# Synaptic Organization of Simple Cell Receptive Fields

> **NIH NIH R01** · UNIVERSITY OF PENNSYLVANIA · 2020 · $569,228

## Abstract

PROJECT SUMMARY
Image representation in primary visual cortex depends critically on the spatiotemporal pattern of
convergence of lateral geniculate axons as well as on the dynamic properties of thalamocortical
synapses. Thalamic input represents only a fraction of the excitatory drive to cortical cells in
layer 4 ,but dominates their visual response patterns. We will use electrophysiological methods
in vivo to record visual responses simultaneously from thalamorecipient neurons in primary
visual cortex and several of their input cells in the lateral geniculate nucleus. In Aim 1, we will
test the hypothesis that inhibition controls the temporal course of simple cells in layer 4. In Aim
2, we will characterize the properties of the postsynaptic potentials from single LGN neurons
onto excitatory and inhibitory simple cells in layer 4. Finally, in Aim 3, we will use dynamic clamp
in layer 4 simple cells to study convergent input from combinations of single LGN neurons
(using knowledge obtained in Aims 1 and 2) without concomitant activation of cortical circuits.
We will estimate the contributions of local inhibition in establishing L4 simple cell visual
responses. These studies will generate critical insight into the transformation of visual
information that takes place in the thalamocortical synapse.

## Key facts

- **NIH application ID:** 9933926
- **Project number:** 5R01EY027205-04
- **Recipient organization:** UNIVERSITY OF PENNSYLVANIA
- **Principal Investigator:** Diego Contreras
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $569,228
- **Award type:** 5
- **Project period:** 2017-08-01 → 2022-05-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9933926

## Citation

> US National Institutes of Health, RePORTER application 9933926, Synaptic Organization of Simple Cell Receptive Fields (5R01EY027205-04). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/9933926. Licensed CC0.

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