In vivo cell-type and brain region classification via multimodal contrastive learning

Area of Science:

• Neuroscience
• Computational Neuroscience
• Machine Learning

Background:

• Electrophysiological methods record neural activity but lack cell-type and brain region specificity without additional analysis.
• Accurate identification of neuronal populations is essential for understanding neural computation.

Purpose of the Study:

• To develop a scalable algorithm for identifying cell-type and brain region from electrophysiological recordings.
• To improve our understanding of neural computation by enabling precise neural population identification.

Main Methods:

• Developed a multimodal contrastive learning approach named Neuronal Embeddings via Multimodal contrastive learning (NEMO).
• Jointly embedded neural activity autocorrelations and extracellular waveforms.
• Fine-tuned the model for downstream tasks like cell-type and brain region classification.

Main Results:

• NEMO achieved state-of-the-art cell-type classification on an opto-tagged visual cortex dataset.
• NEMO demonstrated high accuracy in brain region classification using the International Brain Laboratory dataset.
• The approach effectively integrates multimodal neural data for classification.

Conclusions:

• NEMO offers a promising solution for accurate cell-type and brain region classification directly from electrophysiological data.
• This method advances the ability to analyze neural recordings without requiring post-hoc molecular or histological validation.
• The developed approach facilitates a deeper understanding of neural circuits and computation.