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Interpretable deep learning for deconvolutional analysis of neural signals.

Bahareh Tolooshams^1,2,3, Sara Matias^1,4, Hao Wu^1,4

¹Center for Brain Science, Harvard University, Cambridge MA, 02138.

Biorxiv : the Preprint Server for Biology

|January 23, 2024

Summary

We introduce Deconvolutional Unrolled Neural Learning (DUNL), an interpretable deep learning method. DUNL links neural activity to network parameters, enabling mechanistic understanding of neural dynamics.

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Area of Science:

Computational Neuroscience
Machine Learning
Systems Neuroscience

Background:

Deep learning models for neural dynamics often lack interpretability.
Understanding the link between neural activity and network parameters is crucial.

Purpose of the Study:

To develop an interpretable deep learning method for analyzing neural population dynamics.
To establish a direct link between neural activity and network parameters using a generative model.

Main Methods:

Algorithm unrolling applied to design sparse deconvolutional neural networks.
Deconvolutional Unrolled Neural Learning (DUNL) framework developed.
Application to single-trial local signals across multiple brain areas and recording modalities.

Main Results:

DUNL provides interpretable network weights related to stimulus-driven activity.
Successfully deconvolved single-trial signals in various brain regions (midbrain, somatosensory thalamus, piriform cortex, striatum).
Uncovered multiplexed salience and reward prediction error signals in dopamine neurons.
Performed simultaneous event detection and characterization.
Characterized heterogeneous neural responses during naturalistic experiments.

Conclusions:

DUNL offers a mechanistic understanding of neural activity through interpretable deep learning.
The method is versatile for analyzing diverse neural recordings.
Advances interpretable AI for neuroscience research.