Abstract
Neural responses are known to be variable. In order to understand how this neural variability constrains behavioral performance, we need to be able to measure the reliability with which a sensory stimulus is encoded in a given population. However, such measures are challenging for two reasons: First, they must take into account noise correlations which can have a large influence on reliability. Second, they need to be as efficient as possible, since the number of trials available in a set of neural recording is usually limited by experimental constraints. Traditionally, cross-validated decoding has been used as a reliability measure, but it only provides a lower bound on reliability and underestimates reliability substantially in small datasets. We show that, if the number of trials per condition is larger than the number of neurons, there is an alternative, direct estimate of reliability which consistently leads to smaller errors and is much faster to compute. The superior performance of the direct estimator is evident both for simulated data and for neuronal population recordings from macaque primary visual cortex. Furthermore we propose generalizations of the direct estimator which measure changes in stimulus encoding across conditions and the impact of correlations on encoding and decoding, typically denoted by Ishuffle and Idiag respectively.
Highlights
The advent of technical advances like multi-electrode recordings and calcium imaging allows the simultaneous recording of an ever increasing number of neurons
Neural responses in sensory areas vary substantially upon repeated presentations of the same stimulus, and this limits the reliability with which two similar stimuli can be discriminated by any read-out of neural activity
Traditional methods for quantifying Fisher information rely on decoding neural activity; optimizing a decoder requires larger amounts of data than available in typical experiments, and as a result
Summary
The advent of technical advances like multi-electrode recordings and calcium imaging allows the simultaneous recording of an ever increasing number of neurons The availability of this data allows us to explore the qualitative properties of the neural code and the reliability of coding. In contrast to the downstream circuitry, which potentially had a lifetime of experience to learn the statistics of neural responses, an experimenter recording neural activity only has finite data both to fit the response statistics and to assess the coding reliability. This requires efficient methods to achieve an accurate estimate of reliability with as few trials as possible. Fisher information quantifies the amount of information that can be extracted by the ideal observer (or, equivalently, an optimal decoder)
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