# CRCNS: The neural basis of probabilistic inference in the visual system

> **NIH NIH R01** · UNIVERSITY OF ROCHESTER · 2020 · $383,230

## Abstract

Combining incoming sensory information with previously learned knowledge is one of the fundamental
computations of sensory cortex, yet It remains poorly understood. In order to investigate the neural basis of
this computation in visual cortex we combine three key techniques. First, we employ a rigorous
mathematical framework to make predictions about how the activity of sensory neurons should change with
learning and depend on the task. Second, we causally manipulate the neural circuitry by eliminating those
inputs that have been hypothesized to carry previously learned information. Third, we record the spiking
activity of many primary visual cortex (V1) neurons simultaneously, with and without those inputs.
We will combine these three techniques in three important scenarios. In the first scenario, we will measure
and analyze how V1 responses change over the course of learning two different versions of an
orientation-discrimination task. We will use this new data to validate our theoretical framework and compare
it to alternative theories. In the second scenario, we will analyze V1 responses while the subject is
multitasking, switching between two different tasks. This will provide insights into the source of performance
limitations due to multitasking and into the basis of hierarchical decision-making. In the third scenario, we
\'{ill c_on1pare V1 activity for sequential de~ision'.rnaking taskswh!Jn the brain over-weig_h_ts e§rly E)Vi_dencE)
(displaying a 'confirmation bias'), and when it does not. This will allow us to test a new computational
account of the confirmation bias in the visual domain.
Our results will address several important debates in systems neuroscience: What is the function of
feedback connections to sensory areas? What is the source and role of correlated variability of sensory
responses? What mathematical framework best describes sensory computations?
RELEVANCE (See instructions):
This project will study how the brain combines the visual information on the retina with prior knowledge
about the world, in order to support visual perception and decision-making. Insights into the neurological
basis of those processes will help us understand the effect of diseases such as schizophrenia, autism, and
ADHD on visual processing and function.

## Key facts

- **NIH application ID:** 10005435
- **Project number:** 5R01EY028811-04
- **Recipient organization:** UNIVERSITY OF ROCHESTER
- **Principal Investigator:** Ralf Manfred Haefner
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $383,230
- **Award type:** 5
- **Project period:** 2017-09-30 → 2022-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10005435

## Citation

> US National Institutes of Health, RePORTER application 10005435, CRCNS: The neural basis of probabilistic inference in the visual system (5R01EY028811-04). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10005435. Licensed CC0.

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