. In a number of systems including wind detection in the cricket, visual motion perception and coding of arm movement direction in the monkey and place cell response to position in the rat hippocampus, firing rates in a population of tuned neurons are correlated with a vector quantity. We examine and compare several methods that allow the coded vector to be reconstructed from measured firing rates. In cases where the neuronal tuning curves resemble cosines, linear reconstruction methods work as well as more complex statistical methods requiring more detailed information about the responses of the coding neurons. We present a new linear method, the optimal linear estimator (OLE), that on average provides the best possible linear reconstruction. This method is compared with the more familiar vector method and shown to produce more accurate reconstructions using far fewer recorded neurons. Introduction To determine how information is represented by nervous systems, we need to understand ...

Tuning curves are widely used to characterize the responses of sensory neurons to external stimuli, but there is an ongoing debate as to their role in sensory processing. Commonly, it is assumed that a neuron’s role is to encode the stimulus at the tuning curve peak, because high firing rates are the neuron’s most distinct responses. In contrast, many theoretical and empirical studies have noted that nearby stimuli are most easily discriminated in high-slope regions of the tuning curve. Here, we demonstrate that both intuitions are correct, but that their relative importance depends on the experimental context and the level of variability in the neuronal response. Using three different information-based measures of encoding applied to experimentally measured sensory neurons, we show how the best-encoded stimulus can transition from high-slope to high-firing-rate regions of the tuning curve with increasing noise level. We further show that our results are consistent with recent experimental findings that correlate neuronal sensitivities with perception and behavior. This study illustrates the importance of the noise level in determining the encoding properties of sensory neurons and provides a unified framework for interpreting how the tuning curve and neuronal variability relate to the overall role of the neuron in sensory encoding. Citation: Butts DA, Goldman MS (2006) Tuning curves, neuronal variability, and sensory coding. PLoS Biol 4(4): e92.

Directionally selective mechanosensory afferents in the cricket cereal sensory system form a map of air current direction in the terminal abdominal ganglion. The global organization of this map was revealed by studying the anatomical relationships between an ensemble of sensory afferents that represented the entire range of receptor hair directional sensitivities on the sensory epithelium. The shapes and three-dimensional posi-tions of the terminal arborizations of these cells were highly conserved across animals. Afferents with similar directional sensitivities arborized near each other within the map, and their terminal arborizations showed significant anatomical overlap. There was a clear global organization pattern of afferents within the map: they were organized into a spiral shape, with stimulus direction mapped continuously around the spiral. These results The functional characteristics of any neural map depend on two principal factors: the physiological properties of the neurons that comprise the map, and the global structural organization of their terminal arborizations within the CNS. The anatomical projections of the neurons in a neural map form the template on which their functional properties are represented and, hence, define the nature of the functional interface to the next computational stage of the CNS. The goals of this study were to examine quantitatively how the projections of an array of receptor neurons are organized anatomically in the CNS to represent specific functional characteristics and to assess the effect of the anatomical and physiological characteristics of the receptor array on those representations. The system we studied was the cricket cereal sensory system. This system is capable of detecting the direction and dynamics of behaviorally relevant air currents with great accuracy and preci-

. In a number of systems including wind detection in the cricket, visual motion perception and coding of arm movement direction in the monkey and place cell response to position in the rat hippocampus, firing rates in a population of tuned neurons are correlated with a vector quantity. We examine and compare several methods that allow the coded vector to be reconstructed from measured firing rates. In cases where the neuronal tuning curves resemble cosines, linear reconstruction methods work as well as more complex statistical methods requiring more detailed information about the responses of the coding neurons. We present a new linear method, the optimal linear estimator (OLE), that on average provides the best possible linear reconstruction. This method is compared with the more familiar vector method and shown to produce more accurate reconstructions using far fewer recorded neurons. Introduction To determine how information is represented by nervous systems, we need to understand...