciences
Yoshito Masamizu: RIKEN Center for Brain Science
Hiroaki Mizukami: Jichi Medical University
Tetsuo Yamamori: RIKEN Center for Brain Science
Kiyoto Kasai: The University of Tokyo
Masanori Matsuzaki: The University of Tokyo

Nature Communications, 2023, vol. 14, issue 1, 1-17

Abstract: Abstract Although cortical feedback signals are essential for modulating feedforward processing, no feedback error signal across hierarchical cortical areas has been reported. Here, we observed such a signal in the auditory cortex of awake common marmoset during an oddball paradigm to induce auditory duration mismatch negativity. Prediction errors to a deviant tone presentation were generated as offset calcium responses of layer 2/3 neurons in the rostral parabelt (RPB) of higher-order auditory cortex, while responses to non-deviant tones were strongly suppressed. Within several hundred milliseconds, the error signals propagated broadly into layer 1 of the primary auditory cortex (A1) and accumulated locally on top of incoming auditory signals. Blockade of RPB activity prevented deviance detection in A1. Optogenetic activation of RPB following tone presentation nonlinearly enhanced A1 tone response. Thus, the feedback error signal is critical for automatic detection of unpredicted stimuli in physiological auditory processing and may serve as backpropagation-like learning.

Date: 2023
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DOI: 10.1038/s41467-023-42553-3

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