Abstract
Tuning curves and receptive fields are widely used to describe the selectivity of sensory neurons, but the relationship between firing rates and information is not always intuitive. Neither high firing rates nor high tuning curve gradients necessarily mean that stimuli at that part of the tuning curve are well represented by a neuron. Recent research has shown that trial-to-trial variability (noise) and population size can strongly affect which stimuli are most precisely represented by a neuron in the context of a population code (the best-encoded stimulus), and that different measures of information can give conflicting indications. Specifically, the Fisher information is greatest where the tuning curve gradient is greatest, such as on the flanks of peaked tuning curves, but the stimulus-specific information (SSI) is greatest at the tuning curve peak for small populations with high trial-to-trial variability. Previous research in this area has focussed upon unimodal (peaked) tuning curves, and in this article we extend these analyses to monotonic tuning curves. In addition, we examine how stimulus spacing in forced choice tasks affects the best-encoded stimulus. Our results show that, regardless of the tuning curve, Fisher information correctly predicts the best-encoded stimulus for large populations and where the stimuli are closely spaced in forced choice tasks. In smaller populations with high variability, or in forced choice tasks with widely-spaced choices, the best-encoded stimulus falls at the peak of unimodal tuning curves, but is more variable for monotonic tuning curves. Task, population size and variability all need to be considered when assessing which stimuli a neuron represents, but the best-encoded stimulus can be estimated on a case-by case basis using commonly available computing facilities.
Highlights
Mapping the response of a neuron to a range of stimuli by constructing a tuning curve or receptive field is one of the longest established and most widely used approaches in sensory neuroscience
This means that, for a given tuning curve gradient and variability, responses to stimuli on the tuning curve flank are more informative than they would be for a single neuron with a unimodal tuning curve
Summary The single neuron stimulus-specific information (SSI) for monotonically-tuned neurons is dependent upon the level of trial-to-trial variability and the marginal SSI is dependent upon the variability and the population size
Summary
Mapping the response of a neuron to a range of stimuli by constructing a tuning curve or receptive field is one of the longest established and most widely used approaches in sensory neuroscience. Despite the simplicity and widespread use of tuning curves, they remain open to misinterpretation; in particular, there is a tendency for neurons to be associated by default with the stimuli that trigger their strongest responses This is sometimes stated explicitly, but is implicit in the language used to describe response properties, for example in the term “preferred stimulus” or when a neuron is described as being selective for a particular stimulus. Even within the simplified framework of rate coding there are a number of measures that can be used to quantify the amount of information transmitted by a neuron about a specific stimulus, to construct informational tuning curves and identify the bestencoded stimuli These measures have distinct, but overlapping scopes of application and do not always yield similar predictions as to the best-encoded stimuli, so selecting the right measure is an important step in any analysis. Mathematical definitions of all three measures are given in the Materials and Methods Section
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