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Transition from Target to Gaze Coding in Primate Frontal Eye Field during Memory Delay and Memory-Motor Transformation.

Sajad A, Sadeh M, Yan X, Wang H, Crawford JD - eNeuro (2016)

Bottom Line: We treated neural population codes as a continuous spatiotemporal variable by dividing the space spanning T and G into intermediate T-G models and dividing the task into discrete steps through time.We found that FEF delay activity, especially in visuomovement cells, progressively transitions from T through intermediate T-G codes that approach, but do not reach, G.This was followed by a final discrete transition from these intermediate T-G delay codes to a "pure" G code in movement cells without delay activity.

View Article: PubMed Central - HTML - PubMed

Affiliation: Centre for Vision Research, York University, Toronto, Ontario M3J 1P3, Canada; Neuroscience Graduate Diploma Program, York University, Toronto, Ontario M3J 1P3, Canada; Department of Biology, York University, Toronto, Ontario M3J 1P3, Canada.

ABSTRACT
The frontal eye fields (FEFs) participate in both working memory and sensorimotor transformations for saccades, but their role in integrating these functions through time remains unclear. Here, we tracked FEF spatial codes through time using a novel analytic method applied to the classic memory-delay saccade task. Three-dimensional recordings of head-unrestrained gaze shifts were made in two monkeys trained to make gaze shifts toward briefly flashed targets after a variable delay (450-1500 ms). A preliminary analysis of visual and motor response fields in 74 FEF neurons eliminated most potential models for spatial coding at the neuron population level, as in our previous study (Sajad et al., 2015). We then focused on the spatiotemporal transition from an eye-centered target code (T; preferred in the visual response) to an eye-centered intended gaze position code (G; preferred in the movement response) during the memory delay interval. We treated neural population codes as a continuous spatiotemporal variable by dividing the space spanning T and G into intermediate T-G models and dividing the task into discrete steps through time. We found that FEF delay activity, especially in visuomovement cells, progressively transitions from T through intermediate T-G codes that approach, but do not reach, G. This was followed by a final discrete transition from these intermediate T-G delay codes to a "pure" G code in movement cells without delay activity. These results demonstrate that FEF activity undergoes a series of sensory-memory-motor transformations, including a dynamically evolving spatial memory signal and an imperfect memory-to-motor transformation.

No MeSH data available.


Related in: MedlinePlus

Single-neuron example and population results for V neurons. A shows the time-normalized spike density profile for an example V neuron (top) and the data points corresponding to the spatially tuned time steps across 16 half-overlapping time steps (bottom). The RF plot corresponding to the highlighted time step (bottom panel, light red circle with green boarders; first time-step here) is shown with the spatial code highlighted above the plot. B shows the population time-normalized post-stimulus time histogram (mean ± SEM) and the mean (±SEM) of the spatially tuned data points at these time steps across the V population. Colored data points (bottom) correspond to time steps at which the population spatial coherence was significantly higher than the pretarget baseline and gray shades correspond to eliminated time steps, with spatial coherence indistinguishable from pretarget baseline. The histogram shows the percentage of neurons at each time step that exhibited spatial tuning. The baseline firing rate is calculated based on the average firing rate in the 100 ms pretarget period is shown by the solid horizontal lines in spike density plots (A, B, top). For reference, the approximate visual, delay, and motor epochs are depicted at the top of the panels.
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Figure 5: Single-neuron example and population results for V neurons. A shows the time-normalized spike density profile for an example V neuron (top) and the data points corresponding to the spatially tuned time steps across 16 half-overlapping time steps (bottom). The RF plot corresponding to the highlighted time step (bottom panel, light red circle with green boarders; first time-step here) is shown with the spatial code highlighted above the plot. B shows the population time-normalized post-stimulus time histogram (mean ± SEM) and the mean (±SEM) of the spatially tuned data points at these time steps across the V population. Colored data points (bottom) correspond to time steps at which the population spatial coherence was significantly higher than the pretarget baseline and gray shades correspond to eliminated time steps, with spatial coherence indistinguishable from pretarget baseline. The histogram shows the percentage of neurons at each time step that exhibited spatial tuning. The baseline firing rate is calculated based on the average firing rate in the 100 ms pretarget period is shown by the solid horizontal lines in spike density plots (A, B, top). For reference, the approximate visual, delay, and motor epochs are depicted at the top of the panels.

Mentions: The spatiotemporal progression of the neuronal code was analyzed by plotting the best-fit model (y-axis) as a function of the discretely sampled time steps (x-axis). To visualize these trends (and for the population analysis in the next section), we performed a nonparametric fit to these data for each neuron. Only data corresponding to spatially tuned time steps contributed to the fit. Fit values were included for every time step whose two neighboring time steps (both before and after) exhibited spatial tuning. The fit was discontinued for the range at which at least two consecutive time steps were not spatially tuned. A Gaussian kernel with a bandwidth of 1 time step was used for nonparametric fitting of these data. This choice was made conservatively to avoid oversmoothing of the data. As can be noted in Figures 5, 6, 8, 9, and 10, the fit values closely matched the data points obtained for individual neurons. Unless stated otherwise, we used the fit values, rather than individual data points, for statistical tests reported in this study, because they were less likely to be influenced by outliers.


Transition from Target to Gaze Coding in Primate Frontal Eye Field during Memory Delay and Memory-Motor Transformation.

Sajad A, Sadeh M, Yan X, Wang H, Crawford JD - eNeuro (2016)

Single-neuron example and population results for V neurons. A shows the time-normalized spike density profile for an example V neuron (top) and the data points corresponding to the spatially tuned time steps across 16 half-overlapping time steps (bottom). The RF plot corresponding to the highlighted time step (bottom panel, light red circle with green boarders; first time-step here) is shown with the spatial code highlighted above the plot. B shows the population time-normalized post-stimulus time histogram (mean ± SEM) and the mean (±SEM) of the spatially tuned data points at these time steps across the V population. Colored data points (bottom) correspond to time steps at which the population spatial coherence was significantly higher than the pretarget baseline and gray shades correspond to eliminated time steps, with spatial coherence indistinguishable from pretarget baseline. The histogram shows the percentage of neurons at each time step that exhibited spatial tuning. The baseline firing rate is calculated based on the average firing rate in the 100 ms pretarget period is shown by the solid horizontal lines in spike density plots (A, B, top). For reference, the approximate visual, delay, and motor epochs are depicted at the top of the panels.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4829728&req=5

Figure 5: Single-neuron example and population results for V neurons. A shows the time-normalized spike density profile for an example V neuron (top) and the data points corresponding to the spatially tuned time steps across 16 half-overlapping time steps (bottom). The RF plot corresponding to the highlighted time step (bottom panel, light red circle with green boarders; first time-step here) is shown with the spatial code highlighted above the plot. B shows the population time-normalized post-stimulus time histogram (mean ± SEM) and the mean (±SEM) of the spatially tuned data points at these time steps across the V population. Colored data points (bottom) correspond to time steps at which the population spatial coherence was significantly higher than the pretarget baseline and gray shades correspond to eliminated time steps, with spatial coherence indistinguishable from pretarget baseline. The histogram shows the percentage of neurons at each time step that exhibited spatial tuning. The baseline firing rate is calculated based on the average firing rate in the 100 ms pretarget period is shown by the solid horizontal lines in spike density plots (A, B, top). For reference, the approximate visual, delay, and motor epochs are depicted at the top of the panels.
Mentions: The spatiotemporal progression of the neuronal code was analyzed by plotting the best-fit model (y-axis) as a function of the discretely sampled time steps (x-axis). To visualize these trends (and for the population analysis in the next section), we performed a nonparametric fit to these data for each neuron. Only data corresponding to spatially tuned time steps contributed to the fit. Fit values were included for every time step whose two neighboring time steps (both before and after) exhibited spatial tuning. The fit was discontinued for the range at which at least two consecutive time steps were not spatially tuned. A Gaussian kernel with a bandwidth of 1 time step was used for nonparametric fitting of these data. This choice was made conservatively to avoid oversmoothing of the data. As can be noted in Figures 5, 6, 8, 9, and 10, the fit values closely matched the data points obtained for individual neurons. Unless stated otherwise, we used the fit values, rather than individual data points, for statistical tests reported in this study, because they were less likely to be influenced by outliers.

Bottom Line: We treated neural population codes as a continuous spatiotemporal variable by dividing the space spanning T and G into intermediate T-G models and dividing the task into discrete steps through time.We found that FEF delay activity, especially in visuomovement cells, progressively transitions from T through intermediate T-G codes that approach, but do not reach, G.This was followed by a final discrete transition from these intermediate T-G delay codes to a "pure" G code in movement cells without delay activity.

View Article: PubMed Central - HTML - PubMed

Affiliation: Centre for Vision Research, York University, Toronto, Ontario M3J 1P3, Canada; Neuroscience Graduate Diploma Program, York University, Toronto, Ontario M3J 1P3, Canada; Department of Biology, York University, Toronto, Ontario M3J 1P3, Canada.

ABSTRACT
The frontal eye fields (FEFs) participate in both working memory and sensorimotor transformations for saccades, but their role in integrating these functions through time remains unclear. Here, we tracked FEF spatial codes through time using a novel analytic method applied to the classic memory-delay saccade task. Three-dimensional recordings of head-unrestrained gaze shifts were made in two monkeys trained to make gaze shifts toward briefly flashed targets after a variable delay (450-1500 ms). A preliminary analysis of visual and motor response fields in 74 FEF neurons eliminated most potential models for spatial coding at the neuron population level, as in our previous study (Sajad et al., 2015). We then focused on the spatiotemporal transition from an eye-centered target code (T; preferred in the visual response) to an eye-centered intended gaze position code (G; preferred in the movement response) during the memory delay interval. We treated neural population codes as a continuous spatiotemporal variable by dividing the space spanning T and G into intermediate T-G models and dividing the task into discrete steps through time. We found that FEF delay activity, especially in visuomovement cells, progressively transitions from T through intermediate T-G codes that approach, but do not reach, G. This was followed by a final discrete transition from these intermediate T-G delay codes to a "pure" G code in movement cells without delay activity. These results demonstrate that FEF activity undergoes a series of sensory-memory-motor transformations, including a dynamically evolving spatial memory signal and an imperfect memory-to-motor transformation.

No MeSH data available.


Related in: MedlinePlus