Predicting Single Neuron Responses of the Primary Visual Cortex with Deep Learning Model.
Adv Sci (Weinh)
; 11(15): e2305626, 2024 Apr.
Article
em En
| MEDLINE
| ID: mdl-38350735
ABSTRACT
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.
Palavras-chave
Texto completo:
1
Base de dados:
MEDLINE
Assunto principal:
Córtex Visual
/
Aprendizado Profundo
Tipo de estudo:
Prognostic_studies
/
Risk_factors_studies
Limite:
Animals
Idioma:
En
Ano de publicação:
2024
Tipo de documento:
Article