# Receptors, microcircuits and hierarchical connectivity in predictive coding and sensory awareness

> **NIH NIH R01** · UNIVERSITY OF WISCONSIN-MADISON · 2024 · $337,598

## Abstract

SUMMARY
The standard view of how we make sense of the world around us focuses on reconstructing our environment
from the information received by our sensory organs. In this view, low-level brain areas (e.g., primary sensory
cortex) represent basic features of objects, which are elaborated on in successive processing stages, until
representations become increasingly complex in high-level areas (e.g., frontal cortex). An alternative view is
predictive coding (PC), in which we model our environment to generate sensory predictions. In PC, high-level
brain areas generate predictions of sensory activity and transmit them to low-level areas. A prediction that
does not match the sensory information gives rise to a prediction error. This error signal is sent from low- to
high-level brain areas to update the model of our environment, thereby improving future predictions to minimize
errors. Modeling studies show PC is a fast and efficient way to process sensory information, and PC provides
innovative hypotheses for understanding sleep and anesthesia, particularly when disconnected consciousness
occurs (consciousness without awareness of the environment), like dreaming. PC also holds great promise for
conceptualizing and treating brain disorders, including schizophrenia and depression. But key central features
of PC have not been empirically tested and little is known about the underlying neural mechanisms. The goal
of the proposed project is to characterize the neural dynamics, circuits and receptors enabling PC. There are
two principle hypotheses. First, predictions depend on N-methyl-D-aspartate receptors (NMDAR) because
NMDAR influence the activity of high-level brain areas where predictions are generated, and NMDAR are
enriched on neurons in lower-level areas receiving predictions. Second, in disconnected consciousness, a
breakdown of information transmission from low-level to high-level brain areas, as well as a breakdown of
computations within each area, explains why models of our environment are not updated by external sensory
information. These breakdowns prevent the comparison of predictions and sensory information, as well as the
transmission of prediction errors to high-level brain areas. To test these hypotheses, we use a cross-species
experimental design connecting cellular, circuit and systems levels to behavior. We will perform
electroencephalography, machine learning and computational modeling to define the neural basis of PC in
humans performing prediction tasks. Then we will manipulate PC using different anesthetic agents with diverse
mechanisms, establishing causal relationships between receptors, large-scale brain networks and PC. In
parallel, we will simultaneously record activity from sensory and high-level brain areas of non-human primates
(NHPs) using the same PC tasks and pharmacological interventions to measure cellular and circuit level
contributions to PC. Investigating PC will illuminate the fundamental mechanisms of perception,...

## Key facts

- **NIH application ID:** 10876337
- **Project number:** 5R01NS117901-05
- **Recipient organization:** UNIVERSITY OF WISCONSIN-MADISON
- **Principal Investigator:** Yuri B Saalmann
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $337,598
- **Award type:** 5
- **Project period:** 2020-07-15 → 2026-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10876337

## Citation

> US National Institutes of Health, RePORTER application 10876337, Receptors, microcircuits and hierarchical connectivity in predictive coding and sensory awareness (5R01NS117901-05). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/10876337. Licensed CC0.

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