Abstract
Here we present our Python toolbox "MR. Estimator" to reliably estimate the intrinsic timescale from electrophysiologal recordings of heavily subsampled systems. Originally intended for the analysis of time series from neuronal spiking activity, our toolbox is applicable to a wide range of systems where subsampling-the difficulty to observe the whole system in full detail-limits our capability to record. Applications range from epidemic spreading to any system that can be represented by an autoregressive process. In the context of neuroscience, the intrinsic timescale can be thought of as the duration over which any perturbation reverberates within the network; it has been used as a key observable to investigate a functional hierarchy across the primate cortex and serves as a measure of working memory. It is also a proxy for the distance to criticality and quantifies a system's dynamic working point.
Highlights
Recent discoveries in the field of computational neuroscience suggest a major role of the socalled intrinsic timescale for functional brain dynamics [1,2,3,4,5,6,7,8]
Decaying correlations are commonly found in recurrent networks, where the intrinsic timescale can be related to information storage and transfer [10,11,12]
Such decaying autocorrelations are found in the network-spiking-dynamics recorded in the brain: Here, the intrinsic timescale serves as a measure to quantify working memory [3, 4] and unravels a temporal hierarchy of processing in primates [1, 2]
Summary
Recent discoveries in the field of computational neuroscience suggest a major role of the socalled intrinsic timescale for functional brain dynamics [1,2,3,4,5,6,7,8]. Autocorrelations and the intrinsic timescale can be derived from single neuron activity, they characterize the dynamics within the whole recurrent network. In experiments we approach this level: we typically sample only a small part of the system, sometimes only a single or a dozen of units This subsampling problem is especially problematic in neuroscience, where even the most advanced electrode measurements can record at most a few thousand out of the billions of neurons in the brain [16, 17]. The main advantage of using our toolbox over a custom implementation to determine intrinsic timescales is that it provides a consistent way that can be adopted across studies. It supports trial structures and we demonstrate how multiple trials can be combined to compensate for short individual trials.
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