Identifying neural dynamics using interventional state space models
Keywords: Causal dynamical systems, interventions, state space models, photostimulation, micro-stimulation
TL;DR: We develop interventional state space models (iSSM), a class of causal models that can predict neural responses to novel perturbations and identify neural dynamics.
Abstract: Neural circuits produce signals that are complex and nonlinear. To facilitate the understanding of neural dynamics, a popular approach is to fit state space models (SSM) to data and analyze the dynamics of the low-dimensional latent variables. Despite the power of SSM in explaining neural circuit dynamics, it has been shown that these models merely capture statistical associations in the data and cannot be causally interpreted. Therefore, an important research problem is to build models that can predict neural dynamics under causal manipulations. Here, we propose interventional state space models (iSSM), a class of causal models that can predict neural responses to novel perturbations. We draw on recent advances in causal dynamical systems and present theoretical results for the identifiability of iSSM. In simulations of the motor cortex, we show that iSSM can recover the true latents and the underlying dynamics. In addition, we illustrate two applications of iSSM in biological datasets. First, we apply iSSM to a dataset of calcium recordings from ALM neurons in mice during photostimulation and uncover dynamical mechanisms underlying short-term memory. Second, we apply iSSM to a dataset of electrophysiological recordings from macaque dlPFC recordings during micro-stimulation and show that it successfully predicts responses to unseen perturbations.
Primary Area: applications to neuroscience & cognitive science
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Submission Number: 13361
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