Perceptual decisions are based on sensory information encoded by populations of neurons over short periods. To identify the most important aspects of the population code for guiding behavior, I varied visual attention, which improves perception of an attended location or feature. The amount of information encoded in a population of neurons is limited by both the variability of individual cortical neurons and by correlated variability that is shared across the population (noise correlations). I found that attentional changes 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 most crucial aspect of the way that responses of populations of neurons guide behavior, so it is necessary to monitor 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 awake primates to understand the mechanism by which inter-neuronal correlations arise and the extent to which they can flexibly adapt to task demands. In my previous experiments I found that attentional modulation of 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 the mentored phase of this award to investigate the origin and flexibility of noise correlations by creating a computational model to test whether a mechanism thought to underlie gain changes in single neurons in many sensory, motor and cognitive processes (sensory normalization) could also cause modulation of noise correlations (Aim 1). The result that modulation of correlations accompanies all gain changes would 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 adaptive correlation 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 previous experiments, noise correlations limited population sensitivity, and attention adaptively decreased correlations (as predicted by the normalization hypothesis).
In Aim 2, I will use a task in which noise correlations improve, rather than limit, performance to determine whether attention can adaptively increase correlations.
In Aim 3, I will investigate the origin of noise correlations by measuring correlations between different cortical areas and varying attention to determine whether correlations depend on the strength of shared functional inputs. Collectively, these studies will have implications for the impact of correlations on population coding and the way sensory information is transmitted from area to area and used to guide behavior.

Public Health Relevance

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.

Agency
National Institute of Health (NIH)
Institute
National Eye Institute (NEI)
Type
Research Transition Award (R00)
Project #
5R00EY020844-05
Application #
8541019
Study Section
Special Emphasis Panel (NSS)
Program Officer
Steinmetz, Michael A
Project Start
2010-08-01
Project End
2014-08-31
Budget Start
2013-09-01
Budget End
2014-08-31
Support Year
5
Fiscal Year
2013
Total Cost
$233,998
Indirect Cost
$38,854
Name
University of Pittsburgh
Department
Neurosciences
Type
Schools of Arts and Sciences
DUNS #
004514360
City
Pittsburgh
State
PA
Country
United States
Zip Code
15213
Ni, A M; Ruff, D A; Alberts, J J et al. (2018) Learning and attention reveal a general relationship between population activity and behavior. Science 359:463-465
Ruff, Douglas A; Cohen, Marlene R (2017) A normalization model suggests that attention changes the weighting of inputs between visual areas. Proc Natl Acad Sci U S A 114:E4085-E4094
Kanashiro, Tatjana; Ocker, Gabriel Koch; Cohen, Marlene R et al. (2017) Attentional modulation of neuronal variability in circuit models of cortex. Elife 6:
Oby, Emily R; Perel, Sagi; Sadtler, Patrick T et al. (2016) Extracellular voltage threshold settings can be tuned for optimal encoding of movement and stimulus parameters. J Neural Eng 13:036009
Ruff, Douglas A; Cohen, Marlene R (2016) Stimulus Dependence of Correlated Variability across Cortical Areas. J Neurosci 36:7546-56
Ruff, Douglas A; Cohen, Marlene R (2016) Attention Increases Spike Count Correlations between Visual Cortical Areas. J Neurosci 36:7523-34
Rabinowitz, Neil C; Goris, Robbe L; Cohen, Marlene et al. (2015) Attention stabilizes the shared gain of V4 populations. Elife 4:e08998
Mayo, J Patrick; Cohen, Marlene R; Maunsell, John H R (2015) A Refined Neuronal Population Measure of Visual Attention. PLoS One 10:e0136570
Ruff, Douglas A; Cohen, Marlene R (2014) Global cognitive factors modulate correlated response variability between V4 neurons. J Neurosci 34:16408-16
Ruff, Douglas A; Cohen, Marlene R (2014) Attention can either increase or decrease spike count correlations in visual cortex. Nat Neurosci 17:1591-7

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