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Abstract:
Modern experimental technologies enable simultaneous recording of large neural populations. These high-dimensional data present a challenge for analysis. Recent work has focused on extracting low-dimensional dynamical trajectories that may underly such responses. Such
methods enable visualization and may also provide insight into neural computations. Previous work focuses on modeling a population’s dynamics without conditioning on external
stimuli. Our proposed technique integrates linear dimensionality reduction with a latent dynamical system model of neural activity. Under our model, population response is governed by a low-dimensional dynamical system with quadratic input. In this framework the number of parameters in grows linearly with population (size given fixed latent dimensionality). Hence it is computationally fast for large populations, unlike fully-connected models. Our method captures both noise correlations and low-dimensional stimulus selectivity through the simultaneous modeling of dynamics and stimulus dependence. This approach is particularly well-suited for studying the population activity of sensory cortices, where neurons often
have substantial receptive field overlap.
