Distinct Roles of Parvalbumin- and Somatostatin- Expressing Interneurons in Working Memory

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1 Article Distinct Roles of Parvalbumin- and Somatostatin- Expressing Interneurons in Working Memory Highlights d Roles of parvalbumin and somatostatin neurons in working memory were investigated d d Somatostatin neurons contributed to target-dependent delay-period activity Reward delivery strongly suppressed parvalbumin neuronal activity Authors Dohoung Kim, Huijeong Jeong, Juhyeong Lee, Jeong-Wook Ghim, Eun Sil Her, Seung-Hee Lee, Min Whan Jung Correspondence mwjung@kaist.ac.kr d These results suggest distinct roles of different interneurons in working memory In Brief Kim and colleagues found different discharge characteristics and stimulation effects of parvalbumin- and somatostatin-expressing interneurons in medial prefrontal cortex of mice performing a working memory task. Their results suggest distinct roles of parvalbumin- and somatostatinexpressing interneurons in working memory. Kim et al., 2016, Neuron 92, November 23, 2016 ª 2016 Elsevier Inc.

2 Neuron Article Distinct Roles of Parvalbuminand Somatostatin-Expressing Interneurons in Working Memory Dohoung Kim, 1 Huijeong Jeong, 2 Juhyeong Lee, 2 Jeong-Wook Ghim, 3 Eun Sil Her, 3 Seung-Hee Lee, 2 and Min Whan Jung 1,2,3,4, * 1 Graduate School of Medical Science and Engineering 2 Department of Biological Sciences Korea Advanced Institute of Science and Technology, Daejeon 34141, Korea 3 Center for Synaptic Brain Dysfunctions, Institute for Basic Science, Daejeon 34141, Korea 4 Lead Contact *Correspondence: mwjung@kaist.ac.kr SUMMARY Inhibitory interneurons are thought to play crucial roles in diverse brain functions. However, roles of different inhibitory interneuron subtypes in working memory remain unclear. We found distinct activity patterns and stimulation effects of two major interneuron subtypes, parvalbumin (PV)- and somatostatin (SOM)-expressing interneurons, in the medial prefrontal cortex of mice performing a spatial working memory task. PV interneurons showed weak target-dependent delay-period activity and were strongly inhibited by reward. By contrast, SOM interneurons showed strong target-dependent delay-period activity, and only a subtype of them was inhibited by reward. Furthermore, optogenetic stimulation of PV and SOM interneurons preferentially suppressed discharges of putative pyramidal cells and interneurons, respectively. These results indicate different contributions of PV and SOM interneurons to prefrontal cortical circuit dynamics underlying working memory. INTRODUCTION Working memory refers to the system of temporarily maintaining and manipulating information required to perform complex cognitive tasks (Baddeley, 1992). The neural basis of working memory is unclear, but persistent (Funahashi et al., 1989; Fuster and Alexander, 1971; Kubota and Niki, 1971) and sequential (Baeg et al., 2003; Batuev et al., 1979; Fujisawa et al., 2008; Harvey et al., 2012) neuronal discharges have been observed in the prefrontal and other areas of the cortex as potential neural substrates for working memory. For a neural network to carry information based on such neuronal activity, balanced excitation and inhibition must be critical. Indeed, experimental (Constantinidis et al., 2002; Rao et al., 2000) and theoretical (Brunel and Wang, 2001; Fellous and Sejnowski, 2003; Lim and Goldman, 2013; Machens et al., 2005; Wang et al., 2004) studies have shown that properly tuned excitation and inhibition are necessary to maintain persistent neuronal discharges during a working memory task. These results suggest that inhibitory interneurons may play important roles in the maintenance of working memory. There exist a number of different local interneuron subtypes in the cerebral cortex and parvalbumin (PV)- and somatostatin (SOM)-expressing interneurons constitute two major subtypes (Kepecs and Fishell, 2014; Rudy et al., 2011). PV and SOM interneurons have substantially different morphological features, anatomical connectivity, and physiological properties (Hangya et al., 2014; Hu et al., 2014; Kepecs and Fishell, 2014; Lovett- Barron and Losonczy, 2014; Petersen and Crochet, 2013; Roux and Buzsáki, 2015; Rudy et al., 2011). PV interneurons mainly target the perisomatic domain of pyramidal cells and mediate fast and powerful inhibition. By contrast, SOM interneurons mainly target distal dendrites of pyramidal cells and exert focal inhibition of synaptic inputs to pyramidal cells (Hangya et al., 2014; Hu et al., 2014; Rudy et al., 2011). PV and SOM interneurons also differ in their influences on other interneurons. PV interneurons strongly inhibit each other with little inhibitory influences on other interneurons, whereas SOM interneurons strongly inhibit other interneurons without inhibiting themselves in the visual cortex (Pfeffer et al., 2013). Recent studies in behaving animals have found that PV and SOM interneurons show distinct activity patterns and modulate responses of nearby neurons differentially (Kvitsiani et al., 2013; Lee et al., 2012, 2014; Pinto and Dan, 2015; Royer et al., 2012; Sturgill and Isaacson, 2015; Wilson et al., 2012), providing important insights on the roles of PV and SOM interneurons in diverse cortical functions (Hangya et al., 2014; Hu et al., 2014; Kepecs and Fishell, 2014; Lovett-Barron and Losonczy, 2014; Petersen and Crochet, 2013; Roux and Buzsáki, 2015). However, roles of PV and SOM interneurons in high-order cognitive processes, including working memory, remain largely unknown. To elucidate roles of PV and SOM interneurons in working memory, we examined discharge characteristics and stimulation effects of PV and SOM interneurons in the medial prefrontal cortex (mpfc) of mice performing a spatial working memory task. 902 Neuron 92, , November 23, 2016 ª 2016 Elsevier Inc.

3 Figure 1. Working Memory Task (A) Figure 8-shaped maze. The maze had five doors (gray solid lines) and six photobeam sensors (green dotted lines). The water reward (8 ml) was provided at two reward sites (blue circles) in correct trials. (B) Delayed non-match-to-place task. Shown is an example sequence of the task. Sample phase, the animals were forced to visit a randomly determined target site (no reward provided); delay period, the animals waited for 3 s on the central track; and choice phase, the animals were rewarded by choosing the target different from the one they visited during the sample phase. (C) Learning curve. The animals performance (% correct choice) during the training, before surgery, is shown (mean ± SEM across animals; n = 17). The linear regression indicated significantly improving performance (slope = 1.9; t test and p = ) over sessions. RESULTS Spatial Working Memory Performance of Mice PV-Cre and SOM-Cre mice were trained in a delayed nonmatch-to-place task in a figure 8-shaped maze (Figure 1). They were forced to visit a randomly determined target site (left or right) during the sample phase. Then, following a 3 s delay on the central stem of the maze (delay period), they were allowed to choose freely between the two target sites and rewarded by visiting the one unvisited during the sample phase. The mice performed a total of trials in one daily session, and they were overtrained in the task before unit recording (training duration, 5 18 days before and 5 13 days after surgery). The PV-Cre (n = 11) and SOM-Cre (n = 6) mice chose correct targets in 95.2% ± 0.4% and 96.3% ± 0.4% of trials (mean ± SEM across sessions), respectively, during experimental sessions. Optical Tagging of Specific Cell Types and Unit Classification We recorded and stimulated PV- and SOM-expressing interneurons in the prelimbic cortex. For optical tagging of PV- and SOMexpressing interneurons, we injected a double-floxed (DIO) Credependent adeno-associated virus (AAV) vector carrying the gene for channelrhodopsin-2 (ChR2) in-frame fused to enhanced yellow fluorescent protein (AAV-DIO-hChR2(H134R)-eYFP) into the prelimbic cortex of PV-Cre and SOM-Cre knockin mice. Histological examination revealed that AAV-DIO-hChR2(H134R)- eyfp was colocalized with PV and SOM throughout the prelimbic cortex of PV-Cre and SOM-Cre mice, respectively (Figure 2A). For quantification, we counted the numbers of ChR2-, PV-, and SOM-expressing neurons in the prelimbic cortex (one brain section each from three PV-Cre and three SOM-Cre mice in which immunohistochemistry was successfully performed). We found 109 PV-positive and 107 ChR2-postive neurons in PV-Cre mice, and the majority (101; 87.8%) of them expressed both PV and ChR2. In SOM-Cre mice, we found 148 SOM-positive and 163 ChR2-positive neurons, and the majority of them (146; 88.5%) expressed both SOM and ChR2 (Figure 2B). Thus, only a small fraction of ChR2-postive neurons was PV- (six out of 107) or SOM-negative (15 out of 163). A microdrive array containing one optical fiber and 4 8 tetrodes was implanted in the left or right mpfc (counterbalanced across animals). The optical fiber was held in the same position throughout the experiment, but tetrodes were advanced after each recording session. Histological examination revealed that the optical fibers were positioned around the border between the dorsal anterior cingulate cortex and prelimbic cortex, and all units (1,192 from 17 mice) were recorded from the prelimbic cortex (Figure 2C). The recorded neurons were classified into two groups based on discharge rates and spike waveforms. One group (type I; fast-spiking cells; n = 157) showed high firing rates and narrow spike waveforms, and the other (type II; putative pyramidal cells; n = 952) had low firing rates and wide spike waveforms (the rest were unclassified, n = 83; Figure 2D). We found 26 and 29 neurons that were reliably activated by light stimulation ( mw, 5 ms duration) with short latencies (PV, 2.15 ± 0.11 ms and SOM, 2.58 ± 0.13 ms) and low spike jitters (the SD of spike latency; PV, 0.96 ± 0.07 ms and SOM, 0.93 ± 0.05 ms) in PV-Cre and SOM- Cre mice, respectively (Figures 2E; see Supplemental Experimental Procedures and S1 for details of identifying light-activated neurons; light stimuli were delivered at the end of each recording session). These light-activated neurons were considered as PV and SOM interneurons that are tagged with ChR2, respectively (Figure S2 shows light-activated responses of all optogenetically confirmed PV and SOM neurons). The majority (21 out of 26, 80.8%) of optogenetically confirmed PV neurons were type I neurons, which is consistent with previous results (Hu et al., 2014; Rudy et al., 2011). Of a total of 29 optogenetically confirmed SOM neurons, nine were type I (31.0%; narrow-spike SOM or ns-som neurons), 14 were type II (48.3%; wide-spike SOM or ws-som neurons), and the rest (n = 6) were unclassified (Figure 2D). This result is consistent with the finding that there are Neuron 92, , November 23,

4 Figure 2. Optical Tagging and Unit Classification (A) Histological verification of ChR2 expression. Shown are coronal sections of a PV-Cre (left) and a SOM-Cre (right) mouse brain expressing ChR2-eYFP (green) stained with anti-pv (left) or anti-som (right) antibody (red) along with DAPI (blue). A high-magnified image of a PV- (left) or SOM-expressing (right) neuron is shown in the inset box. Secondary motor cortex, M2; anterior cingulate cortex, Cg; prelimbic cortex, PL. (B) The diagrams show the proportions of neurons expressing ChR2 and/or PV/SOM in the prelimbic cortex. (C) The extent of ChR2 expression in each animal is indicated in green, with dark color indicating overlapping areas across animals. The vertical bars denote locations of optical probes, and each red circle indicates the location of each tetrode in the last recording session. The diagrams are coronal section views of mouse brains (1.98, 1.94, and 1.78 mm anterior to bregma). Modified with permission from Elsevier (Franklin and Paxinos, 2007). (D) The recorded neurons were classified into type I (filled circles) and II (filled triangles) based on the mean firing rate, half-valley width, and peak-valley ratio. The open squares denote unclassified neurons. The purple symbols indicate light-activated neurons in PV-Cre mice, and the yellow and green symbols denote lightactivated neurons in SOM-Cre mice. (E) Example neuronal responses to light stimulation. Top, raster plot. Each row is one trial and each dot is one spike. The blue bar denotes a period of light stimulation. Bottom, peri-stimulus time histogram (PSTH). The time 0 denotes the time of light stimulus (5-ms duration) onset. The averaged spike waveforms of spontaneous (black) and optically driven (blue) spikes recorded through four tetrode channels are shown (inset). The scale represents 250 ms and 50 mv. See also Figures S1 and S2. multiple subtypes of SOM neurons in the cortex (Kvitsiani et al., 2013; Ma et al., 2006; McGarry et al., 2010). Only type I PV (n = 21), ns-som (n = 9), and ws-som (n = 14) neurons were analyzed, but similar results were obtained when all PV and SOM neurons were included in the analysis (data not shown). More than a half of untagged type II neurons were inhibited by light stimulation with short latencies (<10 ms) in both PV- Cre (n = 255 out of 449; 56.8%) and SOM-Cre (n = 297 out of 486; 61.1%) mice. The majority of untagged type I neurons were also inhibited by light stimulation in PV-Cre (n = 36 out of 55; 65.5%) and SOM-Cre (n = 63 out of 72; 87.5%) mice (examples shown in Figure 2E). These results are consistent with the finding that both PV and pyramidal neurons are inhibited by other PV and SOM neurons (Pfeffer et al., 2013). Target Selectivity during Delay Period Mean discharge rates of PV, ns-som, and ws-som neurons during the working memory task were 26.0 ± 2.3, 20.8 ± 4.6, and 5.4 ± 1.0 Hz, respectively. PV and ns-som neurons tended to fire at high rates largely throughout a trial, including the delay period, except following reward delivery. By contrast, ws-som neurons tended to fire briefly at different phases of a trial, including the delay period (Figure 3A). Overall discharge characteristics of untagged type I and II neurons were similar to those of PV and ws-som neurons, respectively (Figure S3). In order to investigate roles of PV and SOM neurons in working memory, we focused our analyses on neural activity and stimulation effects of PV and SOM neurons during the delay period. We first examined target-dependent firing of PV and SOM 904 Neuron 92, , November 23, 2016

5 Figure 3. Delay-Period Activity of PV and SOM Neurons (A) Raster plots and spike density functions (Gaussian kernel with s = 100 ms was applied to each spike) during four different behavioral stages (trial onset, sample target, delay, and reward; the animal s approximate position at the onset of each stage is indicated by gray shading on top) are shown for example PV, ns-som, and ws-som neurons. The trials were divided into correct left choice (deep blue), incorrect left choice (light blue), correct right choice (deep red), and incorrect right choice (light red) trials and plotted separately. The time 0 denotes the onset of each behavioral stage (the time of breaking a photobeam sensor). (B D) Target-dependent firing of PV and SOM interneurons during delay period. (B) Distributions of the index of target preference for the entire delay-period activity (3 s) of PV and SOM (ns-som and ws-som combined) neurons. (C) Distributions of the magnitude of target preference (absolute value of the index of target preference) for the entire delay-period activity of PV and SOM neurons. *significant difference between PV and SOM neurons (t test and p < 0.05). (D) Time courses of the magnitude of target preference are shown for PV and SOM neurons with a smaller analysis time window (0.5 s window advanced in 0.1 s time steps). The red squares indicate significant differences between PV and SOM neurons (t test and p < 0.05). (E G) Target-dependent firing was separately examined for ns-som and ws-som interneurons. The same format as in (B) (D). The bar graphs represent mean and SD (B and E), or mean and SEM (C and F), and shading represents SEM (A, D, and G). See also Figures S3 S6. neurons during the delay period. As shown by representative examples in Figure 3A, delay-period activity in ipsilateral- and contralateral-choice trials tended to be similar for PV neurons (low target selectivity), but different for SOM neurons (high target selectivity). Delay-period activity of all tagged PV and SOM neurons is shown in Figure S4. For the analysis of group data, we computed the index of target preference (an index for preferential firing in ipsilateral versus contralateral target-choice trials; positive and negative values indicate preferential firing during the ipsilateral and contralateral target-choice trials, respectively; Supplemental Experimental Procedures) for the delay-period activity using only correct trials. Because it is not entirely clear how to classify interneurons (DeFelipe et al., 2013; Kepecs and Fishell, 2014; Markram et al., 2004; Roux and Buzsáki, 2015), we analyzed SOM neural data with as well as without combining ns-som and ws-som neural data together. Individual neurons showed mixed target preferences during the delay period (i.e., both ipsilateral and contralateral choice-preferring responses were found) so that the mean target preference was not significantly different from zero for both cell types (t test, PV, p = and SOM, p = 0.718; Figure 3B). However, when we compared the magnitude of target preference (absolute value of the index of target preference; i.e., an index for target selectivity regardless of its direction), a significant difference was found between PV and SOM neurons. The magnitude of target preference for the entire delay-period activity was significantly larger for SOM (ns-som and ws-som combined) than PV neurons (0.201 ± and ± 0.014, respectively; t test, p = 0.003; Figure 3C). An analysis using a smaller time window (0.5 s) also showed that the magnitude of target preference was larger for SOM than PV neurons largely throughout the delay period (Figures 3D; see S3E for the result of a shuffling analysis). Because ns-som and ws-som neurons showed different physiological characteristics, we also analyzed target-dependent firing of ns-som and ws-som neurons separately. The index of target preference was not significantly different from zero for both cell types (t test, ns-som, p = and ws-som, p = 0.602; Figure 3E), and no significant difference was found in the magnitude of target preference between ns-som and ws-som neurons (0.153 ± and ± 0.043, respectively, for the entire delay period; t test, p = 0.289; Figures 3F and 3G). Untagged type I neurons showed weak target-dependent firing as PV neurons, but untagged type II neurons showed strong target-dependent firing during the delay period (Figure S3). As previously reported in the rat mpfc (Baeg et al., 2003; Fujisawa et al., 2008), few type II neurons showed persistent activity throughout the delay period and, instead, they conveyed target information based on sequential discharges during the delay period (Figure S5). Target-dependent firing of mpfc neurons could not be accounted for by target-dependent variation in the Neuron 92, , November 23,

6 Figure 4. Results of Decoding Analysis The identity of sample target was decoded based on delay-period activity. (A) The relationship between ensemble size and correct decoding of sample target based on neuronal ensemble activity during the entire delay period. The neural decoding was performed for different trial types (different combinations of the left/right sample and choice targets) and then averaged according to correct and error trials. Those neurons with mean delay-period activity <0.5 Hz were excluded from the analysis. The black color shows decoding results for correct trials, and the gray color shows decoding results for error trials. (B) The identity of sample target was decoded based on ensemble activity of untagged type II neurons (n = 334) in a 0.5 s time window that was advanced in 0.1s steps. The vertical lines denote the onset and offset of the delay period. (C) Neural decoding of sample-target based on type II neuronal ensemble activity during different phases of the delay and post-delay periods. *significantly different from chance (50%) level (t test and p < 0.05). The bar graphs represent mean and SEM (C), and shading represents SEM (A and B). animal s movement trajectory or head direction during the delay period (Figure S6). These results show that SOM neurons conveyed more specific information than PV neurons about the identity of sample target and upcoming target selection during the delay period (i.e., the content of working memory). Delay-Period Activity during Error Trials We then examined how well neural activity during the delay period discriminated the identity of sample target in correct and error trials using the Bayesian decoding procedure (Supplemental Experimental Procedures). The training data set consisted only of correct trials so that each trial (either correct or error trial) was compared against neural activity during correct trials. We did not divide SOM neurons into ns-som and ws-som neurons in this analysis because of a small number of error trials (2.0 ± 0.7 per session). A neuron-dropping analysis showed that all neuron types transmitted significant information about sample target during the delay period in correct trials (Figure 4A). PV neurons, albeit of low target selectivity, conveyed substantial amounts of information about sample target because of high firing rates. Performance of neural decoding was significantly worse for error- compared to correct-trial delay-period activity for SOM, untagged type I, and untagged type II neurons (McNemar s test, p = , , , respectively). However, it was similar between correct- and error-trial delay-period activity for PV neurons (p = 1.000; Figure 4A). In addition, decoding performance was significantly different between PV and SOM neurons in error (74.5% for PV; 50.5% for SOM; c 2 -test, p = ), but not in correct trials (74.5% correct decoding for PV; 70.5% for SOM; p = 0.370; ensemble size, n = 5 neurons). These results indicate that PV neurons tended to fire similarly, but SOM neurons tended to fire differently during the delay period between correct and error trials. To obtain insights on the nature of information transmitted during the delay period, we performed a sliding-window analysis (0.5 s window advanced in 0.1 s steps) of delay-period activity in error trials. We also included the 1 s time period after delay offset in this analysis because it took 1 s for the central sliding door to open after delay offset. We subjected only untagged type II neurons to the sliding-window analysis because small numbers of optically tagged neurons, along with the small number of error trials, did not warrant reliable decoding results. The probability for correctly decoding sample target based on error-trial neuronal ensemble activity decreased gradually over time (Figure 4B), so that it was not significantly different from chance level (50%) for the last 1 s of the delay period (t test, p = 0.218). It was further reduced to 37.5% ± 3.4% during the 1 s time period following delay offset, which is significantly below chance level (p = ; Figure 4C). Note that decoding of sample target is inversely related to decoding of upcoming goal choice in error trials because error-trial neural activity (testing data; LL and RR trials) was decoded against correct-trial neural activity (training data; LR and RL trials). These results suggest that delay-period activity is more related to sample target (retrospective memory) during the early phase of the delay period, but more to the upcoming goal choice (prospective memory) in the late phase. 906 Neuron 92, , November 23, 2016

7 Figure 5. Responses of PV and SOM Neurons to Reward (A and B) Mean normalized firing rates in rewarded (correct, cyan) and unrewarded (error, gray) trials are shown for PV and SOM (ns-som and ws-som combined) neurons (A) and separately for ns-som and ws-som neurons (B). (C) Distributions of the index of reward-dependent firing for PV, ns-som, and ws-som neurons. (D) Experimental setting for the probabilistic Pavlovian conditioning task (top) and temporal structure of the task (bottom). The odor cues were paired with probabilistic reward delivery. (E G) Reward-related responses of PV and SOM neurons during the Pavlovian conditioning task. The same format as in (A) (C). The reward onset was aligned to the first licking response after delay offset. (H) Mean GLM coefficients for trial outcomes (colors, reward; gray, no reward). The bar graphs represent mean and SEM (C and G), and shading represents SEM (A, B, E, F, and H). *significantly different from zero (t test and p < 0.05). See also Figure S7. Response to Reward As shown by examples in Figure 3A, PV and ns-som neurons abruptly reduced their firing rates following reward delivery in correct trials. By contrast, ws-som neurons showed variable responses to reward delivery (activity increase, activity decrease, or no change). As a population, PV neurons decreased their discharge rates following reward delivery for a prolonged time (10 s). Mean discharge rates during the first 2 s time period following reward period onset was significantly different between rewarded (correct) and unrewarded (error) trials for PV neurons (15.9 ± 1.9 and 27.8 ± 3.4 Hz, respectively; paired t test, p = ). SOM (ns-som and ws-som combined) neurons showed a trend for decreased firing following reward delivery (mean neural activity during the first 2 s time period following reward period onset, 5.7 ± 1.3 and 9.9 ± 2.4 Hz, respectively; t test, p = 0.060; Figure 5A). When analyzed separately, population responses of ns-som neurons decreased for 3 s following reward delivery in correct trials, but ws-som neurons showed variable responses to reward so that the population average showed little change to reward delivery (Figures 5B and 5C). Mean neural activity during the first 2 s following reward period onset was significantly different between rewarded and unrewarded trials for ns-som neurons (9.3 ± 1.7 and 19.7 ± 3.6 Hz, respectively; paired t test, p = 0.008), but not for ws-som neurons (3.5 ± 1.6 and 3.2 ± 1.4 Hz, respectively; p = 0.948). Reward responses of untagged type I and II neurons were similar to those of PV and ws-som neurons, respectively (Figures S7A S7C). In some sessions (n = 75), we omitted reward delivery even when the mice made correct responses in a subset of trials. PV (n = 7), ns-som (n = 8), and untagged type I neurons (n = 44) reduced their firing rates in rewarded, but not unrewarded, correct trials (see Figure S7D), indicating that they distinguished between reward delivery and reward omission rather than between correct and incorrect responses. Responses of PV and SOM neurons during the reward period are likely to represent neural activity that is related to trial Neuron 92, , November 23,

8 outcome as well as behavioral variables. To minimize behavioral variations between rewarded and unrewarded trials, and to examine generality of reward-related responses under different behavioral settings, we examined reward-related responses of mpfc neurons under a head-fixed condition in a separate group of mice. The mice were subjected to a probabilistic Pavlovian conditioning task in which odor cues were paired with probabilistic water delivery (Supplemental Experimental Procedures; Figure 5D). We recorded 35 PV, five ns-som, 22 ws-som, 117 untagged type I, and 821 untagged type II neurons from the prelimbic cortex of four PV-Cre and four SOM-Cre mice (confirmed by histological examination as in Figure 2; data not shown). PV neurons tended to reduce their discharge rates in rewarded trials (neural response aligned to the first licking response since delay offset), with its response duration shorter (2 s) than that under the freely moving condition (10 s). However, SOM neurons showed variable responses to reward delivery, so that their population average showed little deviation from the baseline response (Figures 5E and 5F). Mean neural activity during the first 1 s following the reward onset (the time of the first licking response since the delay offset) was significantly different between rewarded and unrewarded trials for PV neurons (15.6 ± 1.8 and 19.2 ± 2.1 Hz, respectively; paired t test, p = ), but not for SOM neurons (ns-som neurons, 18.9 ± 5.5 and 19.3 ± 6.8 Hz, respectively; p = 0.860; ws-som, 5.2 ± 1.0 Hz and 4.9 ± 0.9 Hz, respectively, p = 0.502; also see Figure 5G). Similar results were obtained when we examined reward-related activity considering other confounding variables (such as licking response) using a generalized linear model (GLM; Supplemental Experimental Procedures) (Figure 5H). Mean GLM coefficients for reward and no reward during the first 1 s since the reward onset were significantly different from each other for PV, but not ns-som or ws-som neurons (t test, p = 0.002, 0.191, and 0.767, respectively). For untagged type I and II neurons, mean GLM coefficients for reward and no reward were not significantly different from each other (Figures S7E S7G). Other Behavioral Correlates We also examined behavioral correlates of PV and SOM neurons other than delay-period activity and reward-related responses. First, we examined PV and SOM neuronal responses in association with trial onset, sample period onset (arrival at the sample target), and delay onset in addition to reward period onset (the times of breaking photobeam sensors shown in Figure 1A). Neural activity related to leaving a reward site (Kvitsiani et al., 2013)was not analyzed because we could not precisely determine the animal s position at the reward site due to frequent interferences of position signals by recording/optic cables. Other than rewarddependent firing of PV and ns-som neurons, significant eventassociated responses (comparison of neural activity during the 1 s periods before and after each event onset; t test) were observed only for PV neurons at trial onset and sample period onset (Figure 6A), which might be contributed from movement speed-dependent firing of PV neurons (see below). Mean firing rates during the 3 s time period before delay onset and the entire delay period (3 s) were significantly correlated for all neuron subtypes, albeit they were more variable in ws-som neurons, and no significant difference was found between them (t test, PV, p = 0.710; ns-som, p = 0.780; and ws-som, p = 0.498; Figure 6B). Second, to examine movement speed-dependent firing, we binned the entire task into 1 s duration bins and calculated Pearson s correlation coefficient between neuronal firing rate and movement speed for each neuron. The time period from reward onset to 1 s before trial onset was excluded from this analysis to prevent reward-related neural responses from contributing to the analysis outcome. In the majority of neurons, neural activity was significantly correlated with movement speed (PV, 17 out of 21, 81.0%; ns-som, 8 out of 9, 88.9%; and ws-som, 9 out of 14, 64.3%), with PV neurons showing a significant bias toward positive correlation with movement speed (r = ± 0.046, t test, p = ). Third, we examined target-dependent firing during the sample and reward periods, which may be related to different sensory and/or motor responses at different locations in the maze. The magnitudes of target preference during the sample and reward periods (first 2 s) were significantly larger for SOM (ns- SOM and ws-som combined) than PV neurons (sample period, ± and ± 0.020, respectively; t test, p = 0.002; reward period, ± and ± 0.031, respectively; p = 0.044; Figure 6D). Fourth, we examined licking response-related neuronal activity during the Pavlovian conditioning task. A previous study has shown that discharges of SOM neurons (ns-som and ws-som not distinguished) are strongly responsive to licking responses (Pinto and Dan, 2015). As shown by the examples in Figure 6E, licking rate was higher in rewarded than unrewarded trials in the majority of sessions. An analysis using a GLM revealed that ns-som neurons tended to increase their firing rates in association with licking responses (Figures 6F and 6G). Note that this result cannot account for reward-induced suppression of ns-som neurons during the working memory task (response directions are opposite). Modulation of Delay-Period Activity by Optical Stimulation of PV and SOM Neurons We then examined effects of PV or SOM neuron stimulation on delay-period activity of mpfc neurons. For this, a continuous light pulse was applied unilaterally throughout the delay period ( mw, 3 s), which did not affect behavioral performance (correct choice, no stimulation, 96.3% ± 0.7%; light stimulation, 96.8% ± 0.4%; paired t test, p = 0.310). Light stimulation induced varying degrees of response suppression in both type I and II neurons, along with occasional mild response enhancement (Figures 7A and S8), in both PV-Cre and SOM-Cre mice. Suppression of type I neuronal activity was stronger in SOM-Cre than PV-Cre mice (46.9% ± 4.9% and 26.7% ± 7.7% suppression, respectively; t test, p = 0.031) and, conversely, suppression of type II neuronal activity was stronger in PV-Cre than SOM-Cre mice (44.8% ± 4.0% and 14.0% ± 4.9% suppression, respectively; p = ; only correct trials were analyzed; Figure 7B). As expected, light stimulation enhanced firing rates of optically tagged PV and SOM neurons (Figure 7C). The effect of light stimulation on type II neuronal activity decreased gradually during the delay period, and the rate of decrease was steeper in SOM-Cre than PV-Cre mice (linear regression, slopes, Neuron 92, , November 23, 2016

9 Figure 6. Other Task-Related Responses of PV and SOM Neurons (A) Mean normalized firing rates of PV, ns-som, and ws-som neurons during four different behavioral stages (trial onset, sample target, delay, and reward). The bottom plot shows mean running speed. The trials were divided into correct (blue) and incorrect (gray) trials. The time 0 denotes the onset of each behavioral stage. The bar graphs show mean firing rates before (gray) and after (black) each stage onset (1 s each) in correct trials. (B) Scatterplots showing mean firing rates before (abscissa) and during (ordinate) the delay period (3 s each). The numbers indicate slopes of the regression lines. (C) Frequency histograms showing correlations between movement speed and neuronal activity. The empty and filled bars denote neurons that were significantly and insignificantly correlated with movement speed, respectively. (D) The magnitude of target preference during trial onset, sample, and reward stages (first 2 s following each stage onset). *significant difference between PV and SOM neurons (t test and p < 0.05). (E) Examples of licking and neuronal responses following reward onset (the time of the first licking response since delay offset) in the Pavlovian conditioning task. The lick/spike raster and lick/spike density functions are shown for each example. Blue and gray represent rewarded and unrewarded trials, respectively. (F) Frequency histograms showing correlations between lick rate and neuronal activity. The empty and filled bars denote neurons with significant and insignificant correlations with licking response, respectively. (G) Mean GLM coefficients for lick onset and offset. The bar graphs represent mean and SEM (A and D), and the horizontal error bars (C and F) and shading (A, E, and G) represent SEM. and 0.042, respectively; Figure 7D), which might reflect spike frequency adaptation of activated SOM neurons (Ma et al., 2006; McGarry et al., 2010). We also examined whether PV or SOM neuron stimulation affects delay-period activity of surrounding neurons differently depending on the upcoming target choice (only correct trials were analyzed). Because the stimulation effect of SOM neurons decreased gradually during the delay period, we examined the light stimulation effect for the early (0 1.5 s) and late (1.5 3 s) phases of the delay period separately using the index of target-dependent response suppression (an index for preferential suppression of delay-period activity in ipsilateral versus contralateral-choice trials; Supplemental Experimental Procedures). Light stimulation induced bidirectional changes in the index of target-dependent response suppression so that the indices were not significantly different from zero (t test with Neuron 92, , November 23,

10 Figure 7. Light Stimulation Effects of PV and SOM Neurons on Delay-Period Activity of Type I and II Neurons (A) Examples of simultaneously recorded untagged type I and type II neuronal activity with (deep colors) and without (light colors) light stimulation (correct trials only). The red and blue indicate delay-period activity associated with ipsilateral and contralateral target choices, respectively (top, raster plots and bottom, spike density functions [s = 100 ms]). (B) Mean % suppression of delay-period activity by light stimulation for type I and type II neurons in PV-Cre and SOM-Cre mice. *significant difference between PV- and SOM-Cre mice (t test and p < 0.05). (C) Examples showing light stimulation-induced activation of optically tagged PV and SOM neuronal activity during the delay period. (D) Time courses of light stimulation effect on type II neurons. (E) Effect of light stimulation on delay-period activity in ipsilateral versus contralateral choice trials. Shown are values for the index of target-dependent response suppression (positive and negative values indicate preferential suppression of ipsilateral and contralateral choice-associated delay-period activity, respectively). The delay period was divided into two halves (early, s and late, s) and the effect of light stimulation was separately examined for each half. The bar graphs represent mean and SEM (B and E) and shading represents SEM (A, C, and D). See also Figure S8. Bonferroni correction for multiple comparisons; p values > 0.05; Figure 7E). Also, no significant difference was found between PV and SOM stimulation groups in the absolute index of targetdependent response suppression (p values > 0.05). Effects of Optical Activation of PV and SOM Neurons on Behavioral Performance Finally, we examined whether strong stimulation of inhibitory interneurons impairs behavioral performance using a separate group of animals (nine PV-Cre and eight SOM-Cre mice; ChR2 expression and optical probe locations confirmed with histological examination as in Figure 2; data not shown). We implanted two relatively large optic fibers (diameter, 200 mm), whose cross section area is 10 times larger than that of the ones used in the physiological experiments (diameter, 62.5 mm), in the mpfc bilaterally, and applied optical stimulation that is stronger (3 10 mw; mw/mm 2 ) than that used in the physiological experiments ( mw; mw/mm 2 ). The animals were first trained in the working memory task (delay duration, 3 s) without optical stimulation for 9 days (Figure 8A). No significant difference was found between PV- and SOM-Cre mice in behavioral performance during the initial training (two-way mixed ANOVA, main effect of genotype, F(1,15) = 0.328, p = 0.575; main effect of session, F(8,120) = , p = ; and genotype 3 session interaction, F(8,120) = 1.992, p = 0.053). The animals were then tested with optical stimulation (continuous stimulation throughout the entire delay period) applied in randomly chosen 910 Neuron 92, , November 23, 2016

11 Figure 8. Light Stimulation Effects of PV and SOM Neurons on Behavioral Performance (A) Performance during learning. The animals (nine PV-Cre and eight SOM-Cre mice) were trained in the working memory task (delay duration, 3 s) for 9 days. (B) Effects of light stimulation on behavioral performance of PV- and SOM-Cre mice. The behavioral performances with (+) and without ( ) optical stimulation in the same block are shown together (thin gray lines) for each animal. The behavioral data were grouped together according to delay duration/stimulation intensity conditions (three sessions each). The squares and error bars represent mean and SEM, respectively. *significantly different (two-way ANOVA followed by Bonferroni s post hoc test and p < 0.05). clarify whether and how stimulation effects are different between PV-Cre and SOM-Cre mice. DISCUSSION one-half of trials. They were tested with increasing delay durations (3, 5, and 10 s; three sessions each; 3 mw stimulation), and, at the final stage, with strong optical stimulation (10 mw; 10 s delay; three sessions; Figure 8B). A significant difference was found in the animal s performance between stimulated and unstimulated trials only with 10-mW stimulation for SOM-Cre mice (two-way mixed ANOVA, main effect of light stimulation, F(1,7) = 4.015, p = 0.085; main effect of block, F(3,21) = 2.773, p = 0.067; stimulation 3 block interaction, F(3,21) = 4.849, p = 0.026; Bonferroni s post hoc test for stimulation effect in 10-mW block, p = 0.002; and the other blocks, p values > 0.05), but not for PV-Cre mice (main effect of light stimulation, F(1,7) = 3.330, p = 0.111; main effect of block, F(3,21) = , p = ; stimulation 3 block interaction, F(3,21) = 0.701, p = 0.562; Bonferroni s post hoc test for block performance, block 1 versus 3, 1 versus 4, and 2 versus 3, p values < 0.05; and all other comparisons, p values > 0.05). We also ran a three-way mixed ANOVA for a direct comparison of stimulation effects between PV-Cre and SOM-Cre mice. We found significant main effects of light stimulation (F(1,14) = 6.247, p = 0.025) and block (F(3,42) = , p = ), but no significant effect of genotype (F(1,14) = 0.327, p = 0.577) or interaction terms (stimulation 3 block, F(3,42) = 2.495, p = 0.073; genotype 3 stimulation, F(1,14) = 0.657, p = 0.431; genotype 3 block, F(3,42) = 2.744, p = 0.055; and genotype 3 stimulation 3 block, F(3,42) = 2.092, p = 0.116). The absence of significant interaction effects might be due to the small sample size. Additional experiments are needed to To investigate roles of PV and SOM interneurons in working memory, we examined discharge characteristics and stimulation effects of PV and SOM interneurons in the mpfc of mice performing a spatial working memory task. We found several differences between the two subtypes of GABAergic interneurons. First, SOM neurons showed stronger target-dependent delay-period activity than PV neurons. Second, PV neurons showed tonic discharges, but a subset of SOM neurons showed phasic discharges. Third, delay-period activity of SOM, but not PV, neurons differed between correct and error trials. Fourth, PV neurons showed stronger and more consistent suppression of activity following reward delivery than SOM neurons. Fifth, optogenetic stimulation of SOM and PV neurons preferentially suppressed type I and type II neuronal activity, respectively. These results suggest distinct roles of different mpfc interneuron subtypes in working memory and reward processing. Role of SOM Neurons in Maintaining Working Memory Our results suggest a more important role of SOM than PV neurons in maintaining the content of working memory. High target selectivity of SOM neuronal activity suggests that SOM neurons can selectively modulate the content of information processed by nearby pyramidal neurons. Furthermore, some ws-som neurons fired phasically during the delay period, indicating that they can influence delay-period activity of nearby pyramidal neurons at specific epochs during the delay period rather than continuously maintaining inhibitory tones. These features of SOM neurons would be well suited to shape temporal dynamics of mpfc neuronal ensemble activity along a specific trajectory. In line with these results, delay-period activity of SOM neurons well predicted the identity of sample target in correct trials, Neuron 92, , November 23,

12 but poorly in error trials, indicating that the animal s performance and SOM neuronal activity were correlated. Moreover, SOM neuron stimulation slightly, but significantly, impaired the animal s performance in the working memory task. These results provide converging evidence that SOM neurons contribute to maintaining the content of working memory during the delay period. Unlike PV neurons, SOM neurons mainly target distal dendrites of pyramidal cells and can mediate localized inhibition (Hangya et al., 2014; Hu et al., 2014; Rudy et al., 2011). We also found relatively weak effects of SOM neuron stimulation compared to PV neuron stimulation in suppressing activity of nearby type II neurons. Collectively, these results suggest that SOM neurons modulate working-memory processes of nearby pyramidal neurons by influencing dendritic processing rather than directly regulating the output of pyramidal neurons. We found similar numbers of ipsilateral and contralateral choice-preferring SOM neurons in the mpfc, so that their mean target preference was not significantly different from zero. We also failed to find evidence for preferential suppression of ipsilateral or contralateral choice-related delay-period activity of surrounding neurons by optogenetic stimulation of SOM neurons. These results suggest that SOM neurons, as a population, process and influence ipsilateral and contralateral target-related information similarly. Type II neurons also showed mixed target preferences so that their mean target preference was not significantly different from zero. These findings suggest that the mpfc processes ipsilateral and contralateral target-related information in an unbiased manner. It remains to be determined, however, whether different mpfc neuron populations projecting to different parts of the brain also process ipsilateral and contralateral target-related information similarly (Li et al., 2015). Role of PV Neurons in Gain Control Our results are consistent with the proposal that PV interneurons play an important role in regulating response gain of nearby pyramidal neurons (Atallah et al., 2012; Cardin et al., 2009; Wilson et al., 2012). First, PV neurons showed low target-dependent firing. Second, PV neuronal activity during correct and error trials conveyed similar information about the sample target and upcoming goal choice. Third, unlike ws-som neurons, PV neurons were active largely throughout the delay period. Fourth, optogenetic stimulation of PV neurons suppressed activity of untagged type II neurons more strongly compared to optogenetic stimulation of SOM neurons. These results support the view that PV neurons play an important role in regulating overall activity level of principal neurons, but do not modulate information processed by individual principal neurons in a selective manner (Hangya et al., 2014; Roux and Buzsáki, 2015; Rudy et al., 2011). PV basket cells mainly target cell bodies of pyramidal neurons and mediate powerful inhibition (Hangya et al., 2014; Hu et al., 2014; Rudy et al., 2011). Low target-dependent firing of PV neurons, along with localization of their synapses on cell bodies of pyramidal cells, will lead to similar inhibitory influences on nearby pyramidal cells in ipsilateral and contralateral target-choice trials. Retrospective versus Prospective Memory Our task does not allow dissociating whether mpfc delayperiod activity is related to the previously visited target (retrospective memory) or upcoming target choice (prospective memory). Nevertheless, the results from the sliding-window analysis of error trials are consistent with the possibility that retrospective and prospective components of working memory are stronger in the early and late delay period, respectively (Rainer et al., 1999; Takeda and Funahashi, 2002, 2004). With the caveat that the analysis of error trials is limited in dissociating retrospective versus prospective memory because working memory might not be properly maintained during the delay period in error trials, our results suggest that SOM neurons can selectively modulate the content of retrospective as well as prospective working memory, because their target selectivity was maintained largely throughout the delay period. By contrast, target selectivity of PV neurons was low throughout the delay period, and error-trial activity of PV neurons poorly predicted the animal s upcoming target selection. Delay-period activity of PV neurons, once set, might have a tendency to be maintained that way regardless of the animal s upcoming choice. Small numbers of error trials and optically tagged neurons prevented us from subjecting them to a sliding-window analysis. Collecting larger numbers of PV and SOM neurons in a more difficult working memory task (i.e., a larger number of error trials) and performing the same analysis may provide useful information about mpfc neural circuit dynamics underlying working memory. Stimulation Effect on Behavior Optical stimulation of PV neurons tended to suppress behavioral performance at long delays (5 10 s), but the effects failed to reach statistical significance. This result was somewhat surprising, because PV-neuron stimulation supposedly suppresses type II neuronal activity more strongly than SOM-neuron stimulation. The result may be because a small group of active mpfc neurons is sufficient to support behavioral performance in a delayed response task (Baeg et al., 2003 and Figure 4A). For a simple working memory task as the one used here, a small proportion of the mpfc neural network might be sufficient to mediate correct performance. This might be a reason why stimulation of PV neurons, whose delay-period activity was of low target selectivity and little related to the animal s upcoming target selection, exerted no significant effect on behavior even with strong stimulation. For the same reason, SOM-neuron stimulation might not be very effective in influencing behavioral performance. SOMneuron stimulation is likely to disrupt target-specific activity of pyramidal neurons, but not in a grossly biased matter, because SOM neurons, as a population, process and influence ipsilateral and contralateral target-related information similarly. Only strong bilateral stimulation of SOM neurons may disrupt target-specific activity of a sufficiently large proportion of mpfc pyramidal neurons enough to impair behavioral performance, which is consistent with a recent finding (Li et al., 2015). Optogenetic stimulation of inhibitory interneurons in the mouse mpfc significantly impaired working memory performance in one study (activation of PV neurons; Rossi et al., 2012), but had no effect in another (activation of GABAergic neurons; Liu et al., 2014). Our results are not perfectly in line with either study. The reasons for the inconsistency across our and these studies are unclear, but there exist numerous differences in experimental procedures, such as stimulated inhibitory neuron subtypes, task structure, 912 Neuron 92, , November 23, 2016