A main focus of systems neuroscience is to understand how sensory information is encoded and used to guide behavior. Perceptual decision-making, like nearly all normal behavioral processes and disorders of the nervous system, is thought to involve the activity of large groups of neurons. Technical limitations, however, have forced most physiological studies to focus on single neurons. These studies have provided many important insights, but they necessarily miss key information about the relationship between groups of sensory neurons and decisions. For example, single neuron responses cannot tell us how that neuron's activity interacts and is combined with that of other neurons within or between cortical areas. Furthermore, my prior work showed that the cognitive state of even a well-trained subject fluctuates greatly from moment to moment, with striking consequences on performance on perceptual tasks. Therefore, combining information from single neurons recorded at different times produces an average over many behavioral states. The experiments in this proposal use the activity of many simultaneously recorded neurons while animals perform a visual discrimination task to track each perceptual decision while it is in progress. Our goal is to understand how the responses of neurons with different physiological, visual and cognitive properties who come from different cortical areas and interact with nearby neurons in different ways are combined to drive decisions.
In Specific Aim 1, we will ask how the responses of neurons with different functional properties are combined to guide decisions by assessing the relationship between the animal's choices and the activity of neurons that differ in their ability to encode the relevant information or are modulated differently by cognitive factors such as visual attention.
In Aim 2, we will assess the validity of current models of decision-making by determining the effect of shared (or correlated) response variability on perceptual performance. The responses from a few dozen simultaneously recorded neurons will provide a means of assessing the correlation structure of a large population and isolating the effects of correlated variability from accompanying changes in firing rates.
In Aim 3, we will examine the role of neurons in different visual areas in decisions, ask whether there is complementary choice-related activity in different areas, and determine whether the role of each area is flexible. We will record simultaneously from groups of neurons in V4, a ventral stream area, and MT, a dorsal stream area, to determine whether we can better predict the animal's decisions from groups of neurons in the two areas together or from one area alone. We will also adjust the visual stimulus to favor the tuning properties of neurons in each of the two areas to see whether the role of each area in decisions depends on the suitability of its neurons for the particular perceptual task. We hope that extending the study of decision-making to populations of neurons will improve our understanding of the underlying neuronal mechanisms and guide future experimental and theoretical work.

Public Health Relevance

Many neurological diseases including depression, schizophrenia, and attention deficit hyperactivity disorder are thought to involve networks of cortical neurons, so understanding the way that information is encoded and decoded by neuronal populations will be critical for diagnosing and developing drug therapies to treat these diseases. The proposed projects will elucidate general mechanisms for population coding and the relationship between the activity of groups of neurons and behavioral output.

National Institute of Health (NIH)
National Eye Institute (NEI)
Research Project (R01)
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Special Emphasis Panel (SPC)
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Steinmetz, Michael A
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University of Pittsburgh
Schools of Arts and Sciences
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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; Alberts, Joshua J; Cohen, Marlene R (2016) Relating normalization to neuronal populations across cortical areas. J Neurophysiol 116:1375-86
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

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