The overall goal of this work is an understanding of how sensory stimuli are transformed into motor plans, and in particular, how identical sensory stimuli can lead to different motor outputs under different behavioral conditions, or contexts. Single neuron recordings in behaving monkeys will be used to test the accuracy of a top-down model in which higher-level, context-specific processing originates in the frontal cortex, which then drives context-specific processing in the rest of the brain in a top-down fashion. An alternative, yet-untested hypothesis is that context-specific processing is instead distributed across many cortical areas, and particularly the posterior parietal cortex. The distribution of processing in the brain is likely to depend on the nature and complexity of the task being performed. Therefore, three different experimental paradigms will be employed. Each paradigm focuses on a different, important aspect of context-dependent processing.
Aim 1 focuses on decisions regarding how to respond to a particular stimulus, for example, with an eye or an arm movement.
Aim 2 focuses on simple context-dependent manipulations that occur in short term spatial memory.
Aim 3 focuses on the ability to rapidly switch between two different, arbitrary stimulus-response mappings. The general experimental approach involves cueing a particular behavioral context, and then measuring neuronal responses to that pure contextual cue in posterior parietal regions LIP and PRR, and in frontal regions FEF, SEF and DLPFC. Both the magnitude and the time course of these responses will be scrutinized across areas, as well as how these contextually driven responses interact with subsequent stimulus-driven activity. These experiments will provide specific information about how visual sensory stimuli are transformed into plans to move the eyes and arms. They will also address whether high level processing occurs primarily in the frontal cortex, or is distributed among several brain areas. These results will have important implications for our understanding of the interactions among different brain regions, which will contribute substantially to our ability to treat and rehabilitate brain-injured patients.
| Holmes, Charles D; Papadimitriou, Charalampos; Snyder, Lawrence H (2018) Dissociation of LFP Power and Tuning in the Frontal Cortex during Memory. J Neurosci 38:8177-8186 |
| Mooshagian, Eric; Snyder, Lawrence H (2018) Spatial eye-hand coordination during bimanual reaching is not systematically coded in either LIP or PRR. Proc Natl Acad Sci U S A 115:E3817-E3826 |
| Mooshagian, Eric; Wang, Cunguo; Holmes, Charles D et al. (2018) Single Units in the Posterior Parietal Cortex Encode Patterns of Bimanual Coordination. Cereb Cortex 28:1549-1567 |
| Kubanek, Jan; Snyder, Lawrence H (2017) Reward Size Informs Repeat-Switch Decisions and Strongly Modulates the Activity of Neurons in Parietal Cortex. Cereb Cortex 27:447-459 |
| Papadimitriou, Charalampos; White 3rd, Robert L; Snyder, Lawrence H (2017) Ghosts in the Machine II: Neural Correlates of Memory Interference from the Previous Trial. Cereb Cortex 27:2513-2527 |
| Chang, Steve W C; Calton, Jeffrey L; Lawrence, Bonnie M et al. (2016) Region-Specific Summation Patterns Inform the Role of Cortical Areas in Selecting Motor Plans. Cereb Cortex 26:2154-66 |
| Kubanek, Jan; Snyder, Lawrence H (2015) Matching Behavior as a Tradeoff Between Reward Maximization and Demands on Neural Computation. F1000Res 4:147 |
| Patel, Gaurav H; Yang, Danica; Jamerson, Emery C et al. (2015) Functional evolution of new and expanded attention networks in humans. Proc Natl Acad Sci U S A 112:9454-9 |
| Papadimitriou, Charalampos; Ferdoash, Afreen; Snyder, Lawrence H (2015) Ghosts in the machine: memory interference from the previous trial. J Neurophysiol 113:567-77 |
| Kubanek, Jan; Hill, N Jeremy; Snyder, Lawrence H et al. (2015) Cortical alpha activity predicts the confidence in an impending action. Front Neurosci 9:243 |
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