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• W2073200841 abstract "Here, we report that temporally patterned, coherent spiking activity in the posterior parietal cortex (PPC) coordinates the timing of looking and reaching. Using a spike-field approach, we identify a population of parietal area LIP neurons that fire spikes coherently with 15 Hz beta-frequency LFP activity. The firing rate of coherently active neurons predicts the reaction times (RTs) of coordinated reach-saccade movements but not of saccades when made alone. Area LIP neurons that do not fire coherently do not predict RT of either movement type. Similar beta-band LFP activity is present in the parietal reach region but not nearby visual area V3d. This suggests that coherent spiking activity in PPC can control reaches and saccades together. We propose that the neural mechanism of coordination involves a shared representation that acts to slow or speed movements together. Here, we report that temporally patterned, coherent spiking activity in the posterior parietal cortex (PPC) coordinates the timing of looking and reaching. Using a spike-field approach, we identify a population of parietal area LIP neurons that fire spikes coherently with 15 Hz beta-frequency LFP activity. The firing rate of coherently active neurons predicts the reaction times (RTs) of coordinated reach-saccade movements but not of saccades when made alone. Area LIP neurons that do not fire coherently do not predict RT of either movement type. Similar beta-band LFP activity is present in the parietal reach region but not nearby visual area V3d. This suggests that coherent spiking activity in PPC can control reaches and saccades together. We propose that the neural mechanism of coordination involves a shared representation that acts to slow or speed movements together. Correlating area LIP spiking activity to nearby LFP activity reveals a mechanism of coordination Coherently active spiking in area LIP predicts movement timing Spiking that does not fire coherently does not predict movement timing Coordination involves coherently active neurons that can coordinate movement timing Vision is essential for guiding accurate arm movements. The tight link between vision and reaching means that arm movements are coordinated with eye movements (Song and McPeek, 2009Song J.-H. McPeek R.M. Eye-hand coordination during target selection in a pop-out visual search.J. Neurophysiol. 2009; 102: 2681-2692Crossref PubMed Scopus (38) Google Scholar, Crawford et al., 2004Crawford J.D. Medendorp W.P. Marotta J.J. Spatial transformations for eye-hand coordination.J. Neurophysiol. 2004; 92: 10-19Crossref PubMed Scopus (267) Google Scholar). Coordinated reach and saccade movements are a central aspect of our natural behavior and lead to faster and more accurate movements (Neggers and Bekkering, 2002Neggers S.F.W. Bekkering H. Coordinated control of eye and hand movements in dynamic reaching.Hum. Mov. Sci. 2002; 21: 349-376Crossref PubMed Google Scholar). An intriguing feature of coordinated reach and saccade movements is that the reaction time (RT) of the reach is correlated with the RT of the saccade (Lünenburger et al., 2000Lünenburger L. Kutz D.F. Hoffmann K.P. Influence of arm movements on saccades in humans.Eur. J. Neurosci. 2000; 12: 4107-4116Crossref PubMed Scopus (81) Google Scholar). Although RTs are influenced by nonspecific factors like motivation and arousal (Broadbent, 1971Broadbent D. Decision and Stress. Academic Press, London1971Google Scholar, Barry et al., 2005Barry R.J. Clarke A.R. McCarthy R. Selikowitz M. Rushby J.A. Arousal and Activation in a Continuous Performance Task.J. Psychophysiol. 2005; 19: 91-99Crossref Scopus (51) Google Scholar), nonspecific influences alone cannot explain saccade and reach RTs. Therefore, RT correlations may result from movement coordination (Dean et al., 2011Dean H.L. Martí D. Tsui E. Rinzel J. Pesaran B. Reaction time correlations during eye-hand coordination: behavior and modeling.J. Neurosci. 2011; 31: 2399-2412Crossref PubMed Scopus (53) Google Scholar). Movement coordination depends on the posterior parietal cortex (PPC), which constructs representations of space for different movements (Andersen and Buneo, 2002Andersen R.A. Buneo C.A. Intentional maps in posterior parietal cortex.Annu. Rev. Neurosci. 2002; 25: 189-220Crossref PubMed Scopus (905) Google Scholar, Bisley and Goldberg, 2010Bisley J.W. Goldberg M.E. Attention, intention, and priority in the parietal lobe.Annu. Rev. Neurosci. 2010; 33: 1-21Crossref PubMed Scopus (611) Google Scholar). Damage to the PPC gives rise to a range of deficits of visual-motor coordination, suggesting that the ability to coordinate gaze with arm and hand movements fundamentally depends on parietal mechanisms (Gaveau et al., 2008Gaveau V. Pélisson D. Blangero A. Urquizar C. Prablanc C. Vighetto A. Pisella L. Saccade control and eye-hand coordination in optic ataxia.Neuropsychologia. 2008; 46: 475-486Crossref PubMed Scopus (50) Google Scholar). Neural firing within the lateral intraparietal area (area LIP) and the parietal reach region (PRR), two subdivisions of the PPC, encodes spatial representations that guide saccadic eye movements and arm movements, respectively (Snyder et al., 1997Snyder L.H. Batista A.P. Andersen R.A. Coding of intention in the posterior parietal cortex.Nature. 1997; 386: 167-170Crossref PubMed Scopus (812) Google Scholar). Coordinated saccade and reach movements may result from spatial representations in posterior parietal circuits that are shared between effectors. Local field potentials (LFPs) in area LIP and PRR also encode spatial representations for saccades and reaches (Pesaran et al., 2002Pesaran B. Pezaris J.S. Sahani M. Mitra P.P. Andersen R.A. Temporal structure in neuronal activity during working memory in macaque parietal cortex.Nat. Neurosci. 2002; 5: 805-811Crossref PubMed Scopus (704) Google Scholar, Scherberger et al., 2005Scherberger H. Jarvis M.R. Andersen R.A. Cortical local field potential encodes movement intentions in the posterior parietal cortex.Neuron. 2005; 46: 347-354Abstract Full Text Full Text PDF PubMed Scopus (256) Google Scholar). LFP activity is generated by temporally coherent patterns of activity in neural circuits (Mitzdorf, 1985Mitzdorf U. Current source-density method and application in cat cerebral cortex: investigation of evoked potentials and EEG phenomena.Physiol. Rev. 1985; 65: 37-100PubMed Google Scholar, Pesaran, 2009Pesaran B. Uncovering the mysterious origins of local field potentials.Neuron. 2009; 61: 1-2Abstract Full Text Full Text PDF PubMed Scopus (29) Google Scholar). Since spatial representations are observed in posterior parietal LFP activity, coherent patterns of neural activity in posterior parietal circuits may coordinate movements through the formation of shared movement representations. To identify shared representations supporting coordinated movement, we recorded spiking and LFP activity in area LIP of two monkeys making either coordinated reach and saccade movements or isolated saccades after a short (1–1.5 s) memory delay. For comparison, we also made recordings in PRR and the dorsal part of visual area 3 (V3d). By taking a spike-field approach (Pesaran et al., 2008Pesaran B. Nelson M.J. Andersen R.A. Free choice activates a decision circuit between frontal and parietal cortex.Nature. 2008; 453: 406-409Crossref PubMed Scopus (314) Google Scholar, Pesaran, 2010Pesaran B. Neural correlations, decisions, and actions.Curr. Opin. Neurobiol. 2010; 20: 166-171Crossref PubMed Scopus (26) Google Scholar), we found that RT was predicted by the activity of area LIP neurons that fired coherently in a 15 Hz beta-frequency band. Area LIP neurons that did not participate in the coherent activity did not predict RT. Area LIP activity only predicted RT before coordinated movements and not when saccades were made alone. The same pattern of results was present in beta-band LFP power in area LIP. Beta-band LFP power also predicted RT in PRR but not in V3d. We propose that coherent beta-band activity in area LIP and PRR coordinates the timing of eye and arm movements through a shared representation that can be used to slow or speed both movements together. Figure 1 presents two potential mechanisms for how neural activity could control reaches and saccades. Reach and saccade movements could rely on separate representations for each movement (Figure 1A , left): a saccade representation that guides eye movements and a reach representation that guides arm movements. If so, increases in saccade preparation will shorten saccade RTs without affecting reach RTs (Figure 1A, upper right), and increases in reach preparation will shorten reach RTs without affecting saccade RTs (Figure 1A, lower right). As a result, effector-specific representations cannot coordinate movements because they do not give rise to correlated RTs without other influences. A neural mechanism of coordinated reach and saccade movements could, instead, depend on a shared representation that controls both movements so that they are made together (Figure 1B, left). If there is a shared representation, increases in coordinated movement preparation will shorten both saccade and reach RTs (Figure 1B, right), and neural activity related to coordination will predict both saccade and reach RTs. Covariations in coordinated preparation in this model could give rise to saccade and reach RT correlations. Analyzing the link between RT and neural activity might reveal shared representations that control both movements together. We trained two monkeys to make either coordinated reaches and saccades (Figure 2A ) or saccades alone (Figure 2B) to a visually cued target. Before coordinated movements, saccade RTs (SRTs) were correlated with reach RTs (RRTs; example in Figure 2C; R = 0.69, mean SRT = 190 ms, mean RRT = 280 ms). Across 105 experimental sessions, SRT-RRT correlations were 0.50 ± 0.24 (mean ± std). Mean SRT across the population was also significantly faster when the saccade was made with a reach (243 ± 0.6 ms, mean ± SEM) than when it was made alone (252 ± 0.6 ms; p < 0.001). These results demonstrate that correlations exist between RTs for saccades and reaches such that saccades can be initiated more quickly when made with a reach. We recorded spiking and LFP activity from 105 sites in area LIP (74 in Monkey H; 31 in Monkey J), 135 sites in PRR (53 in Monkey H; 82 in Monkey J) and 36 visually responsive sites in V3d (36 in Monkey J; Figures 3A and S1). We first present example activity from a single session recorded in area LIP during the reach and saccade task. Spiking and LFP activity in area LIP showed robust selectivity for the preferred (Figure 3Bi) compared with the null (Figure 3Ci) direction. Spatial tuning was present in LFP activity with different dynamics at different frequencies. One pattern of power changes was present before movements to the preferred direction (Figure 3Bii), and another pattern was present before movements to the null direction (Figure 3Cii). LFP power was generally greatest around 15–17 Hz in the beta-frequency band and decreased relative to baseline for preferred direction trials (Figure 3D). In contrast, LFP power increased at frequencies above ∼30 Hz in the gamma-frequency band, and the opposite pattern was present for trials in the null direction (Figure 3E). Thus, reach and saccade movements influence the rate of spiking as well as LFP power in both gamma- and beta-frequency bands. To build a link between neural activity and coordination, we then related LFP power and spike firing rate to saccade and reach RTs. We started by considering LFP power. We examined whether LFP activity predicts movement RTs by grouping LFP power during trials with the slowest or fastest RTs. We selected LFP activity from 72 sites in area LIP with at least 60 trials in each direction and for each task (Monkey H: 57 sites; Monkey J: 15 sites). Before reach and saccade movements in the preferred direction, beta-band LFP power (15 Hz) was significantly greater during the 33% of trials with the slowest SRTs than for the 33% of trials with the fastest SRTs (Figure 4A ; p < 0.05, rank-sum test). The effect was even stronger when the data was grouped according to RRTs (Figure 4B; p < 0.001, rank-sum test). In the following, we will present gamma-band activity by analyzing signals at 45 Hz because this activity displayed the strongest spatially selective persistent memory activity across the population of recordings (see below). At 45 Hz, we found that LFP power was not significantly selective for either RT (SRT: p = 0.32, RRT: p = 0.67, rank-sum test). We obtained similar results at other frequencies above 30 Hz (For example, at 65 Hz, SRT: p = 0.11, RRT: p = 0.23, rank-sum test). Since greater beta-band LFP power is associated with slower RTs, decreasing beta-band LFP power may speed movement initiation. The RT selectivity of beta-band LFP power before a reach and saccade is spatially specific and present before movements to the preferred direction. Before movements to the null direction, the activity was not significantly greater during slow trials regardless of whether activity was sorted by saccade RT or reach RT (Figures 4C and 4D; RRT: p = 0.43. SRT: p = 0.27, rank-sum test). To further establish the specificity of beta-band activity for specific interactions between reach and saccade processes, we asked whether RT selectivity is also present when saccades are made alone. There was no significant difference between activity across the population for the fast versus slow RTs when saccades were made alone in the preferred direction (Figure 4E; at 15 Hz, p = 0.18, rank-sum test) or the null direction (Figure 4F; at 15 Hz, p = 0.63, rank-sum test). Lack of RT selectivity before saccades is also not associated with a lack of spatial selectivity. LFP activity was significantly greater for saccades in the preferred direction than in the null direction (Figures 4E and 4F; Supplemental Information). Therefore, beta-band LFP power in area LIP correlates with SRT only when a saccade is made with a coordinated reach in the preferred direction. The level of beta-band power before movements to the preferred direction, however, is greater before saccades made alone than before coordinated movements. Since SRTs are faster before coordinated reach and saccade movements than before saccades made alone, this is consistent with increasing beta-band activity slowing down movement initiation. The overall picture is that beta-band activity exerts a braking mechanism to control the timing of saccades with reaches. Next, we determined whether beta-band selectivity for RT was also present in the spiking activity of area LIP neurons. We recorded isolated action potentials from 59 neurons that showed spatially tuned activity before a coordinated reach and saccade (p < 0.05; permutation test, 48 neurons in Monkey H; 11 neurons in Monkey J). To determine whether spiking activity that is coherent with beta-band LFP activity also predicts RT, we first divided neurons into two groups: coherent cells and not coherent cells. We defined coherent cells as those cells with activity that is significantly correlated with nearby beta-band LFP activity in area LIP. We defined not coherent cells as those cells whose activity is not significantly correlated with nearby beta-band LFP activity. Thirty-four cells (34/59, 58%) were significantly correlated with LFP at 15 Hz in the late-delay epoch, 500–1,000 ms after target onset (coherent cells; p < 0.05). The remaining 25 cells (25/59, 42%) were not significantly correlated with LFP activity (not coherent cells; p > 0.05). The firing rate of coherent cells showed stronger spatially tuning than the activity of not coherent cells (Figure 5). The difference in firing rate before movements in the preferred and null directions was greater for coherent cells than not coherent cells for both tasks (Figures 5A and 5B; coherent cell average firing rate = 14.9 sp/s; not coherent cell average firing rate = 7.3 sp/s). In general, firing rate was higher for coherent versus not coherent cells throughout the trial, including during the baseline epoch. Note that although firing rate is elevated during the delay as opposed to the baseline epoch, LFP directional selectivity and power (see Figure 3Bii) drop off at frequencies > 60 Hz during the delay. This suggests that the band-limited effects that we see at frequencies < 60 Hz are not due to increased spiking activity associated with upcoming movements in the preferred direction. To determine whether the definition of a cell as coherent or not coherent was consistent across the trial, we also analyzed spike-field coherence during the target epoch, 0–500 ms after target onset, and during the baseline epoch, 500 ms immediately before target onset. Almost the same proportion of cells was defined as coherent during the target epoch (coherent: 35/59, 59%; not coherent: 24/59, 41%) as during the late-delay epoch. The definition of a cell as coherent was consistent between target and late- delay epochs for 44 out of 59 cells (44/59, 75%). We observed consistent results based on the baseline epoch. A similar proportion of cells was defined as coherent during the baseline epoch (coherent: 31/59, 53%; not coherent: 28/59, 47%). The definition of a cell as coherent was again consistent between baseline and late-delay epochs, with 42 cells (42/59, 71%) having the same definition for both epochs. Therefore, the definition of a cell as coherent or not coherent did not vary substantially across the trial. Because we observed beta-band selectivity for RT in the LFP during the delay, we chose to focus our analysis of spiking using the definition of coherence during the delay. The difference in spike-field coherence was not simply due to an increase in firing rate. First, coherence is normalized by the firing rate. Second, if coherence were an artifact of higher firing rates, we would expect that the largest differences in firing rate between coherent and not coherent cells would be present during the late-delay epoch, when coherence was estimated. However, the largest differences in firing rate were not present during the late-delay epoch. The largest differences in firing rate were present immediately following the target onset. Third, the same proportions of neurons were coherently active immediately following target onset and during the late-delay epoch despite the difference in firing rates between these epochs. Fourth, although coherent activity can be detected more easily when the firing rate is higher (Zeitler et al., 2006Zeitler M. Fries P. Gielen S. Assessing neuronal coherence with single-unit, multi-unit, and local field potentials.Neural Comput. 2006; 18: 2256-2281Crossref PubMed Scopus (58) Google Scholar), the number of false positives resulting from the statistical testing procedure we use does not vary with firing rate in the absence of coherent activity (see Supplemental Information; see also [Maris et al., 2007Maris E. Schoffelen J.M. Fries P. Nonparametric statistical testing of coherence differences.J. Neurosci. Methods. 2007; 163: 161-175Crossref PubMed Scopus (178) Google Scholar]). Finally, we recalculated SFC after decimating the firing rate of the significantly coherent units by 50% to match the firing rate of those units not coherent with the local fields. We found that, after decimation, 29/34 (85%) remained significantly coherent with LFP. Consequently, although there was a difference between the firing rate of coherent and not coherent cells, the difference in firing rate we report here was not due to a confounding influence of firing rate on coherence. To determine whether coherent and not coherent spiking predicted RT, we performed an ANOVA to determine whether individual neurons showed significant differences in firing rate between the fast and slow RT trials. We found that before a reach and saccade, 21% of coherent cells have significant (p < 0.05) differences in firing rate between fast and slow RRT groups and 9% have significant differences between fast and slow SRT groups. Of these recordings, 70% showed a decrease in firing rate with faster RTs and the remaining 30% showed an increase in firing rate. We also found that only 3% of coherently active cells are significantly selective for SRT during the saccade alone task, which is within the expected proportion of false positives (5%). Finally, and most importantly, when cells are not coherently active, fewer than 5% of cells show significantly selective differences in firing rate for the fast and slow reaction times for all combinations of task and RT type (reach and saccade, RRT: 4%; reach and saccade, SRT 0%; saccade alone, SRT 4%). To quantify the extent to which populations of cells with coherent and not coherent spiking predicted RT, we used a decoding algorithm to predict the RT from each cell population (Figure 5C; see Experimental Procedures). Unlike the LFP analysis, which was done using fixed proportions of fast and slow trials, the population decoding algorithm required that we use a fixed number of trials in each group. We analyzed the fastest or slowest 25 trials (SRT or RRT) in the preferred direction. Ideally, more trials would be available to perform a multiple neuron decoding analysis but this was the largest number of trials available in the database of neuronal recordings for which there was no overlap between the RTs for the fast and slow groups. Quantifying the extent to which RT could be decoded from neural populations allowed us to summarize and compare the strength of the results across tasks and movements. We computed the probability of correct classification of trials in the preferred direction as fast or slow RT trials, based on firing rate in the eight cells with significant RT selectivity based on an ANOVA. Before coordinated movements to the preferred direction, coherent cells significantly predicted whether a trial had a fast or slow RRT (decode probability correct: 0.86; p < 0.001 binomial test) and a fast or slow SRT (decode probability correct: 0.72; p < 0.001). In contrast, not coherent cells did not significantly predict RRT (decode probability correct: 0.58, p = 0.16) or SRT (decode probability correct: 0.48; p = 0.56). Coherently active cells predicted RRT significantly better than cells that were not coherently active (p < 0.005, two-sample binomial test). Coherently active cells also predicted SRT significantly better than not coherent cells (p < 0.05). Importantly, coherent cells encoded the speed of RTs only when movements were coordinated. When saccades were made alone, despite the fact that mean firing rate did not differ for reach and saccade versus saccade alone trials (Figures 5A and 5B), the decoder performed at chance (decode probability correct: 0.56, p = 0.16). The not coherent cells also did not predict SRT (decode probability correct: 0.48, p = 0.56). The performance advantage of the coherent cell population in decoding RT was not due to the fact that there were more cells in the coherent population than the not coherent population. We repeated the analysis for increasing sizes of coherent and not coherent populations up to the number of available cells. The coherent population outperformed the not coherent population for all cell subsets greater than two (Figure S2). Although we report here the results for eight cells, note that, for all numbers of coherent cells greater than three, the decoder performs best and above chance for RRT during coordinated movements. The decoder also performs well and usually above chance for SRT during coordinated movements at all numbers of cells but not for SRT during saccades made alone or for not coherent cells. We also examined whether the better decoding performance of the coherent cells could be due to their higher overall firing rate. When we subtracted the mean firing rate from each cell before decoding the firing rate, we found that the results maintained the same pattern of significance. Coherently active cells predicted coordinated movement RT significantly better than cells that were not coherently active (RRT: p < 0.05. SRT: p < 0.01). Neither group of cells predicted SRT before saccades made alone (Coherent decode probability correct = 0.48. Not coherent decode probability correct = 0.46). Additionally, we decimated the firing rate of the significantly coherent units by 50% to match the firing rate of the not coherent units (see Experimental Procedures). When we performed the RT decoding analysis on the coherent units after decimating the firing rates, we found no significant change in performance when decoding RRT (decode probability correct = 0.70, p < 0.05) or SRT (decode probability correct = 0.62, p < 0.05) before a coordinated movement. Therefore, beta-band LFP activity reflects a population of neurons whose firing rate reliably predicts the RT of coordinated eye-hand movements but not saccades made alone. Neurons which do not participate in the coherent beta-band LFP activity do not predict RT of either movement type. Beta-band activity may reflect the coordinated control of reach and saccade RTs together. We have shown that beta-band spiking and LFP activity varies with both SRT and RRT across a population of sites, but this is not necessarily sufficient to demonstrate that the control of saccade and reach RTs occurs together. Activity at some sites may be involved in controlling one effector, while activity at different sites may control the other effector. To link beta-band activity to the coordinated control of movement timing, we examined whether selectivity for both saccade and reach RTs is present in activity at the same sites. We determined RT selectivity by grouping LFP power during trials with the slowest 33% of RTs and LFP power during trials with the fastest 33% of SRTs and computing a z-score using random permutations (see Experimental Procedures) and found that RT selectivity does exist for both movements at the same sites (Figure 6A ). At 15 Hz, LFP activity was significantly selective for both SRT and RRT at 10/72 sites (14%; p < 0.01, Binomial test). In comparison, LFP activity at 45 Hz was selective for both RTs at only 2/72 sites (3%; p = 0.88. Binomial test. Figure 6B). The strength of the effect at single sites is limited by the number of trials available for analysis. When we restrict our analysis to recording sites with at least 135 trials per direction and task, 30% of recording sites were significantly selective for both SRT and RRT in the beta-band. We found a high degree of correlation between SRT selectivity and RRT selectivity in both the beta-band (R = 0.65 at 15 Hz) and the gamma-band (R = 0.41 at 45 Hz). Thus, LFP activity at each recording site predicts the RT of both the saccade and the reach in a similar manner, with the strongest effects present in the beta band. These data suggest that if changes in beta-band power change the RT for both movements, beta-band activity could coordinate movement timing. If beta-band power reflects the joint control of movement RTs, variations in the level of beta-band power could give rise to correlations in the behavioral RTs, and lack of power variation could lead to a reduction or even elimination in the RT correlations. To test this prediction, we calculated the relationship between saccade and reach RTs across groups of trials when beta-band power is relatively constant (see Experimental Procedures). This approach is similar to a partial correlation analysis, which is defined by the relationship between two variables while controlling for a third variable, but it does not require us to correlate LFP power and RT trial-by-trial. We found that average RT correlations calculated during groups of trials when beta-band power was relatively constant (R = 0.32) were significantly lower than correlations calculated in the same way when beta-band power varied (R = 0.37). The difference in RT correlation was significant (p < 0.05, rank-sum test). An average of 18% of the correlation between saccade and reach RTs could be explained by variations in beta-band power in area LIP. At some sites, beta-band power could explain over 60% of the RT correlations. Since SRT and RRT are less correlated when beta-band power does not vary, variation in the level of beta-band activity can contribute to RT correlations. Beta-band power is selective for RT in other areas of posterior parietal cortex and is not selective for RT in nearby occipital cortex. We analyzed a complementary data set of 122 LFP recordings in PRR and 36 visually responsive recordings in V3d, located along the lunate sulcus, with at least 60 trials in each condition, and we plotted RT selectivity from all three areas as the trial progressed (Figure 7). Beta-band LFP activity in area LIP was increasingly selective for RT as the memory period progressed (Figures 7A and 7B). The RT effect was also robust in PRR where 28/122 sessions (23%) were significantly selective when trials were sorted by RRT, and 18/122 sessions (15%) were significantly selective when trials were sorted as a function of SRT (Figures 7C and 7D). In comparison, at 45 Hz, only 12/122 sessions (10%, data not shown) were significantly selective for RRT, and 7/122 sessions (6%, data not shown) were selective for SRT, which is not statistically significant (Binomial test). Beta-band power in the visual areas we studied, in contrast, is not selective throughout the trial (Figures 7E and 7F). PRR LFP recordings also showed RT selectivity for both movements at the same site (data not shown). As in area LIP, LFP activity at 15 Hz in PRR was significantly selective for both SRT and RRT at 22/122 sites (18%; p < 0.01), while at 45 Hz, LFP was selective for both RTs at only 4/122 sites (3%) which, as in area LIP, is not statistically signif" @default.
• W2073200841 created "2016-06-24" @default.
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• W2073200841 date "2012-02-01" @default.
• W2073200841 modified "2023-10-12" @default.
• W2073200841 title "Only Coherent Spiking in Posterior Parietal Cortex Coordinates Looking and Reaching" @default.
• W2073200841 cites W1535209670 @default.
• W2073200841 cites W1581822100 @default.
• W2073200841 cites W1926515346 @default.
• W2073200841 cites W1964491812 @default.
• W2073200841 cites W1970445701 @default.
• W2073200841 cites W1971815199 @default.
• W2073200841 cites W1971828433 @default.
• W2073200841 cites W1973427237 @default.
• W2073200841 cites W1978667192 @default.
• W2073200841 cites W1982643689 @default.
• W2073200841 cites W1989700973 @default.
• W2073200841 cites W1990973912 @default.
• W2073200841 cites W1995494320 @default.
• W2073200841 cites W1997173667 @default.
• W2073200841 cites W1999567494 @default.
• W2073200841 cites W2002699700 @default.
• W2073200841 cites W2006304614 @default.
• W2073200841 cites W2009309616 @default.
• W2073200841 cites W2010465695 @default.
• W2073200841 cites W2011780250 @default.
• W2073200841 cites W2016527110 @default.
• W2073200841 cites W2018175367 @default.
• W2073200841 cites W2019310225 @default.
• W2073200841 cites W2022896516 @default.
• W2073200841 cites W2031721835 @default.
• W2073200841 cites W2033707011 @default.
• W2073200841 cites W2039382055 @default.
• W2073200841 cites W2041262582 @default.
• W2073200841 cites W2055253787 @default.
• W2073200841 cites W2059005805 @default.
• W2073200841 cites W2068904707 @default.
• W2073200841 cites W2080119716 @default.
• W2073200841 cites W2087822149 @default.
• W2073200841 cites W2088092325 @default.
• W2073200841 cites W2099650267 @default.
• W2073200841 cites W2108942699 @default.
• W2073200841 cites W2111736228 @default.
• W2073200841 cites W2113110023 @default.
• W2073200841 cites W2114876288 @default.
• W2073200841 cites W2125132946 @default.
• W2073200841 cites W2129846302 @default.
• W2073200841 cites W2133280087 @default.
• W2073200841 cites W2133947591 @default.
• W2073200841 cites W2148849843 @default.
• W2073200841 cites W2150576740 @default.
• W2073200841 cites W2151229057 @default.
• W2073200841 cites W2151335222 @default.
• W2073200841 cites W2152179757 @default.
• W2073200841 cites W2152525759 @default.
• W2073200841 cites W2155899491 @default.
• W2073200841 cites W2156370424 @default.
• W2073200841 cites W2161472384 @default.
• W2073200841 cites W2169134378 @default.
• W2073200841 cites W2171848154 @default.
• W2073200841 cites W3104833475 @default.
• W2073200841 cites W4234112092 @default.
• W2073200841 doi "https://doi.org/10.1016/j.neuron.2011.12.035" @default.
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