Artificial neural networks in action for an automated cell-type classification of biological neural networks

TL;DR

This paper introduces an automated method using deep learning to classify neuronal cell types based solely on activity signals, overcoming limitations of traditional molecular and cellular feature-based approaches.

Contribution

It presents the first automated classification approach relying on neuronal activity data, demonstrating high accuracy with deep learning models.

Findings

Deep learning models achieved high classification accuracy.

The method outperforms traditional feature-based approaches.

It enables rapid, automated neuron type identification.

Abstract

Identification of different neuronal cell types is critical for understanding their contribution to brain functions. Yet, automated and reliable classification of neurons remains a challenge, primarily because of their biological complexity. Typical approaches include laborious and expensive immunohistochemical analysis while feature extraction algorithms based on cellular characteristics have recently been proposed. The former rely on molecular markers, which are often expressed in many cell types, while the latter suffer from similar issues: finding features that are distinctive for each class has proven to be equally challenging. Moreover, both approaches are time consuming and demand a lot of human intervention. In this work we establish the first, automated cell-type classification method that relies on neuronal activity rather than molecular or cellular features. We test our method on a real-world dataset comprising of raw calcium activity signals for four neuronal types. We compare the performance of three different deep learning models and demonstrate that our method can achieve automated classification of neuronal cell types with unprecedented accuracy.