# Neural Basis of Facial Individual Recognition in Paper Wasps

> **NIH NIH R34** · CORNELL UNIVERSITY · 2022 · $690,388

## Abstract

The neural circuits of animals, including humans, are the combined product of adaptation by natural selection
and the evolutionary history of a species. Distinguishing which features of neural circuits represent
fundamental principles of circuit design versus the quirks of a particular model species requires comparative
approaches. Features of neural circuit design and architecture that have evolved independently multiple times
in distantly related species indicate elements of optimal solutions to solving a particular behavioral or cognitive
problem. At the same time, aspects of neural systems that have evolved to facilitate novel behavior provide
key insights into the process by which neural systems can be modified while maintaining function. Here we
seek funding to develop the paper wasp, Polistes fuscatus, as a model system in neuroscience. Remarkably,
these wasp uses facial individual recognition to differentiate among nestmates. Like primates, these wasps use
holistic visual processing of faces and show cognitive specializations for facial recognition. In many respects,
paper wasp social behavior is more similar to cooperatively breeding vertebrates than other social insects like
honey bees or ants. These wasps represent an independent evolution of specialized face processing relative
to primates, allowing for comparisons of the architecture of neural encoding of facial identity between distantly
related groups that have independently evolved eyes and facial recognition. At the same time, our recent work
shows that individual recognition has evolved in paper wasps within the last few thousand years meaning that
we have a rare opportunity to understand how neural circuits underlying complex social behavior have arisen
from ancestral abilities. We propose a multi-pronged approach that takes advantage of modern tools in
neuroscience that will allow us to rapidly begin to characterize the neural encoding of faces in wasps. (Aim 1)
We will leverage our expertise in single cell genomic approaches to identify which cell types within the wasp
brain are involved in facial processing and individual recognition. (Aim 2) We will build on preliminary data from
multi-channel electrophysiological recordings to examine the neural encoding of facial recognition in the wasp
brain. These two approaches can be readily applied to non-model species such as paper wasps and provide a
direct and immediate path forward for establishing paper wasps as a model for neuroscience studies of social
recognition. (Aim 3) We will work to screen and optimize viral vectors for transgene expression in paper wasps.
Viral vectors of genetically encoded tools for recording and manipulating neurons are now commonplace in
model and non-model vertebrates, but rare in insects. Identifying which vectors work in wasps will immediately
open a wide range of possible experiments and will be readily shared with the scientific community. Paper
wasps accomplish remarkably sophisticated and com...

## Key facts

- **NIH application ID:** 10524286
- **Project number:** 1R34NS128868-01
- **Recipient organization:** CORNELL UNIVERSITY
- **Principal Investigator:** Michael J Sheehan
- **Activity code:** R34 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $690,388
- **Award type:** 1
- **Project period:** 2022-07-15 → 2025-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10524286

## Citation

> US National Institutes of Health, RePORTER application 10524286, Neural Basis of Facial Individual Recognition in Paper Wasps (1R34NS128868-01). Retrieved via AI Analytics 2026-05-26 from https://api.ai-analytics.org/grant/nih/10524286. Licensed CC0.

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