# Testing the Mechanisms, Layers, and Frequencies of Prediction Encoding and its Violation

> **NIH NIH K99** · MASSACHUSETTS INSTITUTE OF TECHNOLOGY · 2020 · $68,547

## Abstract

A key cognitive function is expectation. Expectation is thought to be generated through an agent’s experiences
and learning. An established theoretical model, predictive coding, states that the brain is constantly building
models (signifying changing expectations) of the environment. The brain does this by forming predictions (PD).
These predictions interact with incoming sensory data. When the PD matches the sensed data, the expectation
is correct. When they do not match, a prediction error (PE) signal is generated. This PE signal is then used to
update the prediction, so that the brain’s internal model can more optimally predict future sensory data.
The implications for the predictive coding model are far-reaching. If the model is correct, it would fundamentally
shift our understanding of the neural code from one that represents the “state of the environment” (e.g., the
classic Hubel and Wiesel receptive field model) to one in which the brain performs “active sensing” and builds
internal models of the world, testing them against incoming sensory data. In addition, the predictive coding
model has many implications for our understanding of disease states. For example, autism can be understood
as a failure in correctly predicting social actions, and as a result, every social interaction is “surprising”.
Various theories exist about how a predictive code could be implemented in the brain. They propose that
distinct cortical layers, flow of communication (feedforward/feedback), and oscillatory dynamics are involved in
signaling PEs and PDs. However, little neurophysiological data exist to support these models. Here, I propose
an experiment to manipulate predictions by changing the probabilities associated with objects in a delayed-
match-to-sample task (Aim 1). This will allow me to induce expectations of varying strengths. With my primary
mentor, Earl Miller, I will be trained to perform make multi-area, multi-laminar recordings in monkeys. I will then
use these data to study how expectations are built and what happens when they are violated. In Aim 2, with
my secondary mentor, Nancy Kopell, I will use computational modeling to understand how the changing
probability of inputs map on to a synchronously firing co-active group of cells (an assembly). We hypothesize
that different assemblies represent different predictions. We also hypothesize that the strength of each
assembly will represent the probability of a particular stimulus (thereby forming the neural basis of PD). Finally,
due to the excitatory-inhibitory loops between cells in an assembly, we will investigate whether re-activations of
the assembly occur rhythmically, paced by a beta (15-30 Hz) oscillation in deep cortical layers. Gamma
oscillations (40-90 Hz) in superficial cortical layers could help switch off the current prediction (PD) by signaling
prediction error (PE). In Aim 3, we will test whether interrupting beta oscillations (thought to signal PD) with
closed-loop optogenetic inhi...

## Key facts

- **NIH application ID:** 10224537
- **Project number:** 3K99MH116100-02S1
- **Recipient organization:** MASSACHUSETTS INSTITUTE OF TECHNOLOGY
- **Principal Investigator:** Andre Moraes Bastos
- **Activity code:** K99 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $68,547
- **Award type:** 3
- **Project period:** 2018-09-07 → 2021-04-15

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10224537

## Citation

> US National Institutes of Health, RePORTER application 10224537, Testing the Mechanisms, Layers, and Frequencies of Prediction Encoding and its Violation (3K99MH116100-02S1). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10224537. Licensed CC0.

---

*[NIH grants dataset](/datasets/nih-grants) · CC0 1.0*