Abstract
Applying information theoretic measures to neuronal activity data enables the quantification of neuronal encoding quality. However, when the sample size is limited, a naïve estimation of the information content typically contains a systematic overestimation (upward bias), which may lead to misinterpretation of coding characteristics. This bias is exacerbated in Ca2+ imaging because of the temporal sparsity of elevated Ca2+ signals. Here, we introduce methods to correct for the bias in the naïve estimation of information content from limited sample sizes and temporally sparse neuronal activity. We demonstrate the higher accuracy of our methods over previous ones, when applied to Ca2+ imaging data recorded from the mouse hippocampus and primary visual cortex, as well as to simulated data with matching tuning properties and firing statistics. Our bias-correction methods allowed an accurate estimation of the information place cells carry about the animal’s position (spatial information) and uncovered the spatial resolution of hippocampal coding. Furthermore, using our methods, we found that cells with higher peak firing rates carry higher spatial information per spike and exposed differences between distinct hippocampal subfields in the long-term evolution of the spatial code. These results could be masked by the bias when applying the commonly used naïve calculation of information content. Thus, a bias-free estimation of information content can uncover otherwise overlooked properties of the neural code.
Highlights
A fundamental problem in neuroscience is to understand the nature of the neural code– namely, how much and which type of information is carried by the neuronal activity
Neuroscientists interested in understanding the nature of the neural code often apply methods derived from the mathematical framework of information theory to quantify the statistical relationship between neuronal activity and a certain variable of interest
The standard measures for estimating information content suffer from an upward bias when applied to small sample sizes, which may lead to misinterpretation of the data
Summary
A fundamental problem in neuroscience is to understand the nature of the neural code– namely, how much and which type of information is carried by the neuronal activity. Recent advances in Ca2+ imaging techniques allow the chronic readout of activity from hundreds of simultaneously recorded neurons in freely behaving mice [1–3] and tracking, with little ambiguity, the same neurons over multiple days [4]. Estimating information content from Ca2+ imaging data enables one to investigate the neural code in a population of cells and to study, for example, how the neuronal coding properties evolve over time during the learning of a specific behavioral task. Due to the finite amount of trials or stimulus repetitions that can be recorded in an experiment, estimations of MI in neuroscientific studies generally suffer from a significant positive error (upward bias) [10–13]
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