Work

Dual-region deep learning for odor discrimination from local field potentials

Year: 2026

Type: article

Abstract: Decoding odor information in the mammalian brain is a dynamic process involving interconnected brain regions, such as the Olfactory Bulb (OB) and Piriform Cortex (PCx). Isolating the functional contribution of each region to odor discrimination is challenging due to the complexity of simultaneous multi-region recordings and recurrent feedforward/feedback pathways. To address this, we introduce the Dual-Region Network (DRN), a novel deep learning architecture designed to fuse Welch-derived power spectra (1–600 Hertz) from local field potentials (LFP) recordings in the OB and PCx. The DRN combines residual layers with an attention-based fusion mechanism, providing a computationally lightweight, end-to-end decoding framework. By evaluating the DRN on a public dataset, we achieved 90% accuracy for four odors and 73% for seven odors, significantly outperforming re-implemented deep learning baselines (Electroencephalogram Network (EEGNet): 62.50%, Shallow Convolutional Network (Shallow ConvNet): 62.40%) and approaching the performance of invasive single-unit decoders—without requiring spike sorting. Crucially, LFP-based decoding is advantageous in spatially segregated regions: unlike single-unit responses, which vary with electrode placement relative to active glomeruli, LFPs capture net population dynamics. This yields a robust encoding of odor identity, largely independent of the precise recording location or the quality of unit isolation. Our model performs unsupervised selection of stimulus-selective frequency components, leading to high odor-discrimination power. The ablation simulation confirms that odor molecule encoding is strongly influenced by odor delivery time and region-specific frequency components. The DRN offers a reproducible approach to dual-region neural decoding, providing a transferable framework for dissecting distributed computations within brain circuits.

Source: Engineering Applications of Artificial Intelligence

DOI: 10.1016/j.engappai.2026.114436

Authors: Ali Zareh, Muhammed İkbal Alp, Cağatay Aydın, Mehmet Kemal Özdemir

Institutions: Istanbul Medipol University, KU Leuven, Ankara Medipol Üniversitesi

Language: English

Topic: Olfactory and Sensory Function Studies

Subfield: Sensory Systems

Field: Neuroscience

Domain: Life Sciences

Open Access status: hybrid

Estimated APC paid: $3,200

Funders: Nvidia, Türkiye Bilimsel ve Teknolojik Araştırma Kurumu, Ulusal Metroloji Enstitüsü, Türkiye Bilimsel ve Teknolojik Araştirma Kurumu