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The impact of correlated variability on models of neural coding

Abstract

Variability is a prominent feature of neural systems: neural responses to repeated presentations of the same external stimulus will typically vary from trial to trial. Furthermore, neural variability exhibits pairwise correlations, commonly referred to as correlated variability. Correlated variability is a pervasive neural phenomenon that arises due to a variety of sources including shared input, biological noise, global fluctuations, and neural activity unobserved by experimental apparatuses. It is of theoretical interest because of its importance for models of neural coding: the existence of correlated variability can improve or harm neural coding depending on its structure. In this work, we examine how correlated variability impacts neural coding for both analyses on decoding efficacy and parametric models of neural activity. First, we demonstrate that correlated variability induced by noise sources common to a neural population can be manipulated by heterogeneous synaptic weighting to improve neural coding, even at the cost of amplifying the noise. Second, we demonstrate that correlated variability in neural data exhibits worse than chance decoding fidelity, and identify biological constraints in achieving optimal neural representations. Third, we examine how an improved inference algorithm for common parametric models can shape the scientific interpretation of common systems neuroscience models, despite the presence of correlated variability in the data. Lastly, we identify how omitting correlated variability arising from unobserved activity in parametric models of tuning and functional coupling can bias parametric estimates, and propose a new model and inference procedure to mitigate these biases. Together, our results highlight the importance of correlated variability on a wide range neural coding models.

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