Interpretable deep learning for deconvolutional analysis of neural signals.

ABSTRACT: The widespread adoption of deep learning to build models that capture the dynamics of neural populations is typically based on "black-box" approaches that lack an interpretable link between neural activity and network parameters. Here, we propose to apply algorithm unrolling, a method for interpretable deep learning, to design the architecture of sparse deconvolutional neural networks and obtain a direct interpretation of network weights in relation to stimulus-driven single-neuron activity through a generative model. We characterize our method, referred to as deconvolutional unrolled neural learning (DUNL), and show its versatility by applying it to deconvolve single-trial local signals across multiple brain areas and recording modalities. To exemplify use cases of our decomposition metho

SUBMITTER: Tolooshams B 

PROVIDER: S-EPMC10802267 | biostudies-literature | 2024 Dec

REPOSITORIES: biostudies-literature