Feedback inhibition is recruited steeply with a low dynamic range (0% to 4% of active GCs), and with a non-uniform spatial profile. Additionally, net feedback inhibition shows frequency-dependent facilitation, driven by strongly facilitating mossy fiber inputs. Computational analyses show a significant contribution of the feedback circuit to pattern separation of theta modulated inputs, even within individual theta cycles. Moreover, pattern separation was selectively boosted at gamma frequencies, in particular for highly similar inputs. This effect was highly robust, suggesting that frequency-dependent pattern separation is a key feature of the feedback inhibitory microcircuit.Feedback inhibitory motifs are thought to be important for pattern separation across species. How feedback circuits may implement pattern separation of biologically plausible, temporally structured input in mammals is, however, poorly understood. We have quantitatively determined key properties of  You can probably recall where you left your car this morning without too much trouble. But assuming you use the same busy parking lot every day, can you remember which space you parked in yesterday? Or the day before that? Most people find this difficult not because they cannot remember what happened two or three days ago, but because it requires distinguishing between very similar memories. The car, the parking lot, and the time of day were the same on each occasion. So how do you remember where you parked this morning?This ability to distinguish between memories of similar events depends on a brain region called the hippocampus. A subregion of the hippocampus called the dentate gyrus generates different patterns of activity in response to events that are similar but distinct. This process is called pattern separation, and it helps ensure that you do not look for your car in yesterday’s parking space.Pattern separation in the dentate gyrus is thought to involve a form of negative feedback called feedback inhibition, a phenomenon where the output of a process acts to limit or stop the same process. To test this idea, Braganza et al. studied feedback inhibition in the dentate gyrus of mice, before building a computer model simulating the inhibition process and supplying the model with two types of realistic input. The first consisted of low-frequency theta brainwaves, which occur, for instance, in the dentate gyrus when animals explore their environment. The second consisted of higher frequency gamma brainwaves, which occur, for example, when animals experience something new.Testing the model showed that feedback inhibition contributes to pattern separation with both theta and gamma inputs. However, pattern separation is stronger with gamma input. This suggests that high frequency brainwaves in the hippocampus could help animals distinguish new events from old ones by promoting pattern separation.Various brain disorders, including Alzheimer’s disease, schizophrenia and epilepsy, involve changes in the dentate gyrus and altered brain rhythms. The current findings could help reveal how these changes contribute to memory impairments and to a reduced ability to distinguish similar experiences. Efficiently discriminating similar percepts or experiences is a central capability common to invertebrate and vertebrate species. In general terms, such discrimination can be achieved by decreasing the overlap in representations by neuronal ensembles between input and output patterns, a process termed ‘pattern separation’ . Numerounet inhibition arising from GC activity.In mammals, substantial evidence points toward a role of the hippocampal dentate gyrus (DG) for pattern separation during memory formation and spatial discrimination . The DG net inhibition, that is the dynamic range and gain of the feedback inhibitory microcircuit. It is furthermore necessary to quantify the spatial and temporal properties of the elicited inhibition, in order to investigate its impact on biologically plausible, temporally structured input. For instance, the DG shows prominent theta oscillations during exploration and distinctive slow-gamma activity during associative memory encoding  to its net output (feedback inhibition in GCs). To this end, we antidromically recruited feedback inhibitory circuits, while simultaneously recording GC inhibition and population activity , antidromic stimulation caused action potential associated Ca2+ elevations in a subset of GCs -eYFP in GCs were created by crossing Prox1-Cre mice with Ai32-mice . Local IPSCs occurred with shorter latency and lower jitter than remote IPSCs  to output correlation  thereby demonstrating robust pattern separation over the whole range of input similarities  and measured the area to the identity line , which is subtracted from the corresponding Rout value in the same network without this input (no FB). This procedure isolates the effect of interest (ΔRout) for each individual comparison, controlling for other sources of variability. A single pattern separation measure was then obtained as before, as the area under the curve of bin-wise means of these ΔRout values  the spatial tuning and 2) the facilitation of the feedback circuit . To thisout > 0 within the first 100 ms bin, Wilcoxon signed rank test with Bonferroni corrected p-values: p=0.031,=0.031 for full and FB effect respectively in both paradigms). While mean ΔRout did not differ between frequency paradigms within this first time window, it was significantly elevated in the 30 Hz paradigm in all subsequent time windows . Again, the selective increase during slow-gamma modulated inputs was driven by feedback inhibition , including a contribution from MF facilitation . These results were robust when analysis time windows were decreased even further  and gamma (~10–33 ms) cycles . Indeed,litation . As abovNext, we asked if the frequency dependence of feedback inhibitory pattern separation was sensitive to variations of the inhibitory decay time constants and if there might be an interaction between these decay time constants and the frequency range at which pattern separation is most effective . RemarkaNext, we tested if our main results were robust to alterations in the relative strengths of feedforward vs. feedback inhibition. Since, our model is closely constrained with respect to the recruitment and functional properties of the feedback circuit, we are confident about the resulting computational inferences concerning this circuit. However, the model does not allow strong inferences about the relative roles of feedback and feedforward inhibition, and it is thus necessary to probe if extremely powerful feedforward inhibition might occlude the effects described here. We therefore selectively enhanced the feedforward inhibitory circuit in our model by increasing the PP to BC circuit 2x . This roFinally, we probed the robustness of our findings for various perforant path input strengths . We founTogether these results suggest that frequency dependence is a key feature of the feedback inhibitory microcircuits and predict that feedback inhibition selectively boosts the separation of highly similar input patterns during gamma oscillations.net feedback inhibition is functionally organized in mammals, and how it may contribute to pattern separation of biologically relevant, temporally structured input patterns is, however, incompletely understood.Across brain regions and species, inhibitory circuits contribute critically to regulating the sparsity and overlap of neural representations . In mostnet feedback inhibition by defined GC population sizes in space and time in the hippocampal DG, a structure in which sparse activity and inhibition are thought to critically contribute to the function of pattern separation (net feedback inhibition (net feedback inhibition is steeply recruited by sparse populations of GCs (<4%). This is in good agreement with the sparse range of GC activity reported in vivo . Optogenetic experiments and the remaining dual recording experiments were performed on double transgenic offspring of Tg(Prox1-cre)SJ39Gsat/Mmucd, MMRRC Cat# 036632-UCD, RRID: MMRRC_036632-UCD) obtained as cryopreserved sperm and rederived in the local facility (tm32(CAG-COP4*H134R/EYFP)Hze/J, IMSR Cat# JAX:012569, RRID: IMSR_JAX:012569). For preparation the animals were deeply anesthetized with Isoflurane  and decapitated. The head was instantaneously submerged in ice-cold carbogen saturated artificial cerebrospinal fluid  and the brain removed.All experimental procedures were conducted in accordance to federal law of the state of North Rhine-Westphalia (Aktenzeichen 84–02.04.2014.A254), minimizing unnecessary pain and discomfort. Experiments were performed on horizontal hippocampal slices of 21- to 97-day-old mice. Cafacility  and Ai322PO4, 1.25; NaHCO3, 26; CaCl2, 2.0; MgCl2, 2.0; glucose, 15) at room temperature. Recordings were performed in a submerged recording chamber at 33–35°C under constant superfusion with carbogen saturated ACSF (3 ml/min). Experiments were performed in the superior blade unless otherwise indicated.Horizontal 350 µm thick sections were cut with a vibratome . To obtain maximum-connectivity-plane slices the brain was glued to its dorsal surface compare . The sli3O3SCs, 140; 4-(2-hydroxyethyl)−1-piperazineethanesulfonic acid (HEPES-acid), 5; ethylene glycol tetraacetic acid (EGTA), 0.16; MgCl2, 0.5; sodium phosphocreatine, 5; glucose, 10). For GC current clamp experiments a low-chloride solution −1-piperazineethanesulfonic acid (HEPES-acid), 5; ethylene glycol tetraacetic acid (EGTA), 0.16; MgCl2, 0.5; sodium phosphocreatine, 5) was used. GCs with input resistances greater than 300 MΩ were discarded in order to exclude immature GCs . In all imaging experiments and a subset of optogenetic experiments, the intracellular solution additionally contained 100 µM Alexa 594 hydrazide sodium salt . The identity of visually and electrophysiologically identified mature GC was confirmed by their dendritic morphology after dye filling in every case tested. Pipette resistance of the patch pipettes was 3–7 MΩ. Voltage-clamp recordings were performed with a Multiclamp 700B  or a BVC-700A amplifier . Current-clamp recordings were performed with a Multiclamp 700B. Voltage or current signals were digitized with a Digidata 1322A (Molecular Devices) or  at 10 or 50 kHz and recorded using Clampex 10.2 (Molecular Devices) or Igor Pro 6  on a PC running Windows XP. All electrophysiological recordings were obtained at least in triplicate, then averaged and counted as a single biological replicate. For IPSC measurements, cells were held at 0 mV including liquid-junction potential correction (estimated at 16 mV). To aid the voltage clamp throughout the cell, this depolarized membrane potential was slowly approached during a 15 min pre-equilibration period, during which Cs+ entered the cell. For CC-recordings liquid junction potential was not corrected. IPSCs were normalized to the maximally elicited IPSC over space and power for each respective cell. Importantly, this normalization does not require prespecification of the location or power at which a respective cell’s maximum occurs. Note, that due to this procedure all normalized IPSC values are by definition below 100%. Chemicals for electrophysiological experiments were obtained from Sigma-Aldrich (St. Louis). All drugs were purchased from Tocris Bioscience .Hippocampal dentate GCs were visually identified using infrared oblique illumination contrast microscopy in a 20x or 60x water immersion objective  on an upright microscope . For IPSC measurements the whole-cell patch-clamp configuration was established with a low chloride cesium-methane-sulfonate based intracellular solution (intracellular solution containing in mM: CHture GCs . Hilar cTwo GCs within 100 µm of each other were recorded. To test for single GC-induced feedback inhibition 10 to 15 trains of 10 APs at 100 Hz were elicited by brief (3 ms) current injections in one cell. Inhibition was monitored either in VC, while holding the cell at 0 mV to allow the detection of small IPSCs  with excitation light at 810 nm. Images were acquired with a digital CMOS camera  through a high numerical aperture 20x water immersion Objective . This allowed imaging of a large field of view (320 × 240 µm) with high spatial and temporal resolution  at acceptable signal to noise ratios. Time series were processed with ImageJ 1.48o and IGOR Pro 6.3 in a semiautomatic manner. Regions of interest were manually placed onto all well loaded cells which remained visible throughout the experiment. Ca2+ fluorescence increase normalized to baseline (ΔF/F) traces of individual cells were calculated without background subtraction. The fraction of responders for each time series was extracted by automatic thresholding at ΔF/F = 0.94%. The threshold was determined by combined cell-attached and Ca2+ imaging experiments. Note, that for these experiments the stimulation electrode was placed into the hilus in order to obtain a sufficient number of true positive responders. The imaged cell population comprised on average 46 ± 18 (standard deviation) cells (n = 23 slices). The active cell fraction corresponds to the fraction of responders normalized to the dye-loaded population within each section. To assess the spatial distribution of cell activation in imaging experiments, ΔF/F projections were created by averaging and smoothing four frames during the transient and four frames at baseline fluorescence and then calculating the pixel wise ΔF/F.Dye loading was modified from Antidromic electrical stimulation was achieved using a bipolar cluster microelectrode  connected to a digital stimulus isolator , placed into stratum lucidum in the CA3 region. IPSCs at individual powers were elicited 5 to 13 times at 0.1 Hz and averaged (0.1 ms pulse time). The amplitude beyond which the stimulus isolator could not pass the full current, determined the maximal stimulation amplitude for each experiment.In order to obtain the input-output relationships of the feedback inhibitory circuit data, each variable was averaged over slices by power. This was necessary since only a small subset of experiments in which inhibition was completely blocked could also be successfully imaged (6 of 8 sections). Due to the small numbers of active cells within individual slices with sufficient dye loading (n = 23 slices) analysis of only these six slices leads to a very piecemeal recruitment curve. A more accurate estimation of the recruitment of feedback inhibition was obtained by averaging the cell activation and inhibition over all appropriate slices and relating them by power, respectively. Note that while the fraction of activated cells in non-MCP sections  was mostly zero, IPSCs were almost always present (in 28 of 29 cells in non-MCP sections).Focal optogenetic stimulation was achieved through a galvanometer driven spot illumination device coupled to a 473 nm DPSS Laser  on an upright microscope . The width of the resulting stimulation spot at the focal plane was 8.36 ± 0.04 µm . All illumination spots were placed at approximately 40 µm into the ML at the slice surface. Stimulation pulses were of 20 ms duration.To measure the light intensity distribution throughout a slice the setup was modified to image the slice from below while the laser beam was focused to its surface . This waTo assess the active fraction of GCs, approximately two GCs were recorded in cell-attached mode in each slice in which an IPSC was recorded. Illumination spots were placed along the GC layer at 100 µm intervals . The entTo globally activate the GC population a multimode light fiber  coupled to a 473 nm laser  was placed above the slice surface, non-specifically illuminating the entire hippocampus. Analogous to focal stimulations, the activated cell fraction was calculated as the firing probability of individual cells following 20 ms pulses. Here, no spatial normalization is necessary since cells were sampled from random locations with respect to the light fiber. Firing probabilities for the focal stimulation in these sections was calculated as the simple average of all stimulation locations.The same stimulation paradigm which was used to assess cell activation was used to assess the spatial distribution of feedback inhibition. For individual cells, IPSCs at each location and power were averaged. The entire profile was normalized to the largest measured IPSC of that cell, independent of the power and stimulation location at which it occurred. For analysis, all IPSC profiles were spatially aligned to the recorded cells. The mean distance to apex ± one standard deviation was 356 ± 163 µm and 322 ± 97 µm for cells from the superior and inferior blade, respectively (n = 8 cells in each blade). In order to test whether there were any distinct effects of the apex, such as a steep decay of inhibition, which would be masked by alignment to the recorded cells, we also aligned the profiles to the apex (not shown). However, no such effects were visible. To analyze the saturated IPSC profiles, normalized IPSC amplitudes from p=5 to 7 were averaged for each cell. In order to analyze the effects of local versus remote stimulation for each blade a distance was chosen such that each remote location was still within the DG but in the other blade (800 µm from the recorded cell). Normalized IPSCs of the three locations surrounding the recorded cell or this remote location were averaged within each power to obtain the IPSC amplitudes for further analysis. The cell fraction required for the activation of a half-maximal IPSC in each section was assessed for each cell by linear interpolation between the measured values. Since no differences were found between superior and inferior inhibition, recordings of both blades were pooled to analyze the kinetic properties of IPSCs. All parameters were calculated on the multiple trials of individual cells. The latency was measured as the time from the beginning of the pulse to when the IPSC superseded six fold standard deviation of the baseline. The jitter was calculated as the standard deviation of these latencies for individual cells. The rise time was calculated as the mean 20 to 80 rise time of each cell and the decay time constant was obtained from an exponential fit to the decaying phase of the compound IPSC.Intrinsic properties of hilar cells were quantified based on 4.6 s long depolarizing current steps or 500 ms hyperpolarizing current steps. AP threshold and fast AHP amplitude were measured from the first AP in the first current step in which an AP occurred within the first 10 ms. Clustering fraction and mean AP time were calculated from the current injection that elicited the maximum average AP frequency. The Clustering fraction represents the fraction of APs that occur within 60 ms before or after another AP . Mean APAfter recording, slices were fixed for 1 hr in 4% PFA and stored overnight in 0.25% PBS-T at room temperature. The following day they were transferred to PBS for short term storage or immediately stained. For biocytin staining, sections were washed with PBS and incubated with Streptavidin-Alexa-Fluor-555 Conjugate , 1:1000 in 0.25% PBS-T overnight at 4°C. The following day they were co-stained with DAPI 1:1000 in PBS for 30 min and mounted with Aqua-Poly/Mount. Cells were imaged with the Leica SP8 Confocal Microscope of the Microscopy Core Facility at the University Clinic Bonn using a 40x water immersion objective.Short-term dynamics of GCs and hilar cells were assessed using antidromic electrical or optogenetic stimulation at minimal power . Trains of 10 pulses at 1, 10, 30, 50 Hz were delivered in triplicate and averaged (excluding sweeps with action currents for hilar cells). In all GCs and a subset of hilar cells we confirmed that PSCs could be blocked by at least 90% with 40 µM CNQX + 50 µM D-APV . Facilitation indices were obtained by normalizing the average of the last three PSC peaks to the first.To test for differential dynamics between local and remote inhibition analogous trains of optogenetic 20 ms pulses at powers below saturation  were delivered. For each power and frequency, five repeats were recorded and averaged. AP probabilities were assessed by cell-attached recordings with the stimulation site close to the recorded cell. Cell-attached spikes were detected by automatic thresholding as above.−2 and a leak conductance = 0.0002 Scm−2, which gave an Rin of 165 MΩ, were adopted from A simple multicompartmental passive ‘ball and stick’ model with number of segments following the d_lambda rule  and passhttps://github.com/danielmk/ouropy; copy archived at https://github.com/elifesciences-publications/ouropy) which wraps NEURON’s python module, into which we ported the conductance based DG model by https://github.com/danielmk/pyDentateeLife2020 (copy archived at https://github.com/elifesciences-publications/pyDentateeLife2020).Simulations were run in python 2.7 with NEURON 7.4  on WindoWe first tuned the original model to capture our experimentally determined properties in the most parsimonious way. During tuning we also updated some model properties to better reflect current data and our experimental paradigm in an individual DG lamella:2+ channel mechanism into MCs to more realistically reflect the depolarizing envelope at the onset of a positive current step observed in real MCs. Furthermore, while the original model placed the perforant path input at the distal dendrite of GCs, we moved all perforant path synapses to the middle compartment of the dendrite. In order to be able to capture the results of convergent and divergent synaptic inputs in sufficient resolution to produce the empirically observed activity gradations, we up-scaled cell numbers by a factor of four. To model space, we assumed all cell types to be spread out on a 2 mm DG lamella. Since MCs project to GCs primarily outside the lamellar plane, we removed the MC to GC connection. To allow patterned PP input we adapted PP input specifications from We introduced a T-type CaWe then proceeded in a first phase of model adjustment, and adapted several parameters to reproduce our in vitro findings regarding spatial and temporal feedback inhibition . To modeTo study pattern separation, we generated 400 PP inputs. Each PP synapsed onto 100 randomly chosen GCs with the spatial connection probability being governed by a gaussian probability distribution with standard deviation 1 mm and random peak position, modeling a full, nearly uniform input connectivity of individual afferents . To geneTo quantify pattern similarity, we used Pearson’s product moment correlation coefficient R of the population rate vectors for input and output patterns. The population rate vector refers to the vector of the mean firing rates of all cells in the population within the entire 600 ms simulation, or 100 or 33 ms time windows for the time resolved analyses. All statistical analyses of the model were performed with n = 7 different random network seeds. During Model development (tuning phase), we first ported the model by out within Rin bins of 0.1 and measured the area to the unity line (computed as the mean of the binwise Rin – Rout differences). To compute isolated pattern separation effects of specific manipulations we subtracted the respective Rout values with and without the manipulation, thereby obtaining a ΔRout value for each individual Rin. We then again computed the bin-wise mean and quantified the area under the curve, yielding the mean ΔRout analogous to the full effects. Note, that the sequence of averaging and subtracting is irrelevant, and was inverted only to match the figure panels. Data are displayed as mean ± SEM for each Rin bin  and the tuned network models. For the temporally resolved pattern separation analysis, all measures were computed as above, but on population vector correlations within 100 or 33 ms time bins.To compute full pattern separation effects , we calc Rin bin . The coeAnalyses were performed using ImageJ, Microsoft Excel, Python and Igor Pro. Fits were performed using Igor Pro. Statistical analyses were performed using GraphPad Prism six or Igor Pro. Comparisons were two-tailed whenever applicable. Replicates refer to cells unless otherwise indicated (slices for imaging experiments and network seeds for modeling data). Given the lack of previous information on effect sizes, sample sizes were chosen according to field norms, such that only large effects can be detected (e.g. Cohen’s d > 1 for paired tests). A single outlier facilitation index  during m In the interests of transparency, eLife publishes the most substantive revision requests and the accompanying author responses.Acceptance summary:This study utilizes electrophysiological, optogenetic and computational modeling approaches to provide comprehensive and quantitative description of the spatiotemporal properties of the dentate gyrus feedback inhibitory microcircuitry. It predicts that these properties selectively enhance the separation of highly similar input patterns during learning- related gamma oscillations. The data were found to be high quality, and the idea that pattern separation is frequency dependent will likely be of interest to many researchers.Decision letter after peer review:eLife. Your article has been reviewed by three peer reviewers, and the evaluation has been overseen by a Reviewing Editor and Laura Colgin as the Senior Editor. The following individual involved in review of your submission has agreed to reveal their identity: Mathew V Jones (Reviewer #2).Thank you for submitting your article "Quantitative properties of a feedback circuit predict frequency-dependent pattern separation" for consideration by The reviewers have discussed the reviews with one another and the Reviewing Editor has drafted this decision to help you prepare a revised submission.Summary:This study examines the recruitment of feedback inhibitory interneurons by dentate gyrus (DG) granule cells (GCs) using single whole-cell recordings from GCs during extracellular stimulation of mossy fiber axons in CA3 or optogenetic stimulation of GC populations. The authors very elegantly demonstrate that feedback inhibition in the DG is recruited in a low dynamic range of 0-4% of the active GC population. Moreover, the study demonstrates that inhibition is not uniformly distributed but inhibitory signals show a larger amplitude, low jitter of individually evoked IPSCs and faster time course when induced close to the recorded GC than inhibitory signals evoked at more remote sites in the DG. On the basis of the experimental data and a neuronal network model consisting of the various neuronal components of the DG, the authors demonstrate that such a network is suited to perform pattern separation between two overlapping inputs provided by the modelled perforant path particularly at gamma frequencies. The experimental and computational work is very well performed and the data are of high interest to the currently broadly discussed potential role of the DG in pattern separation. However, addressing the following issues would enhance the strength of the conclusions.Essential revisions:1) "Feedback inhibition is recruited steeply with low dynamic range (0-4% of activate GCs)". This central conclusion is very interesting but not fully convincing.2+ imaging of the MFs near the stimulation site would provide a better dynamic range to make a quantitative estimate of% active MFs.In Figure 1, only 4% of GCs are activated by antidromic stimulation  which subsequently leads to the 4% ceiling shown in Figure 1K. But the critical parameter may not be how many GCs are stimulated but rather how many MFs are stimulated, since MFs that are unconnected to GCs likely contribute to feedback inhibition . Whether the low fraction of GCs responding to antidromic stimulation reflects low connectivity in the slice or inability to recruit MFs is unclear, but either way it raises a question about the robustness of this assay because this limitation dictates the upper limit for% active GCs. Reviewers wondered if CaFigure 2. Reviewers appreciated that the authors use a 2nd method to estimate feedback inhibition wherein the full range of MF/GC activation can be assessed. Reviewers found it very surprising that the max IPSC evoked by focal and global Chr2 stimulation was similar , and that the range of max IPSCs was so variable (<0.1 to >0.8 nA). What is the fraction of hilar interneurons recruited by focal and global ChR2 activation? This might help explain this surprising result and the variability. The authors state  that the dynamic range is determined by the cellular connectivity patterns, but it is not clear how connectivity could generate this narrow dynamic range – please explain. In measuring feedback IPSCs, the authors addressed voltage escape for dendritic IPSCs , but they did not address whether the ChR2-mediated conductance evoked by global light activation affects the measurement of the global max IPSC. In addition, why do the normalized curves not reach 100% ? Same question for all other figures where "IPSC [%]" is reported.2) Figure 3. The slow rise time of IPSCs seem inconsistent with PV interneurons that are thought to be the major source of feedback inhibition. Presumably the IPSCs are slowed by asynchrony/compound events which might be assessed by quantifying individual events rather than averages to infer PV involvement. Were any PV interneurons recruited in Figure 4? It seems important to address the contribution of PVs since they are critical interneuron subtype in the model. Based on reported uIPSC from PVs, can the authors estimate how many PVs might be contributing to feedback inhibition?3) The small number of active PP afferents used (24 of 400) in the input patterns meant that only about 10% of GCs were active even in the absence of inhibition . Just as the authors tested the robustness of their conclusions in the face of stronger feed-forward inhibition, it also seems important to assess the robustness of the conclusions across a range of excitatory drive that might represent EC activity under various conditions.4) Subsection “Quantitative physiological properties of DG feedback inhibition”. The authors report that the range of active GCs that saturate feedback inhibition is in the range of active GCs reported in vivo. However, Pernia-Andrade and Jonas, 2014, Pilz et al., 2016, and Diamantaki et al., 2016, report higher fractions of active GCs in vivo, although reports using cFos labeling typically support the 1-4% range. The authors should discuss more explicitly the literature about GC activity in vivo and the interpretations of their small dynamic range of feedback inhibition in light of the possibility that GC activity might not be as sparse as suggested by cFos.5) The nature of the facilitating inhibition remains unclear. Fast-spiking basket cells in the hippocampus usually show paired pulse or multiple pulse depression and for only few interneuron types paired pulse or multiple pulse facilitation has been observed. Whole-cell recordings from GCs during optogenetic stimulation of the different types of DG-interneurons might help to dissect the nature of the facilitating inhibitory inputs (e.g. Somatostatin-expressing cells).6) GCs also contact Mossy cells (MCs), which in turn recruit DG interneurons and thereby inhibit GCs. It remained unclear why the MC feedback excitation of hilar interneurons was removed from the network model. It is an important functional element of this circuitry, which provides inhibition to GCs.7) Subsection “Input-output relation of the feedback inhibitory microcircuit”, second paragraph and Figure 1—figure supplement 1 legend – Is it correct that the detection threshold used (0.94%) leads to a "true positive rate of 3%"? That seems very low, and implies that 97% of true responses were not detected. Unless this is a typo, is this not a serious problem that implies that the estimates of the active fraction of GCs are extreme underestimates?8) Frequency-dependence of facilitation – Please state explicitly whether there was a "frequency tuning"  or whether all frequencies {greater than or equal to} 10 Hz displayed the same facilitation ratio (greater than 1).out curve, for the seven model families chosen, would be a first-pass at addressing these questions.9) Spike rates – Here, pattern separation was computed from Pearson's correlation, dot product and pattern overlap of population spike rate vectors, all of which are in general sensitive to absolute spike rates. Therefore, it is not surprising that some of the manipulations in the model  would enhance correlations by increasing GC spike rates, thus reducing pattern separation. It would be extremely useful to know if the different outcomes in pattern separation are driven mainly by the impact of the various sources of inhibition on GC spike rate alone, or whether there is indeed something else "special" about the different sources of inhibition, such as their differential IPSC latencies following PP or GC spikes, their IPSC timing jitter or failure rate or their input location onto the GCs. Plots of average GC spike rate versus the area under the ∆R10) "The effect of facilitation on pattern separation is intuitive, since this allows the feedback circuit to integrate GC activity over time, and convert it to inhibition." Reviewers were not sure how intuitive this really is. True, integrating GC activity over time might be useful, but a) depression would allow a mathematical differentiation (or high-pass filtering) of GC activity over time, which arguably could be better than integrating over time for the purpose of separating temporal patterns, and b) the output of the inhibitory circuit is largely depressing anyway, providing the differentiation mentioned above. Thus I think the question remains: what is the purpose of the facilitation of the GC->BC synapse if the ultimate output will be depression at the BC->GC synapse anyway. Indeed, the authors found that the GC->BC facilitation only had a small effect on pattern separation. Essential revisions:1) "Feedback inhibition is recruited steeply with low dynamic range (0-4% of activate GCs)". This central conclusion is very interesting but not fully convincing.2+ imaging of the MFs near the stimulation site would provide a better dynamic range to make a quantitative estimate of% active MFs.In Figure 1, only 4% of GCs are activated by antidromic stimulation  which subsequently leads to the 4% ceiling shown in Figure 1K. But the critical parameter may not be how many GCs are stimulated but rather how many MFs are stimulated, since MFs that are unconnected to GCs likely contribute to feedback inhibition . Whether the low fraction of GCs responding to antidromic stimulation reflects low connectivity in the slice or inability to recruit MFs is unclear, but either way it raises a question about the robustness of this assay because this limitation dictates the upper limit for% active GCs. Reviewers wondered if CaThe reviewer is absolutely right that the critical parameter will be the number of (sufficiently conserved) mossy fibers stimulated, and that our imaging approach only captures a sample of the population (that within the imaging plane). Clearly, in the example from an individual slice , there are activated cells or axons outside the imaging plane. For this reason the average across slices , where 0.36% of GCs lead to 12% inhibition (50µA stim), is a more appropriate estimate. Particularly the observation, that the maximal population fraction imaged (~4%) precludes inferences about higher cell fractions, is well taken. We therefore only cautiously stated ‘a first quantitative estimate’. Indeed, these caveats were the reason why we proceeded to the optogenetic approach, which was not limited by the electrical stimulation technique in terms of how many GCs could be activated, and is not subject to the potential underestimation error pointed out by the reviewer.Concerning the reviewer’s suggestion to image MFs near the stimulation site, we are unsure if this would yield much better estimates. Firstly, any imaging plane would inevitably also only detect a sample of the activated mossy fibers. Secondly, the smaller size of axons, compared to somata, would likely imply substantially worse signal to noise. Thirdly, there would be the added complication that multiple imaged axon collaterals within our image plane might be counted as separate cells. Fourthly, counted active axons could be cut within stratum lucidum, i.e. prior to the hilus. Because of these caveats we reasoned that the number of active somata would in fact allow a reasonable estimate of the number of activated, well-conserved, individual mossy-fibers.+-imaging experiments, is in fact best addressed by the subsequent optogenetic experiments. These demonstrate clearly, that activating cell fractions >4% does not lead to the recruitment of substantial further inhibition.Therefore, we believe the reviewers concern, while absolutely valid in the context of the Ca²Figure 2. Reviewers appreciated that the authors use a 2nd method to estimate feedback inhibition wherein the full range of MF/GC activation can be assessed. Reviewers found it very surprising that the max IPSC evoked by focal and global Chr2 stimulation was similar , and that the range of max IPSCs was so variable (<0.1 to >0.8 nA). What is the fraction of hilar interneurons recruited by focal and global ChR2 activation? This might help explain this surprising result and the variability.For global activation, we know from Figure 2I that this stimulation reliably activates 100% of granule cells. Given the recruitment curve of feedback inhibition, it is extremely likely that this form of stimulation recruits 100% of interneurons within the feedback circuit to generate action potentials. Even so, global stimulation recruited IPSCs of variable sizes . We would ascribe this to either actual cell-to-cell variability concerning the amount of feedback inhibition received, or the number of interneuronal connections conserved within the slice (or a combination of both factors).For local activation, the spatial profile of inhibition  shows that a population of ‘local’ interneurons, which supply inhibition to adjacent GCs, must be independently excitable by local GC populations. Furthermore, since global GC activation does not increase inhibition beyond the locally elicited maximum , all interneurons supplying local inhibition must be already firing at their maximal rates with only local activation. The smaller amplitude of remotely activated inhibition  implies that a substantial fraction of the interneurons supplying inhibition to the patched GC remains inactive when a remote GC population is activated. A rough estimate of the overall fraction of interneurons recruited by local stimulation can be generated from Figure 3D-E as the average IPSC across all stimulus locations (around ~60% at saturated stimulation powers).The authors state  that the dynamic range is determined by the cellular connectivity patterns, but it is not clear how connectivity could generate this narrow dynamic range – please explain.The dynamic range of the feedback inhibitory microcircuit will be determined by i) synaptic weights and ii) the divergence/convergence of connections at all participating synapses as well as iii) the active and passive properties of all participating cells (their input-output functions). We have attempted to make this clearer in the manuscript . For instance, the lower bound of the dynamic range is defined by the number of GCs needed to first elicit interneuronal APs. The upper bound of the dynamic range is reached when all interneurons anatomically integrated into feedback inhibitory circuits no longer increase their firing rates upon further increased GC activity. In the most parsimonious view, this would be simply because convergent GC synapses are already driving the interneurons to their maximal instantaneous rates. Additionally considering the complex hilar circuitry , the upper bound could also be conceived of as a kind of dynamic equilibrium arising from all participating elements, where for instance maximal inhibition is dampened by disinhibitory interneurons to a degree proportional to the increasing excitation . Given these potential complexities , we deliberately focused on the resulting net inhibition of GCs. Regardless of how saturation emerges, it will always be a function of the convergence/divergence of synaptic inputs, the synaptic weights and the input-output functions of participating cells. Also note that, given the high number and dense packing of GCs, 0 to 4% is in fact not all that narrow: Assuming for instance 20000 to 30000 cells in the 350µm slice, 4% would correspond to 800 to 1200 GC.In measuring feedback IPSCs, the authors addressed voltage escape for dendritic IPSCs , but they did not address whether the ChR2-mediated conductance evoked by global light activation affects the measurement of the global max IPSC. In addition, why do the normalized curves not reach 100% ? Same question for all other figures where "IPSC [%]" is reported.We were also concerned about the possibility of ChR2 mediated shunting of inhibitory currents, which we cannot categorically exclude. However, the maximal IPSC amplitudes were not different between the electrical and optogenetic stimulations , which would be expected if there were significant effect of a ChR2 mediated shunting conductance.The normalized IPSC curves never reach 100% because for each cell IPSCs were normalized to that cells own maximum single IPSC . This was done throughout the entire manuscript to avoid defining ‘maximum’ based on other properties such as stimulation paradigm, stimulation power, location, etc. Following this normalization IPSCs were averaged over the condition of interest . Accordingly, all reported normalized IPSCs must by definition remain below 100% (as stated in the aforementioned subsection).2) Figure 3. The slow rise time of IPSCs seem inconsistent with PV interneurons that are thought to be the major source of feedback inhibition. Presumably the IPSCs are slowed by asynchrony/compound events which might be assessed by quantifying individual events rather than averages to infer PV involvement. Were any PV interneurons recruited in Figure 4? It seems important to address the contribution of PVs since they are critical interneuron subtype in the model. Based on reported uIPSC from PVs, can the authors estimate how many PVs might be contributing to feedback inhibition?The reviewer raises an interesting question. Unfortunately, our data do not allow us to make valid inferences about the contribution of different interneuron types, since even un-averaged GC IPSCs were compound events. As the reviewers surmise, this asynchrony likely caused the events to be slow. We did not examine in detail which interneurons are responsible for the properties of feedback inhibition, instead concentrating on a quantitative assessment of the input output properties of this circuit. In this sense, we consider the fact that our recorded IPSCs represented non-synchronous integrated events, reflecting the compound activity of various interneuron types biologically arising from GC activity, as an advantage of this experimental approach.A number of previous studies have, however, addressed the behavior and roles of individual cell-types (summarized in the manuscript in Supplementary file 1). Most pertinent to the present question is perhaps a study by , who performed exactly the kinetic comparison suggested by the reviewers, and conclude that SST-cells play the most important role in isolated feedback inhibition (through CaMKII-mediated activation of GCs). By contrast, less direct evidence from  suggest that PV cells likely also play a role, potentially due to concomitant perforant path input and along with additional interneuron types. However, the optogenetic circuit interrogation by Lee et al. suggests, that together PV and SST- cells still only account for ~50% of inhibition . This may be partially due to the expression patterns of the PV-, SST- and GAD65- mouse lines used, as is usually the case for such transgenic mouse lines.+ basket cells.We are not sure if the interneurons in Figure 4 contain PV interneurons, as we did no post-hoc stainings and used no selective Cre mouse line. However, the recorded cells were all clearly within the hilus (not on the GC border), and had maximal firing rates generally below 60Hz suggesting they did not represent PVWe do not believe the model inferences are critically dependent on the contribution of PV cells, since the experimentally matched properties concerned the spatio-temoral distribution of the arising net inhibition. Which interneurons are the source of this inhibition is, in our view, somewhat incidental (we simply chose a plausible yet parsimonious implementation with just 2 interneuron types for the model).Accordingly, the two modeled interneuron types could just as well be called local and global interneurons, where each would represent diverse population .3) The small number of active PP afferents used (24 of 400) in the input patterns meant that only about 10% of GCs were active even in the absence of inhibition . Just as the authors tested the robustness of their conclusions in the face of stronger feed-forward inhibition, it also seems important to assess the robustness of the conclusions across a range of excitatory drive that might represent EC activity under various conditions.in<1, 0.8x to 2x for 0.9< Rin<1).We now include an additional sensitivity analysis concerning PP-drive . Specifically, we have modified the PP-input weights from 0.6x to 2x leading to GC activity levels of up to 40% in the absence of inhibition. This analysis showed that the frequency dependence was robust over a range of PP-drives (0.8x to 1.6x for 0<R4) Subsection “Quantitative physiological properties of DG feedback inhibition”. The authors report that the range of active GCs that saturate feedback inhibition is in the range of active GCs reported in vivo. However, Pernia-Andrade and Jonas, 2014, Pilz et al., 2016, and Diamantaki et al., 2016, report higher fractions of active GCs in vivo, although reports using cFos labeling typically support the 1-4% range. The authors should discuss more explicitly the literature about GC activity in vivo and the interpretations of their small dynamic range of feedback inhibition in light of the possibility that GC activity might not be as sparse as suggested by cFos.The reviewer points out the important distinction between active cell-fractions that have been reported in the literature. This point is well taken and relies, in our opinion, on the time window in which activity is taken into account. In the electrophysiological experiments cited by the reviewers, cells were defined as active if action potentials were observed in time intervals of >15 minutes  or > 60 seconds . However, GCs firing seconds apart will cooperate neither in recruiting inhibition nor in activating CA3-cells. To recruit feedback inhibition, granule cell populations must be concurrently active within a short time window. An electrophysiologically determined firing rate over longer periods can be converted to an active cell fraction within shorter time-windows. If we recalculate the fraction of active GCs in this manner for short time windows of 20 ms (i.e. a gamma cycle) from Pernia Andrade and Jonas, 2014, and Diamantaki et al., 2016, the fraction of active GCs would be 0.4 and 0.028%, respectively .2+-imaging such as  typically report even lower rates with less than 1 transient per minute, though they generally acknowledge that the measured activity may represent only GC bursts.Studies relying on CaFinally, the immediate early gene studies report activity integrated over minute long sessions of animal behavior, but likely only capture a small subpopulation of GCs undergoing certain types of plasticity (which likely depend on the specific IEG under investigation).Accordingly, we believe that the dynamic range we find for coactive cells within ~20ms time-windows is consistent with the literature. In fact, our reading of the rate estimates using these different approaches would suggest that physiological GC activity rarely exceeds 1 or 2% for theta or gamma windows.We have attempted to incorporate a more explicit, but still brief discussion of this into the manuscript . If the reviewer thinks this should be discussed in more detail, we could dedicate a section of the Discussion to the various sources of over- and underestimation errors for the various techniques mentioned above.5) The nature of the facilitating inhibition remains unclear. Fast-spiking basket cells in the hippocampus usually show paired pulse or multiple pulse depression and for only few interneuron types paired pulse or multiple pulse facilitation has been observed. Whole-cell recordings from GCs during optogenetic stimulation of the different types of DG-interneurons might help to dissect the nature of the facilitating inhibitory inputs (e.g. Somatostatin-expressing cells).We are not sure if we have conveyed the point we wanted to make well. Unequivocal evidence from dual patch-clamp recordings, which we compiled in Supplementary file 1), has demonstrated that the inhibitory synapses from various interneuron types  to GCs are depressing . We have shown that inhibition in the – at least disynaptic – hilar feedback circuit facilitates. We were puzzled by this phenomenon and reasoned that there has to be a facilitating excitatory synapse driving feedback inhibition. Astonishingly, we could not find direct evidence for this in the literature (see Supplementary file 1). Indeed, we found that the MF output that drives feedback interneurons strongly facilitates. We have tried to make this clearer in the revised manuscript (see the aforementioned paragraph).6) GCs also contact Mossy cells (MCs), which in turn recruit DG interneurons and thereby inhibit GCs. It remained unclear why the MC feedback excitation of hilar interneurons was removed from the network model. It is an important functional element of this circuitry, which provides inhibition to GCs.We agree with the reviewer, that mossy cells are an important functional element of this circuitry. This seems to be a misunderstanding. We did not remove the MC feedback excitation of hilar interneurons from the network, but the direct MC excitation of GCs. This was done, because within a hippocampal lamella this connection has been shown to be extremely weak . For instance, Scharfman, 1995, found MC to GC excitation only in 20 out of 1316 cell pairs and only when inhibition was fully blocked.7) Subsection “Input-output relation of the feedback inhibitory microcircuit”, second paragraph and Figure 1—figure supplement 1 legend – Is it correct that the detection threshold used (0.94%) leads to a "true positive rate of 3%"? That seems very low, and implies that 97% of true responses were not detected. Unless this is a typo, is this not a serious problem that implies that the estimates of the active fraction of GCs are extreme underestimates?Thank you for this comment. We have corrected this misleading wording. The detection threshold would lead to equal numbers of false positives and false negatives if the ‘actually active fraction of GCs is 3%’. This implies that in this case, the number of false positives and false negatives would cancel each other out.8) Frequency-dependence of facilitation – Please state explicitly whether there was a "frequency tuning"  or whether all frequencies {greater than or equal to} 10 Hz displayed the same facilitation ratio (greater than 1).The facilitation indices significantly increased with increasing stimulation frequency . We have included this in the manuscript .out curve, for the seven model families chosen, would be a first-pass at addressing these questions.9) Spike rates – Here, pattern separation was computed from Pearson's correlation, dot product and pattern overlap of population spike rate vectors, all of which are in general sensitive to absolute spike rates. Therefore, it is not surprising that some of the manipulations in the model  would enhance correlations by increasing GC spike rates, thus reducing pattern separation. It would be extremely useful to know if the different outcomes in pattern separation are driven mainly by the impact of the various sources of inhibition on GC spike rate alone, or whether there is indeed something else "special" about the different sources of inhibition, such as their differential IPSC latencies following PP or GC spikes, their IPSC timing jitter or failure rate or their input location onto the GCs. Plots of average GC spike rate versus the area under the ∆Rremoving various sources of inhibition are driven by changes in GC spike rates, which we showed in Figure 6—figure supplement 1C, D). These data confirm the standard account, whereby inhibitory circuits support pattern separation simply by increasing GC sparsity. We have attempted to make this more clear in the manuscript .Thank you for this valuable comment. Indeed, our modeling data clearly shows that more is going on than pattern separation through changes in total inhibition and we have not made this sufficiently clear. Firstly, the reviewer is absolutely right in assuming that the main differences in PS when The more interesting and novel finding is that this effect is frequency dependent. Namely, given the exact same mean PP input drive, and the exact same inhibitory circuit, spike modulation at 30Hz leads to significantly lower GC-spiking and better pattern-separation . Remarkably, this decreased GC spiking is achieved with lower interneuron activity rates in 30Hz vs. 10Hz for both BCs and HCs. We now included a new supplementary figure showing the mean active cell fractions and firing rates for each cell type . Accordingly, the 30Hz modulation more efficiently converts interneuron spiking to GC depression than the 10Hz modulation. Our analysis  showed that this frequency dependent effect is mediated by the feedback circuit but not the feedforward circuit, implying that it is not simply the amount of inhibition but rather the timing, as the reviewer suspected.in>0.9; Figure 6G, arrows; subsection “Quantitative properties of the feedback circuit predict frequency dependent pattern separation”, fourth paragraph). Here GC sparsity is by definition constant, since the R-values are computed on the exact same model runs.Furthermore, the most prominent frequency-dependent boosting of feedback inhibitory (but not feedforward inhibitory) pattern separation, is seen for the patterns with the highest similarity . Firstly, these data demonstrate that the frequency dependent effect is robust over the range of PP-drives leading to plausible GC activity levels . They also suggest that the degree of pattern-separation observed is mostly a function of the achieved GC sparsity when effects are investigated over all input similarities 30Hz data are consistently shifted towards more sparsity and better pattern separation, but they appear to be roughly on the same line. I.e. decreasing PP-drive until a network at 10Hz shows the same sparsity as it would at 30Hz leads to the same approximate degree of pattern separation. However, for highly similar input patterns , this was not the case, with 30Hz data showing better pattern separation even if PP-drive is reduced to achieve the same sparsity .All this clearly demonstrates that more is going on than a simple regulation of sparsity through changes in total inhibition. We have attempted to make this clearer in the manuscript. We are not sure which seven model families the reviewer is referring to. There are three basic model families with associated spike rates . The ‘no feedback’ and ‘no inhibition’ data are computed from these three models (e.g. ‘no feedback’ shows the difference between ‘full model’ and ‘no feedback’). The other two model families  seem of limited interest here, since they showed no major effects.10) "The effect of facilitation on pattern separation is intuitive, since this allows the feedback circuit to integrate GC activity over time, and convert it to inhibition." Reviewers were not sure how intuitive this really is. True, integrating GC activity over time might be useful, but a) depression would allow a mathematical differentiation (or high-pass filtering) of GC activity over time, which arguably could be better than integrating over time for the purpose of separating temporal patterns, and b) the output of the inhibitory circuit is largely depressing anyway, providing the differentiation mentioned above. Thus I think the question remains: what is the purpose of the facilitation of the GC->BC synapse if the ultimate output will be depression at the BC->GC synapse anyway. Indeed, the authors found that the GC->BC facilitation only had a small effect on pattern separation.This is an interesting point. It is true that, depending on the definition of pattern separation  the effects of facilitation would differ. Given that we did observe pronounced facilitation we can only speculate as to its purpose. We do believe that facilitation may aid sparse GC spiking to recruit inhibition. Indeed, the view that a subsequent depressing synapse negates a previous facilitating one is problematic in sparsely firing networks, since the facilitation may be necessary to bring interneurons to threshold in the first place. The reviewer is however correct in noting that the contribution of facilitation to pattern separation was relatively small in our simulations. We have changed the Discussion to a more cautious interpretation .


In this article, we describe a novel technique using external fixators and cannulated screws to construct a 3-dimensional navigation drill guide to predict the screw trajectory before screw insertion that can prevent screw collision during arthroscopic ankle arthrodesis. Four orthopedic residents who had no prior experience of ankle arthrodesis were instructed on how to use the 3-dimensional navigation drill guide and where to insert the screws for ankle arthrodesis. Each resident inserted 6.5 cannulated screws on 8 sawbone ankle models using the device and the C-arm fluoroscopy. An experienced attending surgeon also inserted the same screws on 2 sawbone ankle models to find out if there is any difference between the experienced and inexperienced surgeons. All four residents and an attending surgeon did not experience any collision of screws for the three cannulated screws. Notably, one resident had collision of the 4th screw on his first sawbone model. On the second saw bone model, all surgeons could insert 5 screws without redrilling. A 3-dimensional navigation drill guide constructed with external fixators can assist surgeons in implementing percutaneous screws for arthroscopic ankle arthrodesis. Traditionally, ankle arthrodesis has been the preferred surgical method to treat end-stage ankle arthritis , 2. ReceWhen the drill bit with the large diameter collides with the screws inside the talus, it may not be possible to advance the drill bit across the screws. Giving up the drilled hole and attempting a new trajectory will leave a large hole which may decrease the stability of the ankle arthrodesis. Using cannulated screws and guide wires cannot be a solution as the guide wire is much smaller in diameter; it has the limitation of predicting screw collisions inside the talus. When the screw trajectory can be predicted and analyzed prior to the screw insertion, it will help surgeons to prevent the drilling of unused holes and save operation time and lower radiation exposure under C-arm fluoroscopy. In this article, we describe a novel technique using external fixators and cannulated screws to construct a 3-dimensional navigation drill guide to predict the screw trajectory before screw insertion that can prevent screw collision.Prior to the surgery, a 3-dimensional navigation drill guide is constructed using external fixators used for the fixation of wrist fracture, 5.0 cannulated screws, and large diameter S-pins .The two large diameter S-pins are used to form the main frame of the device. The external fixators are connected to the S-pins by the pin clamps where they aid in guiding during the screw fixation. Cannulated screws attached to the external fixators by the pin clamps are used as the drill guide where guide pins can be inserted through the cannulated hole of the screws . Joints The patient is placed in the supine position. After the thorough removal of the articular cartilage and abrasion of the underlying subchondral bone surface by the arthroscopic technique, all the instruments and the arthroscope are removed to prepare for screw fixation. The 3-dimensional navigation drill guide is positioned on the ankle joint where the lower-end of the medial S-pin will sit on the medial corner of the ankle joint . Under CThis simulation can show whether the two screws will collide inside the talus. When the collision is expected, the trajectory of the second screw can be adjusted. The device is positioned on the ankle again using the same method used previously, and the second guide pin is inserted through the second cannulated guide screw which will not collide with the first screw . After rFour orthopedic residents who had no prior experience of ankle arthrodesis were instructed by an experienced surgeon on how to use the 3-dimensional navigation drill guide and where to insert the screws for ankle arthrodesis. Each resident inserted 6.5 cannulated screws on 8 sawbone ankle models using the device and the C-arm fluoroscopy. The number of screws inserted without redrilling was recorded. When 5 screws could be inserted, it was regarded as successful and no further screws were inserted. An experienced attending surgeon also inserted the same screws on 2 sawbone ankle models to find out if there is any difference between the experienced and inexperienced surgeons.All four residents and an attending surgeon did not experience any collision of screws for the three cannulated screws. Notably, one resident had collision of the 4th screw on his first sawbone model. However, the same collision on the second sawbone model did not occur . On the The advantage of arthroscopic ankle arthrodesis over open arthrodesis is that it is minimal invasive and protects the periarticular blood supply to enhance the fusion process and facilitates faster repair. However, more rigid fixation methods such as plates cannot be implemented as it does not open the ankle joint , 19. ConFour novice surgeons who had no prior experience of ankle arthrodesis could insert 3 screws without redrilling using this device on their first trial sawbone model and could insert 5 screws into the second sawbone model. However, we acknowledge that the clinical setting in real patients is quite different from the sawbone model trial. When compared to the sawbone model trial, the patients are in a supine position. Moreover, it will not be easy to place the ankle in the best position for screw insertion. Also, anatomical landmarks are exposed in the sawbone models, which are not seen in the arthroscopic ankle arthrodesis. However, we could implement this device in the patients undergoing arthroscopic ankle arthrodesis with successful insertion of 5 screws . FurtherA 3-dimensional navigation drill guide constructed with external fixators can assist surgeons in implementing percutaneous screws for arthroscopic ankle arthrodesis.


Renshaw cells mediate recurrent inhibition between motoneurons within the spinal cord. The function of this circuit is not clear; we previously suggested based on computational modeling that it may cancel oscillations in muscle activity around 10 Hz, thereby reducing physiological tremor. Such tremor is especially problematic for dexterous hand movements, yet knowledge of recurrent inhibitory function is sparse for the control of the primate upper limb, where no direct measurements have been made to date. In this study, we made intracellular penetrations into 89 motoneurons in the cervical enlargement of four terminally anesthetized female macaque monkeys, and recorded recurrent IPSPs in response to antidromic stimulation of motor axons. Recurrent inhibition was strongest to motoneurons innervating shoulder muscles and elbow extensors, weak to wrist and digit extensors, and almost absent to the intrinsic muscles of the hand. Recurrent inhibitory connections often spanned joints, for example from motoneurons innervating wrist and digit muscles to those controlling the shoulder and elbow. Wrist and digit flexor motoneurons sometimes inhibited the corresponding extensors, and vice versa. This complex connectivity presumably reflects the flexible usage of the primate upper limb. Using trains of stimuli to motor nerves timed as a Poisson process and coherence analysis, we also examined the temporal properties of recurrent inhibition. The recurrent feedback loop effectively carried frequencies up to 100 Hz, with a coherence peak around 20 Hz. The coherence phase validated predictions from our previous computational model, supporting the idea that recurrent inhibition may function to reduce tremor.SIGNIFICANCE STATEMENT We present the first direct measurements of recurrent inhibition in primate upper limb motoneurons, revealing that it is more flexibly organized than previous observations in cat. Recurrent inhibitory connections were relatively common between motoneurons controlling muscles that act at different joints, and between flexors and extensors. As in the cat, connections were minimal for motoneurons innervating the most distal intrinsic hand muscles. Empirical data are consistent with previous modeling: temporal properties of the recurrent inhibitory feedback loop are compatible with a role in reducing physiological tremor by suppressing oscillations around 10 Hz. The activation of Renshaw cells by intraspinal branches of motoneuron axons, and the subsequent inhibition of motoneurons by Renshaw cells, have been a well-established feature of the spinal circuitry for almost 70 years . The preGiven that the neural populations involved cannot be measured experimentally simultaneously and in action, the problem has been previously approached through computational modeling of the system. The results of models are conflicting; some agree with the view that recurrent inhibition decreases synchronous motoneuron firing , but othEarly experimental studies on this circuit focused on recurrent inhibition in the cat hindlimb, which plays an obligatory role in locomotion and other stereotyped behaviors such as postural set and scratching. The distribution of recurrent inhibition has also been described in the cat forelimb, and generally resembles that in the hindlimb . In thisFirst, the distribution of recurrent inhibition has not been directly studied in primate upper limb motoneurons to date. Indirect studies in man, primarily through conditioning of stretch reflexes, report patterns of recurrent inhibition resembling those seen in cats , but theSecond, we have examined temporal characteristics of the recurrent inhibitory pathway directly by delivering trains of stimuli with a Poisson distribution, while measuring the responses in intracellular recordings from motoneurons. This provides empirical data which validate and support the function of recurrent inhibition suggested by our previous biophysically-based model .Macaca mulatta monkeys . All experiments were performed under the authority of appropriate licenses issued by the United Kingdom Home Office, and were approved by the Animal Welfare and Ethical Review Board of Newcastle University.The study was performed on four terminally-anesthetized female 2) and intravenous infusion of alfentanil (10–15 µg/kg/h). The bladder was catheterized to allow urine drainage. An endotracheal tube was placed via a tracheotomy, and positive pressure ventilation began. Central arterial and venous cannulae were inserted via the neck vessels, to allow continuous blood pressure monitoring. Nerve cuff electrodes were placed in the left upper limb on the radial nerve in the axilla, the musculocutaneous nerve, the ulnar and median nerves in the arm, the deep radial nerve at the elbow, and the median and ulnar nerves at the wrist. A laminectomy was made to expose spinal segments C4-T2. Spinal clamps were placed on thoracic and lumbar vertebrae, and the head was mounted in a stereotaxic frame, angled to produce ∼60° neck flexion. Under an operating microscope, the spinal dura was opened and the dorsal roots severed, rootlet by rootlet, from C4-T2 to prevent afferent input from reaching the cord.Animals were sedated with ketamine , general anesthesia was induced by propofol (5–10 mg/kg) and then continued by inhalation of sevoflurane  with alfentanil (10–32 µg/kg/h), since we have found that this yields better neural excitability than inhalational anesthetics such as sevoflurane. The respiratory gas supply was changed to a mixture of air and oxygen , to avoid potential problems with oxygen toxicity caused by breathing pure oxygen for long periods. The nerve cuffs were stimulated in turn , and the motor threshold determined. Subsequent stimulation used 3× motor threshold. Neuromuscular blockade was then initiated .Anesthetic monitoring included heart rate, oxygen saturation, arterial and venous blood pressure, capnography, and core and peripheral temperature. The animal was warmed by a thermostatic heating blanket, and a supply of warmed air. Intravenous fluids were given . Slowly rising trends in heart rate or blood pressure, or rapid changes following a noxious stimulus, were taken as evidence of waning anesthesia; supplemental drug doses were then given and/or infusion rates adjusted as appropriate.m potassium acetate solution), driven with a piezoelectric micromanipulator  and connected to a bridge amplifier . The location of the spinal surface was estimated from the change in potential on contact, although this can be subject to error because of fluid accumulation on the cord. The electrode was advanced while stimulating through one of the nerve cuff electrodes, chosen to produce the largest extracellular antidromic field in the microelectrode recording. Intracellular penetration into a motoneuron was indicated by a sudden drop in potential, often associated with spontaneous spiking. Motoneurons were identified by antidromic spikes following nerve stimulation, and were assigned to the following muscle groups based on the most distal nerve to which they responded: intrinsic hand (median or ulnar at wrist), forearm flexor , forearm extensor , arm extensor . No motoneurons were recorded which responded antidromically to the musculocutaneous nerve. In addition, several cells were encountered which had all the properties of motoneurons based on our past experience , but which did not respond antidromically to any of the implanted nerve cuffs. We classified these as unidentified motoneurons; they were usually recorded deep to motoneurons innervating wrist or elbow muscles, and are most likely to have innervated muscles acting around the shoulder and pectoral girdle because the most ventral parts of the ventral horn of the C7-T1 segments contain many motoneurons projecting to the latissimus dorsi and pectoral muscles , which provided epidural volley recording. Penetrations were made into the cord through the loop with glass micropipette electrodes  using a 1401 laboratory interface and Spike2 software (both Cambridge Electronic Design).At the end of the experiment, the animals were killed by overdose of anesthetic.in vivo inevitably lead to some leak currents and somatic depolarization away from the reversal potential for inhibitory synapses. Synapses from Renshaw cells are primarily on motoneuron dendrites; there is controversy in the literature on the number of synaptic contacts per cell: in mouse lumbar cord, a large number of synaptic contacts per cell often leads to large unitary IPSPs . Averages were compiled of the intracellular records relative to stimulation of a given nerve. The amplitude of IPSPs was measured from onset to peak; any responses smaller than a threshold of 50 µV were discounted as unreliable. It remains possible that this criterion may have caused some connections to be missed, but we think this is unlikely to influence our results substantially. IPSPs recorded close to the resting membrane potential would be small, but sharp electrode intracellular recordings made ry IPSPs , but a rry IPSPs , implyinThe AHP duration was estimated as the time after the spike when the membrane potential returned to half of its prespike level.14 = 16,384 points), which were processed with fast Fourier transforms to yield coherence and phase (frequency resolution 1.53 Hz). When computing coherence with a stimulus, contamination by the stimulus artifact can have an important effect on the measurement of phase  as it has improved power but preserves a false discovery rate of 5%. Accordingly, in the text we cite uncorrected p values, and indicate whether these were significant or not.Recurrent inhibition was quantified as the amplitude , incidence (percentage of motoneurons with an IPSP), and the product of these two measures (amplitude × incidence). For display purposes, error bars were calculated on these values using a Monte Carlo resampling (bootstrapping) procedure to estimate 95% confidence intervals . Values parisons . This prWe report here intracellular recordings from 89 upper limb motoneurons in four monkeys, each tested for recurrent inhibition from multiple upper limb nerves, a total of 630 motoneuron-nerve pairs. Motoneurons were encountered between 2.1 and 4.2 mm below the estimated spinal cord surface.A), there was substantial convergence, with IPSPs generated from the deep radial nerve, and the median and ulnar nerves at the arm. This motoneuron could not be antidromically identified from any of the implanted nerves. The second motoneuron was activated from the deep radial nerve , indicating that it projected to forearm extensor muscles; this received a recurrent IPSP from forearm flexor motoneurons . The third cell was activated from the median nerve at the arm , this forearm flexor motoneuron received recurrent inhibition from motoneurons innervating forearm extensors .Fig. 1A,C), but were not examined further.In our data, we did not see reliable short latency EPSPs following antidromic activation of motor axons as seen in the cat lumbar cord by Fig. 1p = 0.0001, binomial test). IPSPs were seen in fewer motoneurons projecting in the deep radial nerve, and in more unidentified motoneurons, but these trends were not significant after correction for multiple comparisons (p = 0.042 and 0.017 respectively).A–C shows the results of applying this approach to recurrent IPSPs in motoneurons projecting to different muscle groups. The general trend for mean IPSP amplitude (A) to be greater in the motoneurons in which recurrent IPSPs were most common (B) resulted in the of amplitude × incidence showing large differences (C). Recurrent inhibition was greatest in the motoneurons likely to project most proximally , to elbow extensors  or to wrist and digit flexors in the forearm (median nerve at the arm). Conversely, the amplitude of the single recurrent IPSP seen in an intrinsic hand muscle motoneuron was small. A Monte Carlo procedure was used to compare the amplitude × incidence values to those expected on the null hypothesis that they were similarly distributed across motoneuron categories. Recurrent inhibition was stronger for unidentified motoneurons, and weaker for intrinsic hand motoneurons, than expected by chance (p < 0.05 corrected for multiple comparisons).To quantify these effects further, we used three measures , based oFigure 3mplitude A to be gt common B resulteferences C. RecurrD–F shows the same approach applied to recurrent IPSPs evoked from different sources, in any motoneuron type. The nerves that evoked recurrent IPSPs of the largest mean amplitude  were also the nerves that most frequently evoked recurrent IPSPs . Stimulation of the ulnar nerve at the wrist failed to evoke recurrent IPSPs in any motoneurons, and the median nerve at the wrist evoked IPSPs in only one (which innervated forearm flexor muscles). Simulation of the musculocutaneous nerve and the ulnar nerve at the arm evoked quite large recurrent IPSPs, but in a relatively low proportion of motoneurons tested, suggesting a finer grained fractionation in the distribution of recurrent inhibition. Strikingly, stimulation of the radial nerve at the axilla and deep radial at the elbow evoked IPSPs in around 40% of the sampled motoneurons. This is a particularly high fraction, in the context of the rarity of recurrent IPSPs in some motoneuron types. Statistical comparison of the amplitude × incidence values indicated that the radial nerve in the arm and deep radial nerves generated significantly stronger recurrent inhibition than the whole population, whereas the musculocutaneous, ulnar nerve at the arm, and ulnar and median nerves at the wrist all generated significantly weaker recurrent inhibition (p < 0.05 corrected for multiple comparisons).Figure 3The distribution of recurrent inhibition is explored further in Some surprising and unexpected features emerge. First, the relatively large amplitude but infrequent recurrent IPSPs evoked by stimulation of the musculocutaneous nerve were evoked primarily in motoneurons antidromically activated from the radial nerve in the axilla, but not at the elbow, the great proportion of which are likely to be triceps motoneurons. Thus, triceps motoneurons, which are primarily elbow extensors, receive recurrent inhibition from their antagonist flexor muscles . A similar arrangement can be seen for forearm muscles: the deep radial nerve, which innervates wrist and long digit extensor muscles, frequently evoked recurrent IPSPs in forearm flexor motoneurons that innervate wrist and long digit flexor muscles, although these were generally smaller in amplitude than those in more proximal muscle motoneurons.Second, Some features of the distribution do resemble the patterns previous reported in the cat: the intrinsic hand muscles rarely evoked recurrent IPSPs , and rarely received recurrent inhibition . This resembles the pattern seen in both cat hind and forelimb . Second,A consistent finding in early reports in the cat was that the amplitudes of recurrent IPSPs are larger in motoneurons innervating slow twitch than fast twitch motor units . This leA presents the distribution of AHP durations  for motoneurons in different categories. There was no significant difference between mean AHP durations between the four groups illustrated  = 0.29, p = 0.84), nor between their variances  = 0.71, p = 0.55). Using Pearson's correlation coefficient, there was a significant but weak relationship between AHP duration and amplitude of recurrent IPSPs . However, this may be excessively influenced by large outlier values. Using the non-parametric Spearman's rank correlation coefficient , the correlation was not significant .Figure 5A shows coherence calculated between a single example unidentified motoneuron and stimuli given to the radial nerve at the arm. Coherence was small at low frequencies, but rapidly rose to a peak at ∼30 Hz, then gradually declined, remaining clearly above significance even up to 100 Hz; it was significantly different from zero for all frequencies shown (significance limit indicated by dotted line). The coherence phase (B) was high at the lowest frequencies, fell steeply until around 20 Hz, after which the phase-frequency relationship was approximately linear. A linear phase-frequency relationship is compatible with a simple delay; the slope can provide an estimate of this delay  showed a similar profile to the example cell of Figure 6B.Figure 6E presents the coherence spectrum between the stimulus train and motoneuron membrane potential for these simulations. The different colored traces show results for different EPSP conductance rise times . These are the same rise times and plotted with the same color code as used in Figure 6C, the coherence for the simulated data declined above 20 Hz. However, in the simulation coherence remained high for frequencies below 20 Hz, whereas in our experimental measurements, coherence fell below 20 Hz.We have previously used a computational model to investigate how recurrent inhibition could modify oscillations (and thereby synchrony) in motoneurons . To provFigure 6D, superimposed on the phase estimated from the experimental data. The model with the briefest Renshaw cell EPSP (green line) matched the experimental data most closely.The coherence phase calculated from these simulations is shown in Figure 6F accordingly presents a circular histogram of the coherence phase values measured from the 47 experimentally-recorded motoneuron-nerve pairs between 8 and 12 Hz. The phases were tightly clustered, with a mean of 2.12 radians. This compared with 1.48, 1.80, and 2.21 radians for the same phase measurements from the simulations with brief, medium, and long duration EPSPs, respectively . Our experimental data thus match the computational model with the shortest duration EPSPs very well.In our previous paper using this model, we concluded that recurrent inhibition acted to reduce low-frequency motoneuron firing synchrony contributing to physiological tremor in the 8- to 12-Hz band. The phase behavior is thus of especial interest in this frequency range. Figure 6A. We first generated an average of motoneuron membrane potential, triggered by stimuli which have been selected to have a preceding stimulus in a 5-ms window . A prediction of the response to the conditioning stimuli was then generated, based on the average compiled from all stimuli, suitably time shifted . This was subtracted from the conditional average, yielding the measured response to the second stimulus .Coherence is a linear analytical technique. Several known features of the recurrent inhibition circuit would be expected to introduce non-linearities; examples would be the spiking threshold of the Renshaw cells and shunting inhibition at the motoneuron membrane. Paired-pulse facilitation or depression between successive activations of a synapse represent another form of non-linearity. Non-linear summation of IPSPs was investigated in our experimental dataset by constructing conditional averages; the basis of this analysis is illustrated in Figure 7B shows example corrected conditional averages compiled in this way, for different conditioning windows as illustrated (black traces). Overlain is the average formed from all stimuli (red line) for comparison. If IPSPs sum linearly, these traces should be identical. It is apparent that this was not the case; the IPSP produced by a stimulus which occurred 0–5 ms after a preceding stimulus was greatly reduced, and this paired-pulse suppression persisted even for intervals of 20–25 ms. Figure 7C plots the IPSP amplitude as a function of the interstimulus interval. For this cell, the IPSP only recovered to the level seen in the unconditioned average for intervals around 30 ms.Figure 7D shows a similar conditioning curve, averaged over 14 motoneuron-nerve pairs where the IPSPs were sufficiently clear to be measured following the greatly reduced number of triggering stimuli available in a conditioned average. The IPSP was significantly reduced for intervals up to 15 ms.Figure 7E). To compile this plot, conditioning windows 100 ms wide have been used, with the center of the window placed 53–1000 ms before the triggering stimulus. To improve visualization, the abscissa has been plotted as frequency . IPSPs were significantly reduced if they occurred at rates between 5–7 Hz.Further investigation revealed that nonlinear IPSP summation also occurred at much longer intervals E. To comD) and low (E) frequencies. To illustrate the size of this effect, we took the IPSP estimated using an average of all stimuli for the cell illustrated in Figure 7B, red line, and convolved it with the Poisson train used to determine the stimulus timing. This provided a simulation of the membrane potential, as it would have appeared if IPSPs had summed linearly. The coherence between this signal and the stimulus is illustrated in Figure 7F, red line, overlain on the actual coherence seen between this motoneuron membrane potential and stimulation . The experimentally-measured coherence was lower than for the simulated signal at all frequencies, presumably reflecting membrane noise in the motoneuron which was unrelated to the stimulus. However, the curves diverge to a much greater extent at high and lower frequencies, with the highest coherence in the 10- to 30-Hz region.Non-linearities in IPSP summation will therefore reduce the ability of recurrent inhibition to transmit information at both high D and low and low E frequenThis study provides the first direct observation of recurrent inhibition in primate upper limb. Our findings confirm previous indirect observations in human  and direRecurrent inhibition is not ubiquitous; in our data, it was rarely evoked from or received by motoneurons of intrinsic hand muscles –4. This D–F). Second, recurrent inhibition frequently occurred across joints. Particularly noticeable were the large effects in unidentified motoneurons, situated deep to antidromically-activated cells. This location contains motoneurons projecting to proximal muscles including latissimus dorsi, pectoralis major and minor  was surprising.There were two striking features in our data. First, recurrent inhibition was seen between muscles classically viewed as antagonists: from elbow flexors to extensors and between the forearm wrist and digit flexors and extensors , 8D–F. S8nd minor  which mond minor E,F was sGeneral organizational principles of recurrent inhibition arose from extensive studies in cat hindlimb ; resultsE). We did not identify the specific muscles innervated further, so we cannot provide further fine-grained information. However, given the functional fractionation of actions in cats, the flexibility in how these muscles are used suggests an even more fractionated action in primates. Studies in human have suggested that muscles acting about the wrist may have a different organization of spinal interneurons, reflecting their ability to reconfigure agonist/antagonist coupling for both flexion/extension and ulnar/radial deviation movements . The best agreement was seen using the shortest duration EPSPs in Renshaw cells . In our previous work, tremor was reduced by 40%, 58%, and 72% using EPSPs with same fast, medium, and slow rise times as tested here. Our results therefore imply that tremor reduction is at the lower end of previous estimates.Our previous computational model was constructed using a range of reasonable values for parameters gleaned from recordings in cat. The present experiments have, for the first time, made direct phase-frequency measurements of the recurrent inhibitory circuit in primate upper limb motoneurons. Experimentally-measured coherence phases agreed closely with simulation D,F. The Importantly, note that the phase-frequency measurements made here cannot be used directly to infer effects on physiological tremor. Our protocol involved measuring motoneuron responses to strong stimulation of a whole motor nerve, causing synchronous activation of many motor axons. The motoneurons were at rest, so that synaptic inputs did not interact with intrinsic cell properties seen during repetitive firing such as the AHP. Instead, our approach was to simulate the same artificial situation as we had tested experimentally, and to confirm that the model yielded similar results. With the model thus validated, we can then have high confidence in our previous results which simulated the more natural situation of an active motoneuron pool responding to descending inputs.C). The same group also explored non-linearities in Renshaw cell responses (D) occurs at the level of the Renshaw cell. Paired pulse depression at short intervals led to reduced coherence above 30 Hz between the motoneuron membrane potential and stimulus train (F). This is however unlikely to have consequences for motor control, because slow motor unit twitch times already severely attenuate such high frequencies in the final muscle output , which substantially reduced coherence at these frequencies (F). Our computational model did not incorporate this effect so that, unlike the experimental measurements, coherence in the model remained high at low frequencies (E). It remains unclear what causes this low-frequency non-linearity. One possibility is that Renshaw cells inhibit other Renshaw cells (A2), and could plausibly generate nonlinear interactions at long intervals. However, coherence between motor axon stimulation and Renshaw cells does not fall below 10 Hz (hI, which is activated by hyperpolarization and found in adult motoneurons (hI current could reduce the amplitude of a subsequent IPSP in a time window possibly compatible with our observations. The motoneuron model of hI, possibly explaining why our simulations did not show reduced low-frequency coherence. Frequencies below 10 Hz contain most of the voluntary motor command (We also observed a nonlinear reduction in recurrent inhibition at frequencies <10 Hz E, which quencies F. Our coquencies E. It remaw cells . This leaw cells A2, and cow 10 Hz , suggestow 10 Hz . One canoneurons . If acti command . Limitin