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W2983800302 abstract "Full text Figures and data Side by side Abstract eLife digest Introduction Results Discussion Materials and methods References Decision letter Author response Article and author information Metrics Abstract Hippocampal oscillations are dynamic, with unique oscillatory frequencies present during different behavioral states. To examine the extent to which these oscillations reflect neuron engagement in distinct local circuit processes that are important for memory, we recorded single cell and local field potential activity from the CA1 region of the hippocampus as rats performed a context-guided odor-reward association task. We found that theta (4–12 Hz), beta (15–35 Hz), low gamma (35–55 Hz), and high gamma (65–90 Hz) frequencies exhibited dynamic amplitude profiles as rats sampled odor cues. Interneurons and principal cells exhibited unique engagement in each of the four rhythmic circuits in a manner that related to successful performance of the task. Moreover, principal cells coherent to each rhythm differentially represented task dimensions. These results demonstrate that distinct processing states arise from the engagement of rhythmically identifiable circuits, which have unique roles in organizing task-relevant processing in the hippocampus. https://doi.org/10.7554/eLife.09849.001 eLife digest Electrodes placed on the surface of the scalp can reveal rhythmic patterns of electrical activity within the brain. These rhythms reflect the coordinated firing of large numbers of neurons that are connected together within a network in order to process information. A single network can show rhythms with various different frequencies depending on its local connections and the pattern of input that it receives at any given time. One region that exhibits striking changes in these rhythmic patterns is the hippocampus: a brain area that plays a key role in memory. The hippocampus contains many cell types, including interneurons (which form connections with nearby cells) and principal cells (which connect with cells outside of this region). Though both participate in rhythmic circuits, little is known about the different extents to which these distinct cell types are engaged in rhythmic processing, or how rhythmic processing might support memory. Rangel, Rueckemann, Rivière et al. have now addressed these questions by using electrodes to record from the hippocampus as rats learned to associate specific odors in different environments with a reward. As the rats sniffed the odors, their brains showed four different hippocampal rhythms: from a low frequency called “theta”, through “beta” and “low gamma” up to “high gamma” frequencies. Each of these hippocampal rhythms varied in strength over time, indicating that rhythmic processing is dynamic during the task. Rangel, Rueckemann, Rivière et al. found that neurons fired rhythmically during trials in which the rat chose the correct odor-environment combination. In these correct trials, individual principal cells were more likely to fire in synchrony with only one of the rhythms. In contrast, interneurons were more likely to fire in synchrony to each of the four rhythms at some point during a correct choice. Among the four rhythms, coordinated principal cell and interneuron firing with respect to the beta rhythm was most tightly linked with a correct choice. These findings reveal that investigation of rhythmic dynamics in the hippocampus can provide insight into how the timing of cell activity is coordinated to support memory. https://doi.org/10.7554/eLife.09849.002 Introduction Neural oscillations arise from the temporal coordination of activity in organized networks of neurons (Buzsáki and Draguhn, 2004). The unique connectivity of a network constrains the number of distinct rhythmic profiles that its local circuits can manifest, and the input to the network at a given time dictates the rhythmic circuits that are engaged (Cannon et al., 2014). The dynamics of rhythms can thus reflect fast-paced changes in the coordination of activity within local circuits during information processing. Changes in the oscillatory activity of the hippocampus, a brain structure important for memory function, occur as it processes information it receives from multiple brain regions (Buzsáki and Draguhn, 2004; Cannon et al., 2014; Colgin et al., 2009; Schomburg, et al., 2014; Lee et al., 1994; Igarashi et al., 2014). By studying the interactions of hippocampal neurons with their rhythmic circuits, we gain insight into how single neuron activity is coordinated into the local circuit and systems level processes that support memory. Although great advances have been made in describing both single cell and rhythmic correlates of memory in hippocampal circuits, relatively few studies examine the interaction of these phenomena. The hippocampus exhibits a diversity of rhythms (Cannon et al., 2014; Buzsáki, 2002; Buzsáki and Freeman, 2015; Colgin and Moser, 2010). The theta (4–12 Hz) rhythm is a dominant rhythm in the hippocampus that engages both principal and interneuron cell types, and depends on inputs from the medial entorhinal cortex (MEC) and the medial septum (Lee et al., 1994; Buzsáki, 2002; Kocsis et al., 1999; Montgomery et al., 2009; Kubie et al., 1990). The hippocampus also exhibits oscillations in the beta and gamma frequency ranges that span from 15-150Hz (Buzsáki and Freeman, 2015; Colgin and Moser, 2010; Kay and Freeman, 1998; Martin et al., 2007; Gourevitch et al., 2010; Buzsáki and Schomburg, 2015). Changes in the prominence of these higher frequency oscillations can reflect changes in input from converging afferents. Specifically, slow and fast gamma oscillations in the CA1 region of the hippocampus are thought to arise from the influence of CA3 and MEC inputs, respectively (Colgin et al., 2009; Buzsáki and Schomburg, 2015; Schomburg et al., 2014). In addition, an intermediate beta frequency range in CA1 has been hypothesized to reflect inputs from the lateral entorhinal cortex (LEC) (Igarashi et al., 2014). These higher frequency oscillations often occur concurrently with the theta4-12Hz rhythm, and previous studies suggest that coordination of cell activity within co-occurring rhythms produces nested levels of organization in the hippocampal network (Colgin et al., 2009; Harris et al., 2003; Mizuseki et al., 2009; Buzsáki, 2010). Thus, the diverse rhythmic states observed in the hippocampus can reflect the coordination of distinct information processing. The rhythms in the hippocampus are governed by the neurons that constitute its circuits. The diffuse, local projections of the interneuron population place them in an ideal position to shape the rhythmic organization of the network in response to inputs received from a diverse array of afferents (Freund and Buzsáki, 1996; Sik et al., 1995). Interneurons in the CA1 region differ greatly according to their thresholds of excitability, the decay time of their inhibition, and the subcellular compartments where they preferentially target principal cells (Cannon et al., 2014; Royer et al., 2012; Roux et al., 2014; Roux and Buzsáki, 2015). This diversity enables the interneuron population to flexibly sculpt the oscillatory profile of the hippocampal network while simultaneously shaping principal cell activity (Freund and Buzsáki, 1996; Sik et al., 1995; Sik et al., 1997). As the hippocampus integrates dynamic input during behavior, the interneurons can flexibly engage the appropriate circuits, dictating how the hippocampus processes information. Thus, changes in oscillations can indicate that hippocampal circuits have undergone a shift in processing state. Such shifts in processing state can be observed through distinctive rhythmic dynamics in the hippocampus as it processes information during memory tasks. Transient increases in the amplitude of higher frequency beta and low gamma activity can be observed during the presentation of conditioned stimuli, suggesting that the hippocampus undergoes a change in processing state (Igarashi et al., 2014; Kay and Freeman, 1998; Gourevitch et al., 2010; Berke et al., 2008; Rangel et al., 2015). In addition, cross-frequency coupling in the hippocampus develops while learning context-guided odor-reward associations (Tort et al., 2009; 2010), which occurs concurrently with the development of odor-place conjunctive encoding in hippocampal principal neurons (Komorowski et al., 2009). Since the hippocampus exhibits distinctive rhythmic states during memory tasks, and several of them are tied to the onset of learning, these changes in oscillatory profiles could reflect circuit level processes supporting memory function. However, it remains unknown how the rhythmicity of hippocampal circuits relates to the activity of the constitutive neurons during memory processing. We investigated the extent to which rhythmic engagement of distinct cell types during a memory task could support the ability of the hippocampus to represent associations. In previous studies, it has been shown that single neurons in the CA3 and CA1 regions of the hippocampus develop activity that is selective for odors, odor port locations, and conjunctions of particular odors at specific locations (odor-position selectivity) (Komorowski et al., 2009). We designed a novel task to spatially and temporally isolate the sampling of an olfactory cue from its behavioral outcome during a context-guided odor-reward association task. We then performed in vivo recordings of single cell and local field potential activity in the CA1 region of the rat hippocampus to characterize the relationship between individual neurons and local circuit dynamics. We observed changes in theta (4–12 Hz), beta (15–35 Hz), low gamma (35–55 Hz), and high gamma (65–90 Hz) frequency power during odor sampling epochs when task-relevant information must be integrated for successful performance. Theta4-12Hz, beta15-35Hz, low gamma35-55Hz, and high gamma65-90Hz rhythms differentially recruited principal cells and interneurons during successful performance of the task, suggesting that the different frequency bands represent functionally distinct processing states. Notably, principal cell and interneuron entrainment to beta15-35Hz frequency oscillations were the most correlated with correct performance. We propose that the beta15-35Hz rhythm instigates a processing of information in the hippocampus that is distinct from the processing that occurs in theta4-12Hz, low gamma35-55Hz, and high gamma65-90Hz and that the presence of the beta15-35Hz rhythm signals a recruitment of cell activity that may be critical for memory function. Results We recorded both single cell and local field potential activity in the CA1 region of the dorsal hippocampus in order to determine their relationship during intervals when cues must be associated with a reward outcome. In our task, rats learned that pairs of odors have differential value (rewarded or unrewarded) depending upon the spatial context in which they are presented (Figure 1a (top), see Materials and methods). Rats first entered one of the two contexts and then sampled odors presented at two adjacent odor ports. The initiation of a poke triggered the release of an odor after a 250 ms delay. We analyzed neural activity during trials when the rat maintained a nose poke for 1.5 s while sampling a rewarded odor (correct trials) and during trials when the rat maintained a nose poke for 1.5 s while sampling the non-rewarded odor (incorrect trials). We recorded a total of 1368 cells (1301 principal cells, 67 interneurons) from 6 rats across a total of 45 sessions. Two half-sessions were recorded each day, and each half-session was analyzed separately (see Materials and methods). Figure 1 with 1 supplement see all Download asset Open asset Changes in theta (4–12 Hz), beta (15–35 Hz), low gamma (35–55 Hz), and high gamma (65–90 Hz) amplitude during odor sampling intervals. (a) Schematic of our behavioral paradigm in which pairs of odors (odors A and B, and odors C and D are presented in blocks) are differentially rewarded depending upon the context in which they are presented (top), raw local field potential (LFP) trace (middle) and corresponding amplitude spectrogram (bottom) beginning 0.5 s prior to the initiation of a nose poke until 1.5 s after poke onset for a single correct trial. For a more detailed view of the automated apparatus, see Figure 1—figure supplement 1. (b) Amplitude spectrogram averaged across all correct trials for a single session, shown as the log of the amplitude relative to baseline inter-trial intervals. (c) Same as in b, averaged across all sessions. (d) Same as in c, but shown instead as the log of the amplitude of correct trials relative to incorrect trials. Low gamma35-55Hz amplitude demonstrates a greater increase over time during correct trials than incorrect trials. (e-h) Instantaneous amplitude of theta4-12Hz (e), beta15-35Hz (f), low gamma35-55Hz (g), and high gamma65-90Hz (h) during the 1.5 s odor-sampling interval. https://doi.org/10.7554/eLife.09849.003 Dynamic rhythmic activity during the nose poke interval We observed dynamic rhythmic activity during the nose poke interval. Prominent changes in amplitude were observed in the theta (4–12 Hz), beta (15–35 Hz), low gamma (35–55 Hz), and high gamma (65–90 Hz) frequency ranges (Figure 1a (middle, bottom), b-c). For each frequency band, we determined whether amplitude changed over the course of the nose poke or differed according to behavioral outcome (correct or incorrect). We performed a two-factor repeated measures ANOVA and found a significant main effect of time during the nose poke for all frequencies (Figure 1e–h; time: repeated measures ANOVAtheta: d.f. = 5, F= 10.32, p<0.00001; repeated measures ANOVAbeta: d.f. = 5, F= 23.87, p<0.00001; repeated measures ANOVAlow gamma: d.f. = 5, F= 17.34, p<0.00001; repeated measures ANOVAhigh gamma: d.f. = 5, F= 63.78, p<0.00001), and no main effect for outcome (correct or incorrect) in any frequency (outcome: repeated measures ANOVAtheta: d.f. = 1, F= 1.19, p=0.2797, n.s.; repeated measures ANOVAbeta: d.f. = 1, F ≈ 0, p=0.9746, n.s.; repeated measures ANOVAlow gamma: d.f. = 1, F = 1.32, p=0.2546, n.s.; repeated measures ANOVAhigh gamma: d.f. = 1, F = 0.08, p=0.7747, n.s.). These results indicate that while all four frequencies demonstrated significant changes in amplitude over the course of the nose poke, mean amplitudes were not significantly different across correct and incorrect trial types. However, we observed a significant interaction effect in the low gamma35-55Hz frequency range, due to increased low gamma35-55Hz amplitude during correct trials during the last second of the odor-sampling interval (time x outcome: repeated measures ANOVAtheta: d.f. = 5, F= 0.34, p=0.8886, n.s.; repeated measures ANOVAbeta: d.f. = 5, F = 1.46, p=0.2008, n.s.; repeated measures ANOVAlow gamma: d.f. = 5, F = 4.32, p=0.0008; repeated measures ANOVAhigh gamma: d.f. = 5, F = 0.40, p=0.8513, n.s.). This increase in low gamma35-55Hz amplitude at the end of the nose poke during Correct Trials Only is evident in the ratio of the spectrograms for correct and incorrect trials (Figure 1d). This indicates that there is a change in processing over the course of the nose poke within low gamma35-55Hz rhythmic circuits that differentiates between correct and incorrect trials. Together, these results indicate that the nose poke interval contains a shift in processing state in the hippocampus, which is observable through the onset of changes in rhythmic circuits. Interneuron spike-phase coherence relationships to task performance Populations of interneurons exhibited strong spike-phase coherence to the rhythms present during odor sampling. To test whether single cell entrainment to theta4-12Hz, beta15-35Hz, low gamma35-55Hz, or high gamma65-90Hz frequency ranges was related to successful performance of the associative memory task, we first examined whether interneuron spike-phase coherence to each frequency range during the odor sampling interval was selective to correct or incorrect trial types. This interval was initiated by a nose poke, and continued as the poke was sustained for 1.5 s, when the rat committed to a decision. The interneurons (N = 67, 45 sessions with each half-session analyzed separately, 6 rats, see Materials and methods) were categorized as exhibiting significant spike-phase coherence to a given frequency range during Correct Trials Only, Incorrect Trials Only, or All (both correct and incorrect) Trials (Figure 2a). If single cell engagement in a rhythm in the form of spike-phase coherence is important for successful processing during the task, one might expect a larger number of cells to be coherent during Correct Trials Only. The converse might be true if single cell spike-phase coherence was to interfere with successful performance, resulting in a larger number of cells exhibiting significant spike-phase coherence during Incorrect Trials Only. Lastly, cells that exhibit significant spike-phase coherence to a rhythm on All Trials (correct and incorrect) might instead be engaged in underlying processes that are not task-specific. For each rhythm, the proportions of interneurons in each category were compared to the proportions that would be expected if the cells were equally distributed across all three categories. This comparison thus asks whether the number of cells exhibiting significant spike phase coherence to a rhythm is different across the three performance categories. Figure 2 Download asset Open asset Interneuron and principal cell engagement in rhythmic circuits is related to task performance. (a) Proportions of interneurons demonstrating significant spike-phase coherence to theta4-12Hz (far left), beta15-35Hz (middle left), low gamma35-55Hz (middle right), and high gamma65-90Hz (far right) during Correct Trials Only (green), Incorrect Trials Only (red), or All Trials (gray). The largest proportion of theta4-12Hz coherent interneurons (far left) was coherent during All Trials, regardless of outcome. In contrast, the largest proportion of beta15-35Hz coherent interneurons (middle left) was coherent selectively during Correct Trials Only. (b) Same as in a, for the principal cell population. For each rhythm, the largest proportions of principal cells were coherent during Correct Trials Only. (c) The number of interneurons coherent during Correct Trials Only as a ratio of the total number of interneurons coherent during correct trials (# coherent during Correct Trials Only + # coherent during All Trials). (d) Same as in c, for the principal cell population. (e) The proportions of interneurons and principal cells coherent to each rhythm during Correct Trials Only, subdivided into the proportions exhibiting coherence to a single rhythm or multiple rhythms. While the interneuron population demonstrates flexible engagement into multiple rhythmic circuits during successful performance, principal cells are more often engaged in single rhythmic circuits. https://doi.org/10.7554/eLife.09849.005 Figure 2—source data 1 The number of interneurons within each rhythmic category that were coherent to each possible combination of the four rhythms. The interneurons categorized first by significant spike-phase coherence to a given rhythm and then by coherence during a given performance category (Correct Trials Only, Incorrect Trials Only, All Trials) were further divided by their coherence to each possible combination of the four rhythms examined in this study. For the interneurons that exhibited significant spike-phase coherence to a given rhythm during All Trials, the distribution of their coherence to all possible combinations of rhythms is shown separately for correct and incorrect trials. Interneurons coherent during All Trials often exhibited different profiles of engagement across the four rhythms during correct trials compared to incorrect trials. https://doi.org/10.7554/eLife.09849.006 Download elife-09849-fig2-data1-v1.docx Figure 2—source data 2 The number of principal cells within each rhythmic category that were coherent to each possible combination of the four rhythms. The principal cells categorized first by significant spike-phase coherence to a given rhythm and then by coherence during a given performance category (Correct Trials Only, Incorrect Trials Only, All Trials) were further divided by their coherence to each possible combination of the four rhythms examined in this study. For the principal cells that exhibited significant spike-phase coherence to a given rhythm during All Trials, the distribution of their coherence to all possible combinations of rhythms is shown separately for correct and incorrect trials. https://doi.org/10.7554/eLife.09849.007 Download elife-09849-fig2-data2-v1.docx Of the interneurons that exhibited significant spike-phase coherence to beta15-35Hz (Figure 2a, middle left), the number that exhibited coherence to beta15-35Hz during Correct Trials Only was greater than the numbers coherent during Incorrect Trials Only or All Trials (χ2beta (2, N=66) = 51.54, p<0.00001; post hoc pairwise comparisons with Bonferroni adjusted alpha: χ2correct v incorrect (1, N=53) = 38.21, p<0.00001; χ2correct v all (1, N=62) = 20.90, p<0.00001; χ2incorrect v all (1, N=17) = 4.77, p=0.029, n.s.). Similarly, the number of interneurons coherent to high gamma65-90Hz (Figure 2a, far right) during Correct Trials Only was greater than the numbers coherent during Incorrect Trials Only or All Trials, with a larger number of interneurons coherent during All Trials than during Incorrect Trials Only as well (χ2high gamma (2, N=107) = 59.23, p<0.00001), post hoc pairwise comparisons with Bonferroni adjusted alpha: χ2correct v incorrect (1, N=71) = 59.51, p<0.00001; χ2correct v all (1, N=104) = 9.85, p=0.00017; χ2incorrect v all (1, N=39) = 27.92, p<0.00001). In contrast, the largest number of theta4-12Hz coherent interneurons (Figure 2a, far left) were coherent during All Trials, although a greater number of cells still exhibited coherence during Correct Trials Only compared to Incorrect Trials Only (χ2theta (2, N=126) = 80.19, p<0.00001, post hoc pairwise comparisons with Bonferroni adjusted alpha: χ2correct v incorrect (1, N=42) = 34.38, p<0.00001; χ2correct v all (1, N=124) = 15.61, p=0.00007; χ2incorrect v all (1, N=86) = 78.19, p<0.00001). Lastly, the numbers of interneurons coherent to low gamma35-55Hz (Figure 2a, middle right) during Correct Trials Only and All Trials were greater than the number coherent during Incorrect Trials Only, but were not significantly different from each other (χ2low gamma (2, N=91) = 37.21, p<0.00001), post hoc pairwise comparisons with Bonferroni adjusted alpha: χ2correct v incorrect (1, N=49) = 37.74, p<0.00001; χ2correct v all (1, N=88) = 0.18, p=0.6697, n.s.; χ2incorrect v all (1, N=45) = 33.80, p<0.00001). In summary, while the proportion of interneurons exhibiting coherence during Correct Trials Only or All Trials varies across each of the four rhythms, coherence exclusively during incorrect trials is quite rare. Moreover, the heterogeneity across rhythms indicates that each rhythmic circuit uniquely engages interneurons in processing states that differentially contribute to task performance. To determine whether any of the rhythms are unique in their ability to engage interneuron activity during specific trial types, we also compared the distribution of interneurons across the three performance categories for all rhythms. The interneurons coherent to theta4-12Hz were distributed differently across the three performance categories than the interneurons coherent to beta15-35Hz, low gamma35-55Hz, or high gamma65-90Hz (χ2theta-beta (2, N=192) = 38.56, p<0.00001; χ2theta-low gamma (2, 217)= 9.21, p=0.009; χ2theta-high gamma (2, N=233) = 25.28, d.f. = 2, p<0.00001). Post hoc pairwise comparisons revealed that these differences were driven by the relative proportions of interneurons in the Correct Trials Only and All Trials categories, while similar proportions were observed in the Incorrect Trials Only category across rhythms (theta-beta: χ2correct (1, N=192) = 31.46, p<0.00001, χ2incorrect (1, N=192) = 2.86, p=0.0906, n.s., χ2all (1, N=192) = 38.23, p<0.00001; theta-low gamma: χ2correct (1, N=217) = 7.81, p=0.0052, χ2incorrect (1, N=217) = 0.69, p=0.4075, n.s., χ2all (1, N=217) = 9.13, p=0.0025; theta-high gamma: χ2correct (1, N=233) = 23.54, p<0.00001, χ2incorrect (1, N=233) = 0.41, p=0.5230, n.s., χ2all (1, N=233) = 25.26, p<0.00001; Bonferroni adjusted alpha). Thus, interneuron coherence during All Trials occurs more often in the theta4-12H rhythm, distinguishing it from other rhythms. In addition, interneurons coherent to low gamma35-55Hz were distributed differently across the three performance categories than the interneurons coherent to beta15-35Hz (χ2beta-low gamma (2, N=157) = 11.85, p=0.003), due to a greater degree of selectivity in the beta15-35Hz coherent population for engagement during Correct Trials Only (χ2correct (1, N=157) = 8.99, p=0.003, χ2incorrect (1, N=157) = 0.69, p=0.4075, n.s., χ2all (1, N=157) = 11.77, p=0.0006; Bonferroni adjusted alpha). The distributions across the three performance categories were not significantly different between beta15-35Hz and high gamma65-90Hz coherent interneurons (χ2beta-high gamma (2, N=173) = 4.56, p=0.1021, n.s.) or between low gamma35-55Hz and high gamma65-90Hz coherent interneurons (χ2low gamma-high gamma (2, N=198) = 3.44, d.f. = 2, p=0.1793, n.s.). To better illustrate differences observed across rhythms (Figure 2c), we plotted the ratio of the number of interneurons coherent during Correct Trials Only to the total number that exhibited coherence during correct trials (the combined Correct Trials Only and All Trials categories). These results indicate that interneuron engagement in certain rhythms can be differentially dependent upon task performance. Notably, for each of the four rhythms, the smallest number of interneurons exhibited significant spike-phase coherence during Incorrect Trials Only. This decrease in interneuron spike-phase coherence during incorrect trials can also be observed by comparing the magnitude of coherence for the interneurons during correct and incorrect trials. Adjusting for firing rate differences between trial types (Figure 3a, b, see Materials and methods), we observed significant decreases in the strength of interneuron spike-phase coherence to each rhythm during incorrect trials when compared to correct trials (Median (Mdn)theta-correct = 0.1884, Mdntheta-incorrect = 0.0998, Wilcoxon signed-rank test Z = 4.76, p<0.00001; Mdnbeta-correct = 0.0883, Mdnbeta-incorrect = 0.0223, Wilcoxon signed-rank test Z = 9.25, p<0.00001; Mdnlow gamma-correct = 0.0906 Mdnlow gamma-incorrect = 0.0317, Wilcoxon signed-rank test Z = 7.27, p<0.00001; Mdnhigh gamma-correct = 0.0830 Mdnhigh gamma-incorrect = 0.0221, Wilcoxon signed-rank test Z = 8.28, p<0.00001). Since higher firing rates in phase-modulated cells can increase estimates of spike-phase coherence strength, we determined whether firing rate differences between correct and incorrect trials could explain the differences in selective coherence. If the decrease in coherence during Incorrect Trials Only is due to lower firing rates during incorrect trials, then we would observe significantly lower firing rates during incorrect trials compared to correct trials. To the contrary, we observed that interneurons exhibited significantly higher firing rates during incorrect trials than correct trials (Figure 3c; Mdncorrect = 12.78 Hz, Mdnincorrect = 14.20 Hz, Wilcoxon signed-rank test Z = -3.22, p=0.0013). Thus, the lack of interneuron engagement during Incorrect Trials Only is not due to firing rate differences between correct and incorrect trial types. Instead, unsuccessful processing during the task coincides with a unique inability to engage the interneuron population in rhythmic circuits. Taken together, these results suggest that trial outcome is strongly related to interneuron engagement in each of the four rhythms. Figure 3 with 1 supplement see all Download asset Open asset The strength of interneuron coherence to each rhythm is greater during correct trials than incorrect trials. (a) The proportions of interneurons exhibiting a given magnitude of coherence on the x-axis during correct (green) and incorrect (red) trials with respect to the theta4-12Hz, beta15-35Hz, low gamma35-55Hz, or high gamma65-90Hz rhythm. Greater proportions of interneurons exhibit larger magnitudes of coherence during correct trials compared to incorrect trials. (b) The magnitude of coherence during correct trials plotted against the magnitude of coherence during incorrect trials for all interneurons that were coherent to each rhythm during either correct or incorrect trials. (c) The average firing rate during correct trials plotted against the average firing rate during incorrect trials for all interneurons that were coherent to each rhythm during either correct or incorrect trials. https://doi.org/10.7554/eLife.09849.008 To examine whether performance dependent engagement of the interneurons coincides with a rhythmic phase preference, we compared their average phase of spiking during correct and incorrect trial types (Figure 3—figure supplement 1, see Materials and methods). If engagement in a rhythm during Correct Trials Only represents participation in a rhythmic processing state that" @default.
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W2983800302 title "Author response: Rhythmic coordination of hippocampal neurons during associative memory processing" @default.
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