Functional organization of the spinal locomotor network based on analysis of interneuronal activity

Functional organization of the spinal locomotor network based on analysis of interneuronal activity | bioRxiv /* */ /* */ <!-- <!-- /*! * yepnope1.5.4 * (c) WTFPL, GPLv2 */ (function(a,b,c){function d(a){return"[object Function]"==o.call(a)}function e(a){return"string"==typeof a}function f(){}function g(a){return!a||"loaded"==a||"complete"==a||"uninitialized"==a}function h(){var a=p.shift();q=1,a?a.t?m(function(){("c"==a.t?B.injectCss:B.injectJs)(a.s,0,a.a,a.x,a.e,1)},0):(a(),h()):q=0}function i(a,c,d,e,f,i,j){function k(b){if(!o&&g(l.readyState)&&(u.r=o=1,!q&&h(),l.onload=l.onreadystatechange=null,b)){"img"!=a&&m(function(){t.removeChild(l)},50);for(var d in y[c])y[c].hasOwnProperty(d)&&y[c][d].onload()}}var j=j||B.errorTimeout,l=b.createElement(a),o=0,r=0,u={t:d,s:c,e:f,a:i,x:j};1===y[c]&&(r=1,y[c]=[]),"object"==a?l.data=c:(l.src=c,l.type=a),l.width=l.height="0",l.onerror=l.onload=l.onreadystatechange=function(){k.call(this,r)},p.splice(e,0,u),"img"!=a&&(r||2===y[c]?(t.insertBefore(l,s?null:n),m(k,j)):y[c].push(l))}function j(a,b,c,d,f){return q=0,b=b||"j",e(a)?i("c"==b?v:u,a,b,this.i++,c,d,f):(p.splice(this.i++,0,a),1==p.length&&h()),this}function k(){var a=B;return a.loader={load:j,i:0},a}var l=b.documentElement,m=a.setTimeout,n=b.getElementsByTagName("script")[0],o={}.toString,p=[],q=0,r="MozAppearance"in l.style,s=r&&!!b.createRange().compareNode,t=s?l:n.parentNode,l=a.opera&&"[object Opera]"==o.call(a.opera),l=!!b.attachEvent&&!l,u=r?"object":l?"script":"img",v=l?"script":u,w=Array.isArray||function(a){return"[object Array]"==o.call(a)},x=[],y={},z={timeout:function(a,b){return b.length&&(a.timeout=b[0]),a}},A,B;B=function(a){function b(a){var a=a.split("!"),b=x.length,c=a.pop(),d=a.length,c={url:c,origUrl:c,prefixes:a},e,f,g;for(f=0;f<d;f++)g=a[f].split("="),(e=z[g.shift()])&&(c=e(c,g));for(f=0;f<b;f++)c=x[f](c);return c}function g(a,e,f,g,h){var i=b(a),j=i.autoCallback;i.url.split(".").pop().split("?").shift(),i.bypass||(e&&(e=d(e)?e:e[a]||e[g]||e[a.split("/").pop().split("?")[0]]),i.instead?i.instead(a,e,f,g,h):(y[i.url]?i.noexec=!0:y[i.url]=1,f.load(i.url,i.forceCSS||!i.forceJS&&"css"==i.url.split(".").pop().split("?").shift()?"c":c,i.noexec,i.attrs,i.timeout),(d(e)||d(j))&&f.load(function(){k(),e&&e(i.origUrl,h,g),j&&j(i.origUrl,h,g),y[i.url]=2})))}function h(a,b){function c(a,c){if(a){if(e(a))c||(j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}),g(a,j,b,0,h);else if(Object(a)===a)for(n in m=function(){var b=0,c;for(c in a)a.hasOwnProperty(c)&&b++;return b}(),a)a.hasOwnProperty(n)&&(!c&&!--m&&(d(j)?j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}:j[n]=function(a){return function(){var b=[].slice.call(arguments);a&&a.apply(this,b),l()}}(k[n])),g(a[n],j,b,n,h))}else!c&&l()}var h=!!a.test,i=a.load||a.both,j=a.callback||f,k=j,l=a.complete||f,m,n;c(h?a.yep:a.nope,!!i),i&&c(i)}var i,j,l=this.yepnope.loader;if(e(a))g(a,0,l,0);else if(w(a))for(i=0;i (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0];var j=d.createElement(s);var dl=l!='dataLayer'?'&l='+l:'';j.src='//www.googletagmanager.com/gtm.js?id='+i+dl;j.type='text/javascript';j.async=true;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-M677548'); Skip to main content Home About Submit ALERTS / RSS Search for this keyword Advanced Search New Results Functional organization of the spinal locomotor network based on analysis of interneuronal activity View ORCID Profile Pavel E. Musienko , View ORCID Profile Oleg V. Gorskii , View ORCID Profile Tatiana G. Deliagina , View ORCID Profile Pavel V. Zelenin doi: https://doi.org/10.1101/2025.05.06.652406 Pavel E. Musienko a Laboratory of Neuroprosthetics, Institute of Translational Biomedicine, St. Petersburg State University , 199034 St. Petersburg, Russia c Sirius National Technical University , 354340 Sochi, Russia d Federal Center for Brain and Neurotechnologies , 199330 Moscow, Russia Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Pavel E. Musienko Oleg V. Gorskii a Laboratory of Neuroprosthetics, Institute of Translational Biomedicine, St. Petersburg State University , 199034 St. Petersburg, Russia b Laboratory of motor and visceral functions neuromodulation, Pavlov Institute of Physiology , 199034 St. Petersburg, Russia f Life Improvement by Future Technologies Center , 143025 Moscow, Russia Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Oleg V. Gorskii Tatiana G. Deliagina e Department of Neuroscience, Karolinska Institute , SE-17177, Stockholm, Sweden Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Tatiana G. Deliagina For correspondence: Tatiana.Deliagina{at}ki.se Pavel V. Zelenin e Department of Neuroscience, Karolinska Institute , SE-17177, Stockholm, Sweden Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Pavel V. Zelenin Abstract Full Text Info/History Metrics Supplementary material Preview PDF Abstract Locomotion is a vital motor function for any leaving being. In vertebrates, a basic locomotor pattern is generated by the spinal locomotor network (SLN). Although SLN has been extensively studied, due to technical difficulties, most data were obtained during fictive locomotion, and data about activity of spinal neurons during locomotion with intact sensory feedback from limbs are extremely limited. Here, we overcame the technical problems and recorded activity of putative spinal interneurons from spinal segments L4-L6 during treadmill locomotion (with intact sensory feedback from limbs) evoked by stimulation of the mesencephalic locomotor region in the decerebrate cat. We analyzed activity phases of recorded interneurons, by using a new method that took into account the previously ignored information about stability of neuronal modulation in the sequential locomotor cycles. We suggested that neurons with stable modulation (i.e. small dispersion of their activity phase in sequential cycles) represent the core of SLN. Our analysis allowed to reveal functional groups of neurons with stable modulation presumably generating the vertical and horizontal components of the step, and to characterize their location in the spinal cord. Analysis of relationships between activity phases of these groups revealed possible connections between them, suggesting a novel model for generation of locomotion that combines reciprocally active half-centers with a ring consisting of four sequentially active groups, each inactivating the preceding one and activating the next one. 1 INTRODUCTION For most legged vertebrates, forward locomotion represents the main form of progression, although they can also perform backward, sideways and in place stepping. In all studied vertebrates, including humans, the basic pattern of stepping movements in any direction is generated by a neuronal network residing in the spinal cord (the spinal locomotor network, SLN). SLN includes a network capable of generating the basic locomotor pattern without the sensory feedback ( Brown, 1911 ) currently termed as “the central pattern generator”, CPG. However, normally this basic pattern is substantially modified by the activity of the sensory neurons that constitute an important part of SLN ( Rossignol et al., 2006 ). The descending commands from the brain turn SLN on and off, control the speed and gait of locomotion, and modify the basic pattern for steering, obstacle avoidance, etc. ( Orlovsky et al., 1999 ; Bouvier et al., 2015 ; Caggiano et al., 2018 ). Forward locomotion can be evoked, and its speed and gait can be controlled in the decerebrate preparation by stimulation of the mesencephalic locomotor region (MLR) of the brainstem ( Shik et al., 1966 ; Shik & Orlovsky, 1976 ). This experimental paradigm has been extensively used for investigation of SLN. The results of these experiments together with studies of the locomotor kinematics and the motor pattern in intact and spinal animals ( Halbertsma, 1983 ; Krouchev et al., 2006 ; Markin et al., 2012 ) have led to the development of computational models of SLN that include interneurons, motoneurons, limb dynamics and sensory feedback ( McCrea & Rybak, 2008 ; Rybak et al., 2015 ; Shevtsova et al., 2015 ; Markin et al., 2016 ). However, due to technical difficulties related to the recording of spinal neurons during locomotor movements, the experimental data related to operation of SLN during “real locomotion” that is locomotion with close to normal sensory feedback, are extremely limited ( Orlovsky & Feldman, 1972 ; Feldman & Orlovsky, 1975 ). Also, spinal interneurons were recorded during forward air stepping, when the movement-related sensory feedback was present but substantially different from the normal one ( AuYong et al., 2011 ). Most data related to activity of spinal interneurons were obtained during fictive locomotion in immobilized animals or in in vitro newborn mice preparations. Thus, activity of identified spinal interneurons (Renshaw cells and Ia interneurons) was recorded during fictive locomotion caused by stimulation of MLR ( Noga et al., 1987 ; Pratt & Jordan, 1987 ; Shefchyk et al., 1990 ). Since it was demonstrated that MLR is a center for initiation of forward locomotion only ( Musienko et al., 2012 ), most likely, these neurons were recorded during fictive forward locomotion. Also, activity of non-identified spinal interneurons was recorded during spontaneous episodes of fictive locomotion in paralyzed cats ( Baev et al., 1979 ) and in anesthetized rabbits ( Viala et al., 1991 ). In these studies, as well as in studies on in vitro preparations ( Zhong et al., 2012 ), the neuronal activity was recorded along with activity of motor nerves only to a pair of antagonistic muscles in each limb, and thus, it was impossible to determine if forward, backward or in place fictive locomotion was generated. Our earlier results suggest that SLNs generating forward and backward stepping differ to some extent ( Merkulyeva et al., 2018 ; Musienko et al., 2022 ). Thus, it is difficult to assess whether the models based on data obtained during fictive locomotion represent operation of SLN during real forward locomotion. The aim of the present study was to analyze operation of SLN during real forward locomotion with normal sensory feedback from limbs. For this purpose, we recorded activity of putative spinal interneurons during real locomotion evoked in decerebrate cat by MLR stimulation. We employed a new approach for analysis of locomotion-related activity of neurons. Our analysis allowed to reveal functional groups of neurons, to suggest functional connections between them, to characterize their distribution in the spinal cord and to propose a novel model of SLN for generation of forward locomotion. A brief account of this study was published in abstract form ( Zelenin et al., 2019 ). 2 MATERIALS AND METHODS 2.1 Animals Experiments were carried out on five adult cats of either sex (weighing 2.5-4.0 kg). The same animals were used in our previous study ( Musienko et al., 2020 ). All procedures were conducted in accordance with a protocol approved by the Animal Care Committee of the Pavlov Institute of Physiology, St. Petersburg, Russia (protocol #01a/2016), and followed the European Community Council Directive (2010/63EU) and the guidelines of the National Institute of Health Guide for the Care and Use of Laboratory Animals . 2.2 Surgical procedures The surgical procedures were the same as in our previous study ( Musienko et al., 2020 ). The cats were deeply anesthetized with isoflurane (2-4%) delivered in O 2 . The trachea was cannulated, and carotid arteries were ligated. The animals were decerebrated at the precollicular-postmammillar level. A laminectomy was performed in the lumbar area. Anesthesia was discontinued after the surgical procedures, and the experiments were started 2–3 h thereafter. During the experiment, the rectal temperature and mean blood pressure of the animal were continuously monitored and were kept at 37±0.5°C and >80 mmHg. 2.3 Experimental design The experimental design ( Figure 1A ) was similar to that used in the previous study ( Musienko et al., 2020 ). The head, the vertebral column, and the pelvis of the decerebrate cat were fixed in a rigid frame ( Figure 1A ). The forelimbs had no support, whereas the hindlimbs were positioned on a treadmill with two separate belts (left and right moving backward in relation to the animal at the same speed (0.5 m/s) and below referred to as the “treadmill belt”. The distance between the treadmill belt and the fixed pelvis was 21–25 cm (depending on the animal’s size), which determined a semi-flexed limb configuration in the middle of stance typical for walking. Download figure Open in new tab FIGURE 1 Recording of the activity of spinal neurons during MLR-evoked locomotion. (A ) Experimental design. MLR-stim, an electrode for stimulation of mesencephalic locomotor region. MEA, a microelectrode array for recording neuronal activity. Limb-L, a mechanical sensor recording anterior/posterior movements of the left hindlimb (a sensor recording the right hindlimb movements is not shown). FP, a force plate. (B-D) An example of 18 spinal neurons recorded simultaneously in L6 during locomotion. Red and green, neurons with stable and unstable modulation, respectively. Blue, non-modulated neurons. (B) Locations of the neurons. (C) Superimposed spikes of individual neurons extracted from the mass activity. Recording depths (D) are indicated. (D) Activity of individual neurons (##1-18) during locomotion recorded along with hindlimb movements (Limb-R, Limb-L) and contact forces (Force-R, Force-L). Swing phases of the left hindlimb are highlighted. Locomotion was evoked by electrical stimulation of the mesencephalic locomotor region (MLR) ( Shik et al., 1966 ; Shik & Orlovsky, 1976 ; Jordan, 1986 ; Garcia-Rill & Skinner, 1987a , b ). The stimulation started in 2–3 s after the onset of the treadmill belt motion. For MLR stimulation, a bipolar electrode (two 150 μm wires isolated except for the tips and separated by 0.5 mm) was inserted into the brainstem area (Horsley-Clarke coordinates P2, R/L4, H0) by means of a micromanipulator (MLR-stim in Figure 1A ). We used the following parameters of stimulation: frequency, 30 Hz; pulse duration, 0.5-1 ms; current, 50-200 µA. We recorded the anterior-posterior locomotor limb movements by means of two mechanical sensors (one of which, Limb-L, is shown in Figure 1A ), as well as the vertical forces developed by each of the limbs by means of two force plates positioned under the left and right parts of the moving belt (FP in Figure 1A ). 2.4 Electrophysiology Neurons were recorded extracellularly by means of commercially available multichannel electrode arrays (MEA in Figure 1A ). Each array consisted of a shaft with 32 Pt/Ir/Au electrode sites (A1x32-10mm-50-177; Neuronexus, Ann Arbor, MI). Each site surface area was 177 µm 2 . Electrode sites were distributed 50 µm apart vertically, and thus the recording length of the array was approximately 1.5 mm, which allowed for simultaneous recordings of many individual neurons from different areas of the gray matter ( Figure 1B ). The array was mounted on a custom electrode holder driven by a manual micromanipulator. We attempted to systematically explore the whole cross-section of the gray matter except for the motor nuclei areas. Activity of putative spinal interneurons during MLR-evoked locomotion was recorded along with ground reaction forces and signals from mechanical sensors in ∼20-60 locomotor cycles. Only episodes with stable well-coordinated locomotion (similar limb trajectories, stable forces, the right and left hindlimbs stepping in antiphase) were analyzed. For all analyzed episodes, the relative variability (SD/mean) of the cycle duration was <12%, the relative variability of the swing proportion <17%, the relative variability of the step length <20%, the right and left hindlimb movements were close to anti-phase (deviation of the inter-limb phase shift from 0.5 was <0.05). To increase the stability of recording that is to prevent displacement of the nervous tissue in relation to the recording electrodes caused by movements (locomotor, breathing, etc.), the space between the dura matter and walls of the spinal canal was filled by tissue adhesive glue Indermil X Fine (Henkel Ireland Operations and Research Ltd, Dublin, Ireland) in the area of recording. Signals from the electrode array (neuronal activity), from mechanical sensors and force plates were amplified, digitized with a sampling frequency of 30 kHz (neurons) and 1 kHz (sensors), and recorded on a computer disk using a data acquisition and analysis system (Power1401/Spike2, Cambridge Electronic Design, Cambridge, UK). The multiunit spike trains recorded by each electrode were separated into unitary waveforms representing the activity of individual neurons using the spike-sorting procedure in the same system. The spikes with the same waveform were supposedly generated by the same spinal neuron during locomotion ( Figure 1C ). Only neurons with a stable spike shape were used for analysis. Many neurons were recorded simultaneously by several neighboring sites, which increased the confidence of the spike sorting. An example of the activity of 18 neurons simultaneously recorded during locomotion, the waveforms of their spikes, and positions of the neurons on the cross-section of the gray matter are shown, respectively, in Figures 1 , D, C, and B. To exclude recording of the same neuron twice, medio-lateral distance between recording tracks was at least 0.2 mm (while the distance between recording sites that could pick up spikes of the same neuron was not more than 0.1 mm). 2.5 Analysis of activity of individual neurons Neurons with the mean firing frequency less than 1 Hz were considered inactive during locomotion ( Table 1 ). The activity of neurons was typically modulated in the rhythm of stepping movements ( Figure 1D ). To characterize this modulation, the phase histogram of neuronal activity in a step cycle of the ipsilateral limb was generated. Because of some variability in the duration and structure of step cycles within a test and between tests in different animals, we used dual-referent phase analysis ( Berkowitz & Stein, 1994 ), that is we normalized swing and stance phase separately. Step cycle durations were normalized to unity. Swing and stance phases were normalized, respectively, to 0.38 and 0.62 parts of the locomotor cycle that represent proportions of swing and stance in the locomotor cycle averaged across all episodes of locomotion in all cats (for details, see Supplementary Data, page 3). Such normalization ensured that neuronal activity during a definite phase (swing or stance) during one walking episode was compared to activity during the same phase in the other episodes, or when these characteristics were compared in different steps within the same episode. View this table: View inline View popup Download powerpoint TABLE 1 Descriptive statistics for the recorded population. Spinal segment , the segment in which neurons were recorded in a particular cat. The number of neurons as well as the percentage of neurons out of entire population of neurons recorded in an individual cat, or in a segment, or of the entire population of recorded neurons are indicated in parenthesis. Typically, neurons discharged only during a part of the step cycle producing one burst of activity. Some neurons had their bursts on top of the background tonic activity. To estimate the onset and offset phases of the neuron’s burst in the step cycle, the spike time sequence during one step ( Figure 2A ) was converted to the instantaneous rate versus time ( Figure 2B ) and then to instantaneous rate versus phase (1000 data points per normalized cycle; Figure 2C ). The resulting curve was approximated with the best two-level rectangular fit (shown by red lines in Figure 2C ; Zelenin et al., 2011 ). This fit provided the burst onset phase and offset phase expressed as a proportion of the normalized cycle ( Figure 2C ; for details, see Supplementary Data, page 4). The phases ( Figure 2D ) were averaged across all step cycles using the circular statistics methods ( Batschelet, 1981 ), providing the mean onset and offset phases, as well as the corresponding standard deviations, characterizing stability/variability of the phases ( Figure 2E ; for details, see Supplementary Data, page 5). Download figure Open in new tab FIGURE 2 Analysis of the activity of spinal neurons during MLR-evoked locomotion. (A-E) An example of analysis of activity (neuron #4 from Figures 1B-D ). For each individual step, a spike raster aligned at the swing start (A), the instantaneous firing rate vs time (B), the rate vs phase with dual-reference phase normalization (C), and the burst phase (D) were determined. Burst phases in individual steps were used to calculate the average and SD for burst on- and offset phases (E). We supposed that neurons with stable phase of activity have a larger contribution to control of locomotor movements. Stability of the phase is characterized by its SD : the smaller SD reflects the higher stability. We proposed one novelty in analysis: assigning more weight to the data with lower variability (smaller SD ). With this purpose, we converted SD s into “weights” by using a formula: w = 10 / (1+900 SD 2 ) (for details, see Supplementary Data, page 6). Thus, neurons with higher variability (that is neurons with unstable activity, and therefore larger SD ) had lower weight of their phase, and vice versa . To take into account both the onset stability and the offset stability of a neuron, we calculated the integral stability of the activity phase of the neuron during locomotion ( W ): W = ( w on ) 2 + ( w off ) 2 , where w on , w off were the weights of the onset and offset phases, respectively. Averaging of the instantaneous rate versus phase across all recorded steps produced the mean phase profile of the neuron’s activity. A neuron was considered modulated if the activity profiles in individual steps were similar to the average profile (the Pearson’s correlation coefficient was higher than 0.3 in more than 60% of the locomotor cycles). The other neurons, with more random bursts of activity, were considered non-modulated ( Table 1 ; blue neurons in Figures 1B-D ). Modulated neurons were further classified into two subpopulations: with stable modulation and with unstable modulation. We used two criteria for stability of modulation of the neuron. (i) SD of the burst onset and/or burst offset had to be not more than 0.1 part of the locomotor cycle. We accepted stability of only one edge of the burst since some of the recorded neurons had a ramp-up or ramp-down burst shape with stable burst offset and burst onset phase, respectively. (ii) The average coefficient of correlation between the profiles of the activity in individual locomotor cycles and in the entire activity histogram should not be less than 0.6. Modulation was considered stable if both criteria were satisfied. Examples of neurons with stable and unstable modulation (their locations, spike shapes, and activity) are shown in Figures 1B-D (red and green, respectively). It should be noted that we analyzed activity of neurons in relation to the locomotor cycle of the ipsilateral limb. However, we cannot exclude that some of the recorded neurons were commissural interneurons contributing to control of the contralateral limb. 2.6 Analysis of population distribution of burst onset and offset phases The population distribution of any measurable parameter (e.g., the burst offset phase measured in a set of neurons) is traditionally presented with a distribution histogram. We proposed a modified presentation that took into account not only the measured values but also the precision of these measurements. Specifically, each phase φ i measured with a precision SD i , was presented with the probability density p i (φ) : a Gaussian distribution with the mean value equal to φ i and the standard deviation equal to SD i . The average of such distributions across the entire population of neurons provided the probability density to observe a particular value of the measured parameter. Besides taking into account the precision of measurements, this procedure also smoothens the resulting curves. Analogous to the described above probability distribution of onset/offset phases, the weighted probability distribution of onset/offset phases was calculated. With this purpose the weighted averaging was applied to the distributions for individual neurons: Σ( w i p i (φ)) / Σ w i where w i was the weight function described in Matearials and Methods 2.5. For details of these procedures, see Supplementary Data, page 7. 2.7 Cluster analysis To reveal groups of neurons with similar activity phases, we used a modified version of the “nearest neighbor” method of cluster analysis similar to the method used by Krouchev et al. (2006) . For each neuron, its activity phases were presented as a scatterplot in which the burst offset phase of a given neuron was plotted vs its burst onset phase ( Krouchev et al., 2006 ) together with the onset and offset weights (Supplementary Figure 2A). We did not specify the number of clusters a priori . The clustering procedure consisted of three stages: (i) ranking neurons according to stability of their activity, (ii) primary clustering (“seeding”) that produced an initial set of “raw” clusters, (iii) “refining” of the ”raw” clusters. First, we ranked all neurons according to the integral stability of the activity phase of the neuron during locomotion ( W , described in Matearials and Methods 2.5). Then, for primary clustering, the point in the scatterplot corresponding to the first neuron in the ranked list (characterizing the onset and offset of the neuron’s burst) was considered as the first cluster. Then points of other neurons were one by one compared with already existing clusters. Closeness or remoteness of a point from the centers of the existing clusters was characterized by two parameters: Euclidian distance ( D ) and Z-distance ( Z ). If at least one of these distances exceeded the threshold level ( D >0.15 or Z >3) the point gave rise to a new cluster. If the point was close to one or several clusters ( D <0.15 and Z <3), it was merged with the cluster for which D was minimal. For this expanded cluster, its center was re-calculated. The center of the cluster - that is the average onset and offset phases for neurons composing the cluster - was calculated by using the weighted average for a phase of the onset (Φ on ) or offset (Φ off ): Φ = Σ( w i φ i ) / Σ w i , while the corresponding standard deviations were calculated with a formula: SD = Σ( w i SD i ) / Σ w i . The set of clusters obtained during the primary (“seeding”) clustering was used to re-sort the points with a “refining” procedure, similar to “seeding” but by using stricter similarity/closeness criteria: the point was considered far from the center of the cluster if D >0.1 or Z >3. In such a case it was considered as “assorted” (added to cluster 0). If the point was close to one or several clusters ( D <0.1 and Z <3), it was merged with the cluster for which D was minimal. This resorting was repeatedly applied to the entire ranked list of neurons until convergence to the final set of Clusters . In addition, for each cluster the midburst phase Φ mid was calculated as Φ mid = (Φ on + Φ off ) / 2. Detailed description of the clustering analysis is presented in Supplementary Data (pages 8,9) and Supplementary Figures 2,3. 2.8 Statistical analyses All quantitative data in this study are presented as mean± SD . To evaluate the statistical significance of difference in percentages of different Clusters of modulated neurons recorded in L4 and in L6, we used Fisher’s exact test; the significance level was set at P = 0.05. 2.9 Histological procedures At termination of the experiments, cats were deeply anesthetized with isoflurane (5%) and then perfused with isotonic saline, followed by a 10% formalin solution. Frozen spinal cord sections of 50 µm thickness were cut in the regions of recording. The tissue was stained for Nissl substance with cresyl violet. The positions of the array in the spinal cord were verified by observation of the array track. Positions of recording sites were estimated in relation to the array track position. 3 RESULTS 3.1 Neuronal database Altogether, the activity of 243 individual spinal neurons was extracted from the multiunit spike trains recorded in five decerebrate cats during treadmill forward walking evoked by MLR stimulation, including 128 neurons recorded in L4 and 115 neurons – in L6 ( Table 1 ). Of these, few (4%) neurons were “inactive” (i.e. they had the mean cycle frequency of less than 1 Hz) and 17% were active but non-modulated (see criteria for modulation in Materials and Methods). The majority of active neurons (∼86% from L4 and ∼72% from L6) were modulated in the rhythm of stepping movements [ Table 1 , Figure 1D (red and green neurons)]. Only these 193 modulated neurons were used for further analysis. Phase of bursts of individual modulated neurons recorded in L4 and L6 segments in the normalized locomotor cycle is shown in Figure 3 . The neurons were ranked according to the value of the parameter “integral stability of the activity phase” during locomotion ( W ; see Materials and Methods). One can see that neurons with high values of W (with small SD of the burst onset and/or burst offset) located in the upper part of L4 and L6 neurons as well as neurons with low values of W (with large SD of the burst onset and burst offset) located in the lower part of L4 and L6 neurons had various phases of activity in the locomotor cycle. Download figure Open in new tab FIGURE 3 Phase distribution of bursts of individual neurons recorded in L4 and L6 segments in the normalized locomotor cycle. Neurons recorded in each segment are ranked according to the value of their parameter W (integral stability of the activity phase) shown on the right panel in logarithmic scale. Bursts of individual neurons recorded in L4 and L6 are indicated by thick red and blue lines, respectively. Activity of the same neurons in the preceding and the following cycles is indicated by the corresponding pale colors. Thin black lines indicate standard deviations of the burst onset and offset. 3.2 Distribution of burst onset and offset phases across the locomotor cycle One can expect that phases of the locomotor cycle at which the burst onsets and burst offsets occur more frequently, represent the most critical/important phases for generation of the locomotor pattern ( Orlovsky and Feldman, 1972 ). To reveal these key phases, the measured mean burst onset and burst offset phases of individual modulated neurons were used to calculate the probability densities (see Materials and Methods) of the burst onsets ( Figure 4A ) and burst offsets ( Figures 4B ) phases. The values of probability density above its average level in the locomotor cycle (indicated by horizontal dotted lines in Figures 4A,B ) formed peaks. Some of them were large and exceeded the double of the average value (indicated by horizontal dashed lines in Figures 4A,B ), while others did not reach it. Peaks of the burst onsets ( Figure 4A ) fell into 4 phase ranges (I-IV), while peaks for the offset probability ( Figure 4B ) fell into 3 phase ranges (I, II and IV). These results are qualitatively similar to lower precision results of previous studies ( Orlovsky & Feldman, 1972 ). Download figure Open in new tab FIGURE 4 Important phases of the locomotor cycle. (A-D) Distributions of phase probability density (A,B) and phase weighted probability density (C,D) for the burst onsets (A,C) and offsets (B,D) for all (L4+L6) neurons. (E,F) Phases of peaks (parts of the curve above the average level indicated by dotted line in A-D) and major peaks (above the doubled average level indicated by dashed line in A-D) of probability (E) and weight (F) distributions are shown separately for L4 and L6 neurons. Thin and thick lines, and small and large diamonds indicate peaks and major peaks, and the peak tops, respectively. Only significant peaks were taken into account: if a peak would disappear after the exclusion of one neuron with w = 1, this peak was considered not significant. Additionally, two neighboring peaks were fused into one if they were separated by an insignificant trough that would disappear after the addition of one neuron with w = 1. Practically all peaks fell into 4 phase ranges I-IV (indicated by yellow in E,F). The observed complex patterns of peaks can be explained either by real presence of many important phases in the locomotor cycle or by data noise. To reduce the noise, we examined the weighted phase distributions (see Materials and Methods), assigning larger weight to edges with lower SD. Thus, the weight of 1 was assigned to edges with SD = 0.1, which, on one hand, was the median value of both the onset and offset phases in our database, and on the other hand constituted a rather small part of the locomotor cycle (approximately one quarter of the swing or one sixth of the stance). The weight was higher than 9 (although never higher than 10) for edges with SD ≤ 0.01, which is a negligibly small variance, comparable to the instrument error in our experiments. Finally, the weight was lower than 0.3 (though always positive) for edges with SD ≥ 0.2, which is a rather large variance (e.g., larger than a half of the swing). In such weight distribution, data from approximately 30% of the entire population (neurons with large SD , which most likely were not critical for the locomotor pattern generation) was effectively filtered out. On the other hand, data from approximately 20% of the population (neurons with small SD , most likely, comprising the core of SLN) contributed more to the result. The weight distribution ( Figures 4C,D ) had the same peak locations as the probability distribution ( Figures 4A,B ) but the peaks were much clearer. Thus, fuzziness of the original edge distribution was most likely due to random data noise from neurons with unstable modulation (large SD ). In both L4 neurons and L6 neurons, most of the observed peaks of the onsets and offsets fell into the same 4 phase ranges (I-IV) ( Figures 4E , F): range I with phases close to 0.8 part of the locomotor cycle, range II with phases close to 0 (beginning of the cycle), range III with phases close to 0.15 part of the cycle, and range IV with phases close to 0.4 part of the cycle. Thus, neurons were activated and inactivated not randomly in the locomotor cycle, but preferably within one of the 4 phase ranges. These 4 ranges were presumably related to discrete behavioral events: preparation for the limb lift-off (range I), the limb lift-off (range II), transition from the limb flexion to limb extension during swing (range III), and the limb touch-down (range IV). 3.3 | Classification of neurons based on their burst phases To reveal groups of neurons with similar phase of activity modulation in the locomotor cycle, we considered only neurons with stable modulation that are most likely critically important for generation of locomotion. The following criteria for stability of modulation formulated in our previous study ( Musienko et al., 2022 ) were used: (i) SD of the burst onset and/or burst offset should not be more than 0.1 part of the locomotor cycle. We accepted stability of only one edge of the burst, as a subset of the recorded neurons had ramp-up or ramp-down burst shape with stable burst offset and burst onset phase, respectively. (ii) The average coefficient of correlation between the profiles of the activity in individual locomotor cycles and in the entire activity histogram had to be no less than 0.6. According to these criteria, 126 out of 193 modulated neurons (65%) had stable modulation (“stable neurons”, Table 1 ). To the population of stable neurons, we applied a novel method of clustering that relies more on data with higher values of the phase weight (see Materials and Methods). As a result, activities of almost all stable neurons (91%, 115 out of 126 stable neurons) were classified into 19 Clusters ( Table 2 ; Figure 5 ; Supplementary Figure 2A). In 11 neurons, activity phases were unique. We termed them “ Cluster 0” and did not consider in the further analysis. Download figure Open in new tab FIGURE 5 Phase distribution of sorted into Clusters bursts of individual neurons with stable modulation recorded in L4 and L6 segments in the normalized locomotor cycle. The mean bursts of individual Clusters with the middle point indicated (white circles) are shown by thick purple lines. Neurons of different Clusters are demarcated by dotted lines. Clusters are ordered according to the midburst phase. Numbers in circles on the left are Cluster numbers. Four phase ranges I-IV are indicated by yellow. Other abbreviations as in Figure 3 . View this table: View inline View popup Download powerpoint TABLE 2 Rostro-caudal distribution of neurons belonging to different Clusters . Rostro-caudal distribution of neurons belonging to different Clusters as well as assorted neurons ( Cluster 0), was compared to that of the entire recorded population using Fisher’s exact test (df = 1). Statistically significant (with P < 0.05) and visible but statistically insignificant (with 0.05 < P < 0.3, due to low numbers of neurons) biases in the neurons’ location are indicated, respectively, by bold and underlined P values. n , number of neurons. Since the neurons within each Cluster had similar activity phases, in the further analysis, we considered each Cluster as a functionally homogeneous set of neurons. For each Cluster , the phase of activity of the Cluster was characterized by weighted average phases of the burst onsets and offsets of neurons comprising the Cluster with the corresponding SD s (see Materials and Methods). The interval between these average phases was termed the “burst” of the Cluster (thick purple lines in Figure 5 ). As seen in Figure 5 , the majority of Clusters had the burst onset in one phase of the locomotor cycle (swing or stance) and the burst offset in another phase. Only two Clusters ( 2 and 12 ) had bursts within the stance and no one within the swing. 3.4 Functional classification of Clusters In different SLN models ( Ijspeert, 2008 ) the locomotor rhythm is generated by neuronal classes with reciprocal inhibition, which are active out of phase. To reveal such pairs of Clusters active out of phase, for each Cluster we calculated its midburst phase (see Materials and Methods; white circles on thick purple lines in Figure 5 ). Then, for each pair of Clusters , we calculated the phase shifts between their midburst phases (see Materials and Methods as well as Supplementary Data pages 10-11 for specific details). Figure 6A shows the heatmap of the phase shift between midbursts in different Cluster pairs. All Clusters can be combined into 4 pairs of Subgroups active approximately out of phase (the corresponding phase shifts outlined by 4 dashed rectangles in Figure 6A ): Subgroup 1 ( Clusters 10, 13, 17 ) is out of phase with Subgroup 2 ( Clusters 16, 6, 8, 9 ), Subgroup 3 ( Clusters 11, 5 ) – with Subgroup 4 ( Clusters 1, 2 ), Subgroup 5 ( Clusters 14, 18 ) - with Subgroup 6 ( Clusters 12, 19 ), and Subgroup 7 ( Clusters 15, 7 ) – with Subgroup 8 ( Clusters 4, 3 ). Download figure Open in new tab FIGURE 6 Functional classification of Clusters . (A) Heatmap of the phase shift between midbursts of different Cluster pairs. All Clusters can be combined into 4 pairs of Subgroups active approximately out of phase (with phase shifts close to 0.5, outlined by 4 dashed rectangles): Sgr1/Sgr2, Sgr3/Sgr4, Sgr5/Sgr6, Sgr7/Sgr8. (B) The paw trajectory during the locomotor cycle. Green, red, yellow and blue parts of the trajectory are, respectively, forward (F), backward (B), upward (U), and downward (D) limb movements. HC and VC, the horizontal and vertical components of the step. (C) Subgroups are formed by Clusters with similar midburst phases (each color band corresponds to one Subgroup ). These Subgroups can be assigned functional roles ( F PREP , F EXEC , D PREP , D EXEC , B PREP , B EXEC , U PREP , U EXEC ), and they form four Groups ( F, D, B, U ) (see explanations in the text and Supplementary Figure 7). Green and red stars show the middle of the swing and stance, respectively. Clusters constituting one Subgroup had similar midburst phases ( Figure 6C ). Phase ranges of midbursts of Clusters forming individual Subgroups (indicated by colored bands in Figure 6C ) were shifted in relation to one another by approximately 1/8 of the locomotor cycle. To assign functional roles to the Subgroups , one can take into account the paw trajectory during the locomotor cycle ( Figure 6B ; Supplementary Figure 7). It can be divided into forward (the green part of the trajectory, F) and backward (the red part of the trajectory, B) movements corresponding to the horizontal component of the step (HC) or into two parts corresponding to the vertical component of the step (VC) that includes the movements of the limb when it is subjected to unloading and upward transition (the yellow part of the trajectory, U) and to downward transition and loading (the blue part of the trajectory, D), respectively. Phases of HC and VC are shifted by approximately 1/4 of a cycle ( Figure 6B , Supplementary Figure 7). The existence of separate neural mechanisms generation VC and HC of the step was demonstrated earlier ( Musienko et al., 2012 ). Thus, Subgroups 3 and 4, with activity phase ranges close to the middle of the swing and to the middle of stance, respectively, can be responsible for execution of the forward and backward paw movements, and correspondingly termed F EXEC and B EXEC ( Figure 6C ). Subgroup 7, with its activity following F EXEC by 1/4 and leading B EXEC by 1/4, executed the downward movement (limb extension during the swing second half, touchdown, and weight support during the stance first half), while Subgroup 8 executed the upward movement (limb unloading during the stance second half, lift-off, and limb flexion during the swing first half) ( Figure 6C ). Correspondingly, Subgroups 7 and 8 were termed D EXEC and U EXEC . The remaining Subgroups 1, 5, 2, and 6, leading the corresponding executive Subgroups ( F EXEC , D EXEC , B EXEC , and U EXEC ) by 1/8 of the locomotor cycle ( Figure 6C ), can be assigned preparatory roles for the forward, downward, backward, and upward movements, respectively. Correspondingly, they were termed F PREP , D PREP , B PREP , and U PREP . Couples of the preparatory and executive Subgroups, presumably controlling movement in a specific direction (forward, backward, upward, downward) during the step cycle, form Groups F, B, U, and D , respectively ( Figures 6C , 9A). Groups F and B comprise a network generating the HC of movement, while the VC network consists of Groups D and U . Although in general, neurons forming different Clusters were intermingled in the gray matter, we found some specificity in their spatial distribution. Thus, neurons of Clusters 7 and 12 , were found only in the spinal segment L4, while neurons of Clusters 3, 8, 13 and 19 were found only in L6 ( Figures 7 and 5 , Table 2 ). In both L4 and L6, the majority of neurons forming the VC network ( Groups U and D ) were located in the intermediate zone of the gray matter, while the majority of neurons forming the HC network ( Groups F and B ) were located in the dorsal and ventral zones ( Figure 7 , Table 3 ). In both segments these differences in distribution of VC and HC neurons were statistically significant ( Table 4 ). Download figure Open in new tab FIGURE 7 Location of individual neurons of different Clusters in the spinal cord. VC network neurons and HC network neurons are shown separately on cross-sections of the gray matter at spinal segments L4 and L6. Thick hatched lines demarcate the dorsal, intermediate, and ventral zones of the gray matter. Designations (shapes and colors) are as in Figures 6C . View this table: View inline View popup Download powerpoint TABLE 3 Rostro-caudal and dorso-ventral distribution of neurons comprising networks generating the vertical and horizontal components of stepping. VC and HC, networks generating, respectively, the vertical and horizontal components of stepping. The number of neurons ( n ) in each of three zones of the gray matter (dorsal, Zone 1; intermediate, Zone 2; ventral, Zone 3; Figure 8 ) is indicated. Numbers shown in bold are statistically compared in Table 4 . View this table: View inline View popup Download powerpoint TABLE 4 Statistical comparison of the distribution of VC and HC neurons across the transverse section of the gray matter. The relative number of neurons in Zone 2 and Zones 1+3 (see Table 3 ) were statistically compared using Fisher’s exact test (df = 1). Values of P < 0.05 (statistically significant bias in the neurons’ location) are indicated by bold. 3.5 Functional connections between Clusters and Groups To reveal possible functional connections between Clusters , we analyzed short (less than 0.1 part of the cycle) phase shifts between the burst onsets/offsets in different Cluster pairs (Δ in Figures 8A,B ). We suggested that one Cluster ( Pre in Figure 8A ) inhibited another Cluster ( Post in Figure 8A ) if the burst onset of the Pre Cluster was followed by the burst offset of the Post Cluster [activation of “presynaptic” inhibitory neurons inactivated the “postsynaptic” target; Inactivation by inhibition in Figure 8A (i)], or the burst offset of the Pre Cluster was followed by the burst onset of the Post Cluster [ Activation by disinhibition in Figure 8A (ii)]. The latter can be explained by postinhibitory rebound that was demonstrated in spinal interneurons (e.g., Rivera-Arconada & Lopez-Garcia, 2015 ; Zhu et al., 2021 ). The functional connection was considered excitatory if the burst onset of the Pre Cluster was followed by the burst onset of the Post Cluster [ Activation by excitation in Figure 8A (iii)] or if the burst offset of the Pre Cluster was followed by the burst offset of the Post Cluster [ Inactivation due to termination of excitation in Figure 8A (iv)]. Download figure Open in new tab FIGURE 8 Functional connections between Clusters . (A) Revealing possible functional connections between Clusters (see Results for explanation). (B) Heatmap showing functional connections between different Clusters . Blue triangles, inhibitory connections; red triangles, excitatory connections. (C-E) A summary of the revealed inhibitory (C), excitatory (D), and strongest (E) functional connections between Groups . Numbers are the values of parameter S characterizing the strength of functional excitatory or inhibitory connections between the Clusters (see Results for details). S values exceeding the median level (0.25) correspond to “strong” connections (marked by bold), while connections with S below the median are considered “weak”. Figure 8B shows the heatmap of revealed functional connections between different Pre and Post Clusters belonging to different Groups. Cluster pairs with all four types of abovementioned interactions ( Figure 8A ) were found. We did not observe any Cluster pair with both excitatory and inhibitory connections. Between Pre and Post Clusters of each Group , almost all revealed connections were excitatory (red triangles on the pink background in Figure 8B ). Between Pre Clusters forming Group U and Post Clusters forming Group D (VC network), as well as between Pre Clusters forming Group F and Post Clusters forming Group B (HC network), almost all connections were inhibitory (blue triangles on the light blue background in Figure 8B ), which likely explains the reciprocal activity within these pairs of Groups . Finally, between Pre and Post Clusters of Groups that belong to networks contributing to generation of different components of the step (e.g., between Clusters of Group D that belongs to VC and Clusters of Group B that belongs to HC) both inhibitory and excitatory connections were found (blue and red triangles on the white background in Figure 8B ). The values of the phase shifts between the burst onsets/offsets (reflecting the latencies of functional effects, Δ in Figure 8A,B ) were different in different Cluster pairs. One can suppose that functional effects with shorter latencies are mediated by stronger connections, while those with longer latencies are mediated by weaker connections. To evaluate the strength of functional excitatory or inhibitory connections between the Groups, an integral parameter S (“strength”) was calculated: S = (4 N 4 + 3 N 3 + 2 N 2 + N 1 ) / N , where N 4 , N 3 , N 2 , and N 1 were the numbers of revealed connections with the latency in the ranges 0.00-0.01, 0.01-0.03, 0.03-0.06, and 0.06-0.10 parts of the cycle, respectively, and N was the total number of Δ measurements performed for a pair of Groups . The S values varied from 0.03 to 0.46 for excitatory connections and from 0.02 to 0.72 for inhibitory ones ( Figure 8C,D ). However, the median S values for both types of connections were almost the same (approximately 0.25). Connections between Groups with S value below and above 0.25 were considered as weak and strong connections, respectively (indicated by thin and thick lines, respectively, in Figure 8C,D ). Mutual inhibitory connections between Groups of VC ( U and D ) and between Groups of HC ( F and B ), as well as mutual excitatory connections between Clusters within each of Groups, were strong ( Figure 8C,D ). By contrast, excitatory and inhibitory connections between the majority of VC and HC Groups were anisotropic, e.g., Group U strongly excited and weakly inhibited Group F ; by contrast, Group F strongly inhibited and weakly excited Group U ( Figure 8C,D ). Thus, one can suggest that Groups form a generating ring - U-F-D-B -( Figure 8E ), in which each Group inactivates the preceding one and activates the next one. So, although each Cluster of a Group is not homogeneous and contains neurons producing excitatory as well as neurons producing inhibitory effects, the functional effect of one Group upon another Group is determined by the strongest excitatory and inhibitory connections. Thus, activation of Group U leads to activation of all its neurons (due to mutual excitatory connections) leading to inhibition of Groups B and D that results in lifting of the limb above the ground. Simultaneously, activated Group U activates Group F. Activation of Group F leads to activation of all its neurons (due to their mutual excitatory connections) that leads to inhibition of Group U and Group B and resulting in forward progression of the limb. Simultaneously, Group F activates Group D that in turn inhibits Group U and Group F that results in downward movement of the limb. Simultaneously, Group D activates Group B that in turn inhibits Groups D and F resulting in the backward limb movement. Activated Group B simultaneously activates Group U and so on. 4 Discussion 4.1 Neurons comprising the spinal locomotor network (SLN) In the present study we analyzed the spinal locomotor network (SLN) generating real forward locomotion with close to normal movement-related sensory feedback. Spinal interneurons form the core of this network. It should be noted that we discriminate between SLN and the central pattern generator (CPG, the part of the spinal network capable of generating the locomotor pattern without sensory feedback). SLN includes CPG but is not limited to CPG. There is some discrepancy in results of recording of spinal interneurons with activity modulated in the locomotor rhythm. Studies in which spinal interneurons were recorded during forward locomotor movements [e.g., earlier studies in which a small database was obtained during real forward locomotion in cats with intact sensory feedback and after deafferentation ( Orlovsky & Feldman, 1972 ), studies on spinal cats performing forward air-stepping ( AuYong et al., 2011 ), as well as the present study] demonstrated rather uniform distribution of phases of interneuronal activity across the locomotor cycle. By contrast, recording of spinal interneurons during spontaneous episodes of fictive locomotion in rabbits ( Viala et al., 1991 ) and cats ( Baev et al., 1979 ) revealed two clusters of neurons with activity phases almost coincident with either flexor or extensor motoneuronal activity. Unfortunately, in the last two studies only motor nerves to flexor and extensor muscles of the ankle joint were recorded that did not allow to determine the type of generated locomotor pattern (i.e. whether the motor pattern corresponded to forward stepping, backward stepping, or in place stepping). One of possible explanations of the discrepancy between the data obtained during fictive locomotion and forward locomotion with intact movement-related sensory feedback, is that in immobilized animals, neurons were recorded during in place locomotion characterized by simple reciprocal activity of flexor and extensor muscles around all joints of the limb ( Lyakhovetskii et al., 2021 ). Earlier we demonstrated that spinal networks generating the forward and backward stepping differ to some extent ( Merkulyeva et al., 2018 ; Musienko et al., 2022 ). In our study, we focused on interneurons with stable modulation of activity that most likely represent the essential elements of SLN. We excluded neurons with sparse firing from our analysis. It has been suggested that such neurons may play an important role in the rhythm generation ( Strohmer et al., 2024 ). Unfortunately, adequate analysis of sparse activity would require recording of much longer episodes than used in the present study. This can be a goal of future studies. Since spinal interneurons were recorded during real forward locomotion in the presence of normal movement-related sensory feedback, we cannot exclude that locomotion-related modulation of activity in a part of these neurons was driven by the sensory feedback. There are numerous findings (e.g., Grillner & Rossignol. 1978 ; Whelan et al., 1995 ; Hiebert et al., 1996 ) strongly suggesting that in real stepping, the timing of different events in the step cycle is determined not by CPG activity ( Orlovsky et al., 1999 ) but rather by afferent signals about the limb movement. Thus, sensory driven neurons represent an important part of SLN generating forward locomotor movements. However, the majority of interneurons recorded in our study were similar to those revealed during locomotion in deafferented cats ( Orlovsky & Feldman, 1972 ; Dai et al., 2005 ) in terms of location (the ventral and intermediate zones of the gray matter, Figure 7 ), and in terms of distribution of the activity phases across the locomotor cycle. Therefore, one can suppose that most of our SLN neurons belonged to CPG. We correlated activity of recorded neurons with the locomotor cycle of the ipsilateral limb. We cannot exclude that a part of recorded neurons were commissural interneurons involved in control of the contralateral limb. However, the majority of neurons were recorded outside of areas in which commissural interneurons are located. Thus, we do not think that a few commissural neurons in our database can affect the interpretation of our results. 4.2 Possible functional roles of neuronal Clusters Our analysis revealed 4 phase ranges in the cycle ( Figure 4 ) when many neurons were turned on and off. Onset and offset phases of the majority of neuronal Clusters revealed in the present study fell into the abovementioned 4 phase ranges ( Figure 5 ). Thus, activity of SLN neurons is changed mainly around four critical moments in the locomotor cycle that demarcate F, E1, E2, and E3 phases of the cycle revealed by kinematic analysis (Engberg & Lundberg, 1969). Most likely, changes in neuronal activity within range I contribute to transition from weight support (E2 phase) to limb unloading (E3 phase) as well as to preparation for the following limb lift-off. Changes in activity within range II contribute to transition from the stance (E3 phase) to the swing phase (F phase; the limb lift-off) as well as to preparation for the following limb forward transfer. Activity changes within range III may contribute to transition from the limb flexion (F phase) to the knee and ankle extension during swing (E1 phase) as well as to preparation for the following touch-down and weight acceptance. Finally, activity changes within range IV may contribute to transition from the swing (E1 phase) to the stance phase (E2 phase; the limb touch-down). In contrast, transition from limb shortening to limb elongation during stance (in the midstance) is less precisely controlled: neurons of Clusters 19, 12 and 18, 15 that are turned, respectively, on and off in this part of the cycle ( Figure 5 ) are relatively not numerous, and their on- and offset phases are defined not very precisely (note the larger SD s in these Clusters in Figure 5 ). Analysis of the motor pattern of forward stepping revealed a few (2-5 in different studies) motor modules or muscle synergies that underly forward locomotor movement ( Dominici et al., 2011 ; Ting et al., 2015 ; Aoi et al., 2013 ). It was suggested that their phases represent important events in the locomotor cycle. While there is a consensus that this structure exists in motor patterns, however, how it arises, whether it reflects neuronal structure, and whether it is functionally relevant are sources of lively debate. Suggested by our analysis functional effects of each Cluster can be excitatory on some Clusters but inhibitory on others ( Figure 8B ). Thus, one can assume that each Cluster contains several types of neurons differing in projections as well as neurotransmitters. The activity phase of some Clusters ( Figure 5 ) coincided with the activity phase of specific limb muscles ( Krouchev et al., 2006 ; Markin et al., 2012 ). For example, activity of Cluster 3 coincided with activity of muscles initiating the swing (cluster 2 in Figure 7 , Krouchev et al., 2006 , and cluster 5 in Figure 5B , Markin et al., 2012 ) and Cluster 14 was active at the end of swing and the beginning of stance, similar to muscle cluster 5 in Figure 7 , Krouchev et al., 2006 , and cluster 7 in Figure 5B , Markin et al., 2012 . Neurons of such Clusters could be the last order premotor interneurons. However, the other Clusters (e.g., 7, 10, 17 ) had no motoneuronal counterparts, and thus, they probably do not directly affect the motoneurons. 4.3 Conceptual model of SLN Our previous study, in which SLN was activated by epidural stimulation, demonstrated the existence of separate neural mechanisms generating vertical (VC) and horizontal (HC) components of a step ( Musienko et al., 2012 ). The present study suggests that according to their activity phases, all interneuronal Clusters can be assigned either to VC or HC parts of SLN ( Figure 9A ). Moreover, the VC consists of a pair of reciprocally inhibiting Group U (up) and Group D (down) ( Figure 9B ), while the HC consists of a pair of reciprocally inhibiting Group F (forward) and Group B (backward) ( Figure 9C ). Thus, in contrast to previous studies ( Orlovsky & Feldman, 1972 ; Baev et al., 1979 ; Viala et al., 1991 ; AuYong et al., 2011 ), we found several pairs of reciprocally inhibiting neuronal Groups with activity in different and specific parts of the step cycle. Download figure Open in new tab FIGURE 9 Summary of obtained results. (A) Hierarchical organization of the spinal locomotor network (SLN). (B-E) A hypothetical model of SLN based on revealed connections between Clusters . (B) A part of SLN generating the vertical component of stepping. The up (U) and down (D) half-centers are active out of phase (the sequence of activity: up – down – up – down – …) due to reciprocal inhibition. (C) A part of SLN generating the horizontal component of stepping. The forward (F) and backward (B) half-centers are active out of phase (the sequence of activity forward – backward – forward – backward – …) due to reciprocal inhibition. (D) Due to anisotropic inhibition and excitation in pairs U/F, F/D, D/B, and B/U, activity is transferred in ring manner (red arrows show the sequence of activity up – forward – down – backward – up – …). (E) The entire SLN for forward locomotion combines these two half-center-type networks and one ring-type network. Color coding of Clusters, Subgroups and Groups is the same as in Figure 6C . In addition, anisotropic excitatory and inhibitory connections ( Figures 8C,D ) result in formation of the -U-F-D-B- generating ring ( Figure 9D ). For example, when Group U is active, it will inactivate Groups D and B through the strong inhibitory connection and activate Group F through the strong excitatory connection ( Figure 8E ). Similarly, in its turn, activation of Group F will result in inactivation of Groups U and B , and activation of Group D (note that at this phase of the cycle, due to inactivation of Group U , Group D is no longer inhibited by Group U ). Thus, activity will be conveyed from U to F, then from F to D , and so on (red arrows in Figure 9D ). Correspondingly, transition of the neuronal activity from Group U to Group F will result in transition from generation of F phase (limb lift off) to generation of E1 phase (forward protraction of the limb). Transition of activity from Group F to Group D and then to Group B will lead to transition from generation of E1 phase to generation of E2 phase (the limb tough down, loading and beginning of the backward movement). Finally, transition of activity from Group B to Group F will lead to transition from generation of E2 phase to generation of E3 phase and then F phase (the limb unloading, end of the backward movement, and the limb lift-off, respectively). It was demonstrated that the network generating the VC of step ( Groups U and D ) can be activated selectively that leads to in place stepping ( Musienko et al., 2012 ). However, it is not clear whether only one or both VC Groups or only some Clusters of the Groups are rhythmogenic, while the others follow their rhythm, or if the rhythm is an emergent property of a part of or the entire VC network. Also, it is not clear whether the HC network ( Groups F and B ) alone can generate the rhythmic horizontal component of steps or if the HC network is driven by the VC network through the ring connections. These are questions for future studies. It is interesting to note that almost all connections in the ring are not unidirectional but just anisotropic. One can expect that changing the efficacy of these connections (strengthening the weaker while weakening the stronger ones) will reverse the direction of the activity propagation (red arrows in Figure 9D will be reversed), resulting in a change of the direction of locomotion from forward to backward. We also found that spinal interneurons forming the network generating VC and those forming the network generating HC of the step have significantly different distribution across the gray matter of the spinal cord. Most VC neurons ( Groups U and D ) were located in the intermediate part of the gray matter while the majority of HC neurons ( Groups F and B ) – in the dorsal and ventral parts of the grey matter ( Figure 7 ). There are a number of models explaining the generation of locomotor movements, which are debated. Some earlier hypothetical models suggested that the locomotor rhythm can be generated by three or more neuronal elements forming a closed ring ( Kling & Székely, 1968 ). Later, a model was suggested, in which the neuronal activity is continuously cycling through all phases of locomotor cycle (Linden et al., 2022). The most popular model based on numerous studies of locomotor networks in animal models of different complexity, suggests that a rhythm-generating part of the locomotor network consists of just two populations (half-centers) of neurons with mutual inhibitory connections ( Brown, 1914 ; Jankowska et al., 1967 ; Lundberg, 1981 ; Ijspeert, 2008 ). Experimental observations demonstrated that the original half-center hypothesis in its simplest form cannot account for specific profiles of EMG activity ( Perret & Cabelguen, 1980 ) and non-resetting deletions of activity of specific muscles ( Lafreniere-Roula & McCrea, 2005 ). Therefore, a more advanced SLN model ( Rybak et al., 2006 ; McCrea & Rybak, 2008 ; Rybak et al., 2015 ) includes a rhythm generator of the half-center type, and an output stage responsible for formation of the locomotor EMG pattern. Another SLN model ( Grillner, 1985 , 2006 , 2021 ) suggests that the locomotor network contains a set of rhythm-generating units, each controlling muscles of a particular joint, and specific interactions between these units result in generation of a particular locomotor pattern. Finally, CPG controllers incorporating pattern formation models simulating modular muscle activations with multiple patterns loosely corresponding to different locomotor phases have been proposed as well ( Aoi et al., 2013 ). In the present study we suggested a new model of SLN. However, we think that our experimental data are compatible with all other models. Each of the revealed Clusters can be rhythmogenic and thus represent a unit generator or a part of a unit generator from Grillner’s model. We cannot exclude that just one Cluster or a pair of mutually inhibiting Clusters are rhythmogenic, while the other Clusters represent different parts of the pattern formation layer from Rybak’s model. It is also possible that the minimal rhythmogenic circuit must include Clusters from all 4 Groups to form a minimal rhythmogenic ring. Future studies will clarify these issues. Recent advances in genetics led to initiation of numerous studies devoted to identification of components of the spinal locomotor network based on transcription factor expression (for review, Kiehn, 2016 ). It turned out that no single genetically identified type of spinal interneurons has been found to be solely responsible for the locomotor rhythm generation or control of the step direction ( Rancic & Gosgnach, 2021 ). It was suggested that the locomotor rhythm is generated by a set of genetically heterogeneous populations ( Kiehn, 2016 ; Dougherty et al., 2013 ; Caldeira et al., 2017 ). Thus, there is evidence that at least two genetically identified populations of interneurons, glutamatergic Hb9-expressing interneurons and Shox2 non-V2a interneurons (Shox2+ interneurons that do not co-express Ch10), may be involved in generation of the locomotor rhythm ( Dougherty et al., 2013 ; Caldeira et al., 2017 ). By contrast, it was demonstrated that two genetically identified populations, V1 and V2b interneurons are required for the flexor-extensor alternation during locomotion (inhibition of flexors is mediated by V1 interneurons and of extensors by V2b interneurons) (Britz et al., 2015). To conclude, in the present study, by using a novel method of analysis of neuronal activity recorded during real forward locomotion, we have revealed groups of spinal putative interneurons presumably responsible for generation of the vertical and horizontal components of a step, their possible functional connections, and their contribution to generation of limb movement in different parts of the locomotor cycle, as well as roughly demarcated their locations. On the basis of the obtained results, a novel conceptual model for generation of forward locomotion has been suggested. The obtained data can be used as a benchmark for computational models of locomotor neuronal networks. Developed in the present study method of analysis can be used for analysis of rhythmical activity of neurons related to other motor functions (e.g., scratching, paw shaking, etc.). CRediT AUTORSHIP CONTRIBUTION STATEMENT Pavel E. Musienko: Conceptualization, Funding acquisition, Investigation, Methodology, Writing – review and editing. Oleg V. Gorskii: Investigation, Methodology, Writing – review and editing. Tatiana G. Deliagina: Conceptualization, Funding acquisition, Investigation, Methodology, Validation, Visualization, Writing – original draft. Pavel V. Zelenin: Conceptualization, Formal analysis, Investigation, Methodology, Validation, Visualization, Writing – original draft. CONFLICT OF INTEREST STATEMENT The authors have no conflicts of interest to declare. DATA AVAILABILITY STATEMENT The data that support the findings of this study are available from the corresponding author upon reasonable request. ACKNOWLEDGEMENTS This work was supported by the St. Petersburg State University, St. Petersburg, Russia (projects: 94030803, 104623591) to PEM; by grant from NIH (R01 NS-100928) to TGD and PEM; by grant from NIH (R01 NS-064964) to TGD; by grants from Swedish Research Council (2020-02502) to TGD; by the Russian Science Foundation grant 22-15-00092 to PEM; by Sirius University of Science and Technology project: NRB-RND-2115 to PEM. We thank Natalia Merkulyeva for helping with the histological evaluation. Funder Information Declared National Institute of Neurological Disorders and Stroke , R01 NS-064964 , R01 NS-100928 Swedish Research Council , 2020-02502 St. Petersburg State University, St. Petersburg, Russia , 94030803 , 104623591 Russian Science Foundation, https://ror.org/03y2gwe85 , 22-15-00092 Sirius University of Science and Technology, https://ror.org/00n51jg89 , NRB-RND-2115 Abbreviations SLN spinal locomotor network MLR mesencephalic locomotor region CPG central pattern generator HC horizontal component of the step VC vertical component of the step REFERENCES ↵ Aoi , S. , Kondo , T. , Hayashi , N. , Yanagihara , D. , Aoki , S. , Yamaura , H. , Ogihara , N. , Funato , T. , Tomita , N. , Senda , K. , & Tsuchiya K . ( 2013 ). Contributions of phase resetting and interlimb coordination to the adaptive control of hindlimb obstacle avoidance during locomotion in rats: a simulation study . Biological Cybernetics , 107 , 201 – 216 . OpenUrl CrossRef PubMed ↵ AuYong , N. , Ollivier-Lanvin , K. , & Lemay , M. A . ( 2011 ). Preferred locomotor phase of activity of lumbar interneurons during air-stepping in subchronic spinal cats . Journal of Neurophysiology , 105 , 1011 – 1022 . OpenUrl CrossRef PubMed Web of Science ↵ Baev , K. V. , Degtyarenko , A. M. , Zavadskaya , T. V. , & Kostyuk , P. G . ( 1979 ). Activity of interneurons of the lumbar region of the spinal cord during fictive locomotion of thalamic cats . Neirofiziologiya , 11 , 329 – 338 . OpenUrl ↵ Batschelet , E . ( 1981 ). Circular Statistics in Biology . New York : Academic Press . ↵ Berkowitz , A. , Stein , P. S . ( 1994 ). Activity of descending propriospinal axons in the turtle hindlimb enlargement during two forms of fictive scratching: phase analyses . Journal of Neuroscience , 14 , 5105 – 5119 . OpenUrl Abstract / FREE Full Text Britz, O. Zhang, J., Grossmann, K. S., Dyck, J., Kim, J. C., Dymecki, S., Gosgnach, S., & Goulding, M . ( 2015 ). A genetically defined asymmetry underlies the inhibitory control of flexor-extensor locomotor movements . eLife 4 , e04718 . doi: 10.7554/eLife.04718 . OpenUrl CrossRef PubMed ↵ Brown , T. G . ( 1911 ). The intrinsic factors in the act of progression in mammals . Proceedings of the Royal Society of London B , 84 , 308 – 319 . OpenUrl CrossRef ↵ Brown , T. G . ( 1914 ). On the nature of the fundamental activity of the nervous centres: together with an analysis of the conditioning of rhythmic activity in progression, and a theory of the evolution of function in the nervous system . Journal of Physiology , 48 , 18 – 46 . OpenUrl CrossRef PubMed Web of Science ↵ Bouvier , J. , Caggiano , V. , Leiras , R. , Caldeira , V. , Bellardita , C. , Balueva , K. , Fuchs , A. , & Kiehn , O . ( 2015 ). Descending command neurons in the brainstem that halt locomotion . Cell , 163 , 1191 – 1203 . OpenUrl CrossRef PubMed ↵ Caggiano , V. , Leiras , R. , Goñi-Erro , H. , Masini , D. , Bellardita , C. , Bouvier , J. , Caldeira , V. , Fisone , G. , & Kiehn , O . ( 2018 ). Midbrain circuits that set locomotor speed and gait selection . Nature , 553 , 455 – 460 . OpenUrl CrossRef PubMed ↵ Caldeira , V. , Dougherty , K. J. , Borgius , L. , & Kiehn , O . ( 2017 ). Spinal Hb9::Cre-derived excitatory interneurons contribute to rhythm generation in the mouse . Scientific Reports , 7 , 41369 . doi: 10.1038/srep41369 . OpenUrl CrossRef PubMed ↵ Dai , X. , Noga , B. R. , Douglas , J. R. , & Jordan , L. M . ( 2005 ). Localization of spinal neurons activated during locomotion using the c-fos immunohistochemical method . Journal of Neurophysiology , 93 , 3442 – 3452 . OpenUrl CrossRef PubMed Web of Science ↵ Dominici , N. , Ivanenko , Y. P. , Cappellini , G. , d’Avella , A. , Mondì , V. , Cicchese , M. , Fabiano , A. , Silei , T. , Di Paolo , A. , Giannini , C. , Poppele , R. E. , & Lacquaniti , F. ( 2011 ). Locomotor primitives in newborn babies and their development . Science , 334 , 997 – 999 . OpenUrl Abstract / FREE Full Text ↵ Dougherty , K. J. , Zagoraiou , L. , Satoh , D. , Rozani , I. , Doobar , S. , Arber , S. , Jessell , T. M. , & Kiehn , O . ( 2013 ). Locomotor rhythm generation linked to the output of spinal shox2 excitatory interneurons . Neuron , 80 , 920 – 933 . doi: 10.1016/j.neuron.2013.08.015 . OpenUrl CrossRef PubMed Web of Science Endberg , I. , & Lundberg , A . ( 1969 ). An electromyographic analysis of muscular activity in the hindlimb of the cat during unrestrained locomotion . Acta Physiologica Scandinavia , 75 , 614 – 630 . OpenUrl CrossRef PubMed Web of Science ↵ Feldman , A. G. , & Orlovsky , G. N . ( 1975 ). Activity of interneurons mediating reciprocal 1a inhibition during locomotion . Brain Research , 84 , 181 – 194 . OpenUrl CrossRef PubMed Web of Science ↵ Garcia-Rill , E. , & Skinner , R. D . ( 1987a ). The mesencephalic locomotor region . I. Activation of a medullary projection site. Brain Research , 411 , 1 – 12 . OpenUrl PubMed ↵ Garcia-Rill , E. , & Skinner , R. D . ( 1987b ). The mesencephalic locomotor region . II. Projections to reticulospinal neurons. Brain Research , 411 , 13 – 20 . OpenUrl PubMed ↵ Grillner , S . ( 1985 ). Neurobiological bases of rhythmic motor acts in vertebrates . Science , 228 , 143 – 149 . OpenUrl Abstract / FREE Full Text ↵ Grillner , S . ( 2006 ). Biological pattern generation: the cellular and computational logic of networks in motion . Neuron , 52 , 751 – 766 . OpenUrl CrossRef PubMed Web of Science ↵ Grillner , S . ( 2021 ). The CPGs for limbed locomotion – facts and fiction . International Journal of Molecular Science , 20 : 5882 , doi: 10.3390/ijms22115882 . OpenUrl CrossRef ↵ Grillner , S. , & Rossignol , S . ( 1978 ). On the initiation of the swing phase of locomotion in chronic spinal cats . Brain Research , 146 , 269 – 227 . OpenUrl CrossRef PubMed Web of Science ↵ Halbertsma , J . ( 1983 ). The stride cycle of the cat: the modelling of locomotion by computerized analysis of automatic recordings . Acta Physiologica Scandinavica Suppl , 521 , 1 – 75 . OpenUrl ↵ Hiebert , G. W. , Whelan , P. J. , Prochazka , A. , & Pearson , K. G . ( 1996 ). Contribution of hind limb flexor muscle afferents to the timing of phase transitions in the cat step cycle . Journal of Neurophysiology , 75 , 1126 – 1137 . OpenUrl CrossRef PubMed Web of Science ↵ Ijspeert , A. J . ( 2008 ). Central pattern generators for locomotion control in animals and robots: a review . Neural Networks , 21 , 642 – 653 . OpenUrl CrossRef PubMed Web of Science ↵ Jankowska , E. , Jukes , M. G. , Lund , S. , & Lundberg , A . ( 1967 ). The effect of DOPA on the spinal cord. 6. Half-centre organization of interneurones transmitting effects from the flexor reflex afferents . Acta Physiologica Scandinavica , 70 , 389 – 402 . OpenUrl CrossRef PubMed Web of Science ↵ Jordan , L. M . ( 1986 ). Neurobiology of Vertebrate Locomotion: Initiation of Locomotion from the Mammalian Brainstem . London : Macmillan . ↵ Kiehn , O . ( 2016 ). Decoding the organization of spinal circuits that control locomotion . Nature. Reviews Neuroscience , 17 , 224 – 238 . OpenUrl CrossRef PubMed ↵ Kling , U. , & Székely , G . ( 1968 ). Simulation of rhythmic nervous activities . I. Function of networks with cyclic inhibitions. Kybernetik , 5 , 89 – 103 . OpenUrl PubMed ↵ Krouchev , N. , Kalaska , J. F. , & Drew , T . ( 2006 ). Sequential activation of muscle synergies during locomotion in the intact cat as revealed by cluster analysis and direct decomposition . Journal of Neurophysiology , 96 , 1991 – 2010 . OpenUrl CrossRef PubMed Web of Science ↵ Lafreniere-Roula , M. , & McCrea , D. A . ( 2005 ). Deletions of rhythmic motoneuron activity during fictive locomotion and scratch provide clues to the organization of the mammalian central pattern generator . Journal of Neurophysiology , 94 , 1120 – 1132 . OpenUrl CrossRef PubMed Web of Science Lindén , H. , Petersen , P. C. , Vestergaard , M. , & Berg , R. W . ( 2022 ). Movement is governed by rotational neural dynamics in spinal motor networks . Nature , 610 , 526 – 531 . OpenUrl CrossRef PubMed ↵ Lundberg , A . ( 1981 ). “ Half-centres revisited ”, in: Regulatory Functions of the CNS — Principles of Motion and Organization , eds. J. Szentágothai , M. Palkovits , J. Hámori , ( London: Pergammon Press, and Budapest: Akadémiai Kiadó ), 155 – 167 . ↵ Lyakhovetskii , V. , Merkulyeva , N. , Gorskii , O. , & Musienko , P . ( 2021 ) Simultaneous bidirectional hindlimb locomotion in decerebrate cats . Scientific Reports , 11 , 3252 . OpenUrl CrossRef PubMed ↵ Markin , S. N. , Lemay , S. N. , Prilutsky , B. I. , & Rybak , I. A . ( 2012 ). Motoneuronal and muscle synergies involved in cat hindlimb control during fictive and real locomotion: a comparison study . Journal of. Neurophysiology , 107 , 2057 – 2071 . OpenUrl CrossRef PubMed Web of Science ↵ Markin , S. N. , Klishko , A. N. , Shevtsova , N. A. , Lemay , M. A. , Prilutsky , B. , & Rybak , I. A . ( 2016 ). “ A neuromechanical model of spinal control of locomotion ”, in Neuromechanical Modeling of Posture and Locomotion , eds. B. I. Prilutsky and D. H. Edwards , ( New York: Springer ), 21 - 65 . ↵ McCrea , D. A. , & Rybak , I. A . ( 2008 ). Organization of mammalian locomotor rhythm and pattern generation . Brain Research Reviews , 57 , 134 – 146 . OpenUrl CrossRef PubMed Web of Science ↵ Merkulyeva , N. , Veshchitskii , A. , Gorsky , O. , Pavlova , N. , Zelenin , P. V. , Gerasimenko , Y. , Deliagina , T. G. , & Musienko , P . ( 2018 ). Distribution of spinal neuronal networks controlling forward and backward locomotion . Journal of Neuroscience , 38 , 4695 – 4707 . OpenUrl Abstract / FREE Full Text ↵ Musienko , P. E. , Zelenin , P. V. , Lyalka , V. F. , Gerasimenko , Y. P. , Orlovsky , G. N. , & Deliagina , T. G . ( 2012 ). Spinal and supraspinal control of the direction of stepping during locomotion . Journal of Neuroscience , 32 , 17442 – 17453 . OpenUrl Abstract / FREE Full Text ↵ Musienko , P. E. , Lyalka , V. F , Gorskii , O. V. , Merkulyeva , N. , Gerasimenko , Y. P. , Deliagina T. G. , & Zelenin P. V . ( 2020 ) Comparison of operation of spinal locomotor networks activated by supraspinal commands and by epidural stimulation of the spinal cord in cats . Journal of Physiology , 598 : 3459 – 3483 . OpenUrl CrossRef PubMed ↵ Musienko , P. E. , Lyalka , V. F. , Gorskii , O. V. , Zelenin , P. V. , & Deliagina , T. G . ( 2022 ) Activity of spinal interneurons during forward and backward locomotion . Journal of Neuroscience , 42 , 3570 – 3586 . OpenUrl Abstract / FREE Full Text ↵ Noga , B. R. , Shefchyk , S. J. , Jamal , J. , & Jordan , L. M . ( 1987 ). The role of Renshaw cells in locomotion: antagonism of their excitation from motor axon collaterals with intravenous mecamylamine . Experimental Brain Research , 66 , 99 – 105 . OpenUrl CrossRef PubMed Web of Science ↵ Orlovsky , G. N. , & Feldman , A. G . ( 1972 ). Classification of lumbosacral neurons by their discharge pattern during evoked locomotion . Neurophysiology , 4 , 311 – 317 . OpenUrl PubMed ↵ Orlovsky , G. N. , Deliagina , T. G. , & Grillner , S . ( 1999 ). Neuronal Control of Locomotion. From Mollusc to Man . Oxford : Oxford Univ. Press . ↵ Perret , C. , & Cabelguen , J. M . ( 1980 ). Main characteristics of the hindlimb locomotor cycle in the decorticate cat with special reference to bifunctional muscles . Brain Research , 187 , 333 – 52 . OpenUrl CrossRef PubMed Web of Science ↵ Pratt , C. A. , & Jordan , L. M . ( 1987 ). Ia inhibitory interneurons and Renshaw cells as contributors to the spinal mechanisms of fictive locomotion . Journal of Neurophysiology , 57 , 56 – 71 . OpenUrl CrossRef PubMed Web of Science ↵ Rancic , V. , & Gosgnach S . ( 2021 ). Recent Insights into the Rhythmogenic Core of the Locomotor CPG . International Journal of Molecular Sciences , 22 , 1394 . doi: 10.3390/ijms22031394 . OpenUrl CrossRef PubMed ↵ Rivera-Arconada , I. , & Lopez-Garcia , J. A . ( 2015 ). Characterisation of rebound depolarisation in mice deep dorsal horn neurons in vitro . Pflugers Archiv , 467 , 1985 – 1996 . OpenUrl CrossRef PubMed ↵ Rossignol , S. , Dubuc , R. , & Gossard , J. P . ( 2006 ). Dynamic sensorimotor interactions in locomotion . Physiological Reviews , 86 , 89 – 154 . OpenUrl CrossRef PubMed Web of Science ↵ Rybak , I. A. , Stecina , K. , Shevtsova , N. A. , & McCrea , D. A . ( 2006 ). Modelling spinal circuitry involved in locomotor pattern generation: insights from the effects of afferent stimulation . Journal of Physiology , 577 , 641 – 658 . OpenUrl CrossRef PubMed Web of Science ↵ Rybak , I. A. , Dougherty , K. J. , & Shevtsova , N. A . ( 2015 ). Organization of the mammalian locomotor CPG: review of computational model and circuit architectures based on genetically identified spinal interneurons . eNeuro , 2 , 1 – 25 . OpenUrl CrossRef PubMed ↵ Shefchyk , S. , McCrea , D. , Kriellaars , D. , Fortier , P. , & Jordan , L . ( 1990 ). Activity of interneurons within the L4 spinal segment of the cat during brainstem-evoked fictive locomotion . Experimental Brain Research , 80 , 290 – 295 . OpenUrl CrossRef PubMed Web of Science ↵ Shevtsova , N. A. , Talpalar , A. E. , Markin , S. N. , Harris-Warrick , R. M. , Kiehn , O. , & Rybak , I. R . ( 2015 ). Organization of left–right coordination of neuronal activity in the mammalian spinal cord: insights from computational modelling . Journal of Physiology , 593 , 2403 – 2426 . OpenUrl CrossRef PubMed ↵ Shik , M. L. , Severin , F. V. , & Orlovsky , G. N . ( 1966 ). Control of walking and running by means of electrical stimulation of the mid-brain . Biophysics , 11 , 756 – 765 . OpenUrl Web of Science ↵ Shik , M. L. , & Orlovsky , G. N . ( 1976 ). Neurophysiology of locomotor automatism . Physiological Reviews , 56 , 465 – 501 . OpenUrl CrossRef PubMed Web of Science ↵ Strohmer , B. , Najarro , E. , Ausborn , J. , Berg , R. W. , & Tolu , S . ( 2024 ) Sparse Firing in a Hybrid Central Pattern Generator for Spinal Motor Circuits . Neural Computing , 36 ( 5 ), 759 – 780 . OpenUrl CrossRef ↵ Ting , L. H. , Chiel , H. J. , Trumbower , R. D. , Allen , J. L. , McKay , J. L. , Hackney , M. E ., Kesar , T. M. ( 2015 ). Neuromechanical principles underlying movement modularity and their implications for rehabilitation . Neuron , 86 , 38 – 54 . OpenUrl CrossRef PubMed ↵ Viala , D. , Viala , G. , & Jordan , M . ( 1991 ). Interneurones of the lumbar cord related to spontaneous locomotor activity in the rabbit . I. Rhythmically active interneurones. Experimental Brain Research , 84 , 177 – 186 . OpenUrl PubMed ↵ Whelan , P. J. , Hiebert , G. W. , & Pearson , K. G . ( 1995 ). Stimulation of the group I extensor afferents prolongs the stance phase in walking cats . Experimental Brain Research , 103 , 20 – 30 . OpenUrl CrossRef PubMed Web of Science ↵ Zelenin , P. V. , Deliagina , T. G. , Orlovsky , G. N. , Karayannidou , A. , Stout , E. E. , Sirota , M. G. , & Beloozerova , I. N . ( 2011 ). Activity of motor cortex neurons during backward locomotion . Journal of Neurophysiology , 106 , 2698 – 2714 . OpenUrl CrossRef PubMed Web of Science ↵ Zelenin , P. V. , Gorskii , O. V. , Gerasimenko , Y. P. , Deliagina , T. G. , & Musienko , P. E . ( 2019 ). Functional organization of the spinal locomotor network based on analysis of interneuronal activity . SfN Abstracts , 45 , 497 .14. OpenUrl ↵ Zhong , G. , Shevtsova N. A. , Rybak , I. , & Harris-Warrick , R. M . ( 2012 ). Neuronal activity in the isolated mouse spinal cord during spontaneous deletions in fictive locomotion: Insights into locomotor central pattern generator organization . The Journal of Physiology , 590 , 4735 – 4759 . OpenUrl CrossRef PubMed ↵ Zhu , M. , Yan , Y. , Cao , X. , Zeng , F. , Xu , G. , Shen , W. , Li , F. , Luo , L. , Wang , Z. , Zhang , Y. , Zhang , X. , Zhang , D. , & Liu , T . ( 2021 ) Electrophysiological and morphological features of rebound depolarization characterized interneurons in rat superficial spinal dorsal horn . Frontiers Cell Neuroscience , 15 , 736879 . OpenUrl CrossRef View the discussion thread. Back to top Previous Next Posted May 10, 2025. 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