Predicting Single Neuron Responses of the Primary Visual Cortex with Deep Learning Model

• Published in: Advanced science Vol. 11; no. 15; pp. e2305626 - n/a
• Main Authors: Deng, Kaiwen, Schwendeman, Peter S., Guan, Yuanfang
• Format: Journal Article
• Language: English
• Published: Germany John Wiley & Sons, Inc 01-04-2024; John Wiley and Sons Inc; Wiley
• Subjects: Algorithms; Competition; Datasets; deep learning; Methods; neuron responses prediction; Neurons; primary visual cortex; neuron responses prediction; deep learning; primary visual cortex
• ISSN: 2198-3844; 2198-3844
• DOI: 10.1002/advs.202305626

Modeling neuron responses to stimuli can shed light on next‐generation technologies such as brain‐chip interfaces. Furthermore, high‐performing models can serve to help formulate hypotheses and reveal the mechanisms underlying neural responses. Here the state‐of‐the‐art computational model is presented for predicting single neuron responses to natural stimuli in the primary visual cortex (V1) of mice. The algorithm incorporates object positions and assembles multiple models with different train‐validation data, resulting in a 15%–30% improvement over the existing models in cross‐subject predictions and ranking first in the SENSORIUM 2022 Challenge, which benchmarks methods for neuron‐specific prediction based on thousands of images. Importantly, The model reveals evidence that the spatial organizations of V1 are conserved across mice. This model will serve as an important noninvasive tool for understanding and utilizing the response patterns of primary visual cortex neurons.
This study presents a computational model that advances the prediction of single neuron responses to natural stimuli in the mouse primary visual cortex (V1) by 15%–30% with the incorporation of object positions and multiple model ensembles. It computationally reveals the neuron properties and spatial organizations across multiple mice, offering a noninvasive tool for studying the primary visual cortex activity.