Perceptual decisions are based on sensory information encoded by populations of neurons overshort periods. To identify the most important aspects of the population code for guiding behavior, I variedvisual attention, which improves perception of an attended location or feature. The amount of informationencoded in a population of neurons is limited by both the variability of individual cortical neurons and bycorrelated variability that is shared across the population (noise correlations). I found that attentionalchanges in shared variability are likely the major contributor to the resulting behavioral improvement.These results suggest that studies of single neurons that ignore noise correlations miss perhaps the mostcrucial aspect of the way that responses of populations of neurons guide behavior, so it is necessary tomonitor the activity of populations of neurons rather than record one neuron at a time. The goal of this proposal is to use computational methods and multielectrode recordings in awakeprimates to understand the mechanism by which inter-neuronal correlations arise and the extent to whichthey can flexibly adapt to task demands. In my previous experiments I found that attentional modulationof firing rates and noise correlations is linked: the neurons that show the biggest firing rate changes(typically increases in multiplicative gain) show the biggest decreases in correlation. I will use thementored phase of this award to investigate the origin and flexibility of noise correlations by creating acomputational model to test whether a mechanism thought to underlie gain changes in single neurons inmany sensory, motor and cognitive processes (sensory normalization) could also cause modulation ofnoise correlations (Aim 1). The result that modulation of correlations accompanies all gain changeswould imply that correlation changes are a major factor in most cortical computations. My goal in the independent phase will be to experimentally investigate how flexible and adaptivecorrelation changes can be and the impact of correlations on communication between cortical areas.Noise correlations can either severely limit or improve the information available in a neuronal population,depending on the way that neuronal responses are combined. In the task I used in my previousexperiments, noise correlations limited population sensitivity, and attention adaptively decreasedcorrelations (as predicted by the normalization hypothesis).
In Aim 2, I will use a task in which noisecorrelations improve, rather than limit, performance to determine whether attention can adaptivelyincrease correlations.
In Aim 3, I will investigate the origin of noise correlations by measuring correlationsbetween different cortical areas and varying attention to determine whether correlations depend on thestrength of shared functional inputs. Collectively, these studies will have implications for the impact ofcorrelations on population coding and the way sensory information is transmitted from area to area andused to guide behavior.
Many neurological diseases including depression, schizophrenia, and attention deficit hyperactivity disorder (ADHD) are thought to involve networks of cortical neurons, so understanding the way that information is encoded and read out by neuronal populations will be critical for diagnosing and developing drug therapies to treat these diseases. The projects in this proposal will elucidate general mechanisms for population coding, and will in particular enhance our understanding of the neuronal mechanisms underlying attention. Deficits of attention such as ADHD are thought to affect as many as 5% of the children in the United States, and better understanding of the basic neuronal mechanisms related to attention is needed for guiding assessment, diagnosis, and treatment of attentional deficits.
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