Although many studies have shown that attention to a stimulus can enhance the responses of individual cortical sensory neurons, little is known about how attention accomplishes this change in response. Here, we propose that attention-based changes in neuronal responses depend on the same response normalization mechanism that adjusts sensory responses whenever multiple stimuli are present. We have implemented a model of attention that assumes that attention works only through this normalization mechanism, and show that it can replicate key effects of attention. The model successfully explains how attention changes the gain of responses to individual stimuli and also why modulation by attention is more robust and not a simple gain change when multiple stimuli are present inside a neuron's receptive field. Additionally, the model accounts well for physiological data that measure separately attentional modulation and sensory normalization of the responses of individual neurons in area MT in visual cortex. The proposal that attention works through a normalization mechanism sheds new light a broad range of observations on how attention alters the representation of sensory information in cerebral cortex.

The effects of attention on neuronal responses in visual cortex have been likened to a change in stimulus contrast. Attention and stimulus contrast both modulate the magnitude of neuronal responses. However, changes in stimulus contrast also affect the latency of visual responses. Although many neurophysiological studies have examined how attention affects the strength of neuronal responses, few have considered whether attention affects neuronal latencies. To compare directly the effects of stimulus contrast and attention, we recorded responses from individual neurons in area V4 of macaque monkeys while they performed a task that independently controlled spatial attention and stimulus contrast. As expected, changes in stimulus contrast affected both the magnitude and latency of neuronal responses. Although attention had the expected effects on the magnitudes of neuronal responses, we did not detect statistically reliable changes in neuronal latency. A direct comparison of the effects of contrast and attention revealed a reliable difference. When a shift in spatial attention decreased response magnitude, response latency increased much less than when the same magnitude change was caused by reducing stimulus contrast. Thus, attention is distinct from contrast in the way it affects the relationship between neuronal response magnitude and latency.

Analysis of the neural code for sensory-motor latency in smooth pursuit eye movements reveals general principles of neural variation and the specific origin of motor latency. The trial-by-trial variation in neural latency in MT comprises: a shared component expressed as neuron-neuron latency correlations; and an independent component that is local to each neuron. The independent component arises heavily from fluctuations in the underlying probability of spiking with an unexpectedly small contribution from the stochastic nature of spiking itself. The shared component causes the latency of single neuron responses in MT to be weakly predictive of the behavioral latency of pursuit. Neural latency deeper in the motor system is more strongly predictive of behavioral latency. A model reproduces both the variance of behavioral latency and the neuron-behavior latency correlations in MT if it includes realistic neural latency variation, neuron-neuron latency correlations in MT, and noisy gain control downstream from MT.

Descriptions of how attention modulates neuronal responses suggest that the strength of its effects depends on stimulus conditions. Attention to an isolated stimulus in the receptive field of an individual neuron typically produces a moderate enhancement of the cell's response, but neuronal responses are often strongly modulated when attention is shifted between multiple stimuli that lie within the receptive field. However, previous reports have not compared these stimulus effects under equivalent conditions, so differences in task difficulty could have been responsible for much of the difference. Consequently, the quantitative effects of stimulus conditions have remained unknown, and it has not been possible to address the question of whether the differences that have been observed could be explained by a single mechanism. We measured the attentional modulation of the responses of 70 single neurons in area MT of two rhesus monkeys using a task designed to keep attention stable across different stimulus configurations. We found that attentional modulation was indeed much stronger when more than one stimulus was within the receptive field. Nevertheless, the broad range of attentional modulations seen across the different conditions could be readily explained by single mechanism. The neurophysiological data from all stimulus conditions were well fit by a model in which attention acts via a response normalization mechanism (Lee and Maunsell, 2009). Collectively, these results validate previous impressions of the effects of stimulus configuration on attentional modulation, and add support to hypothesis that attention modulation depends on a response normalization mechanism.