We recorded from over 280 single cortical neurons throughout the medial anterior lobe of the cat cerebellum during passive movements of the hindlimbs resembling stepping on a moving treadmill. We used three stepping patterns, unilateral stepping of either the ipsilateral or contralateral leg and bipedal stepping in an alternating gait pattern. We found that over 60% of the neurons, mostly Purkinje cells, responded to stepping of one or both legs, and over 40% to more than one type of stepping pattern. Responsive cells were distributed throughout the five anterior lobules, and the highest concentration was found in traditional hindlimb areas in lobules 2 and 3. A comparison of response waveforms showed that they are similar for neighboring cells in many parts of the cerebellar cortex, and they tend to form local blob-like groupings. Response patterns, i.e., relationship among responses to each stepping type, tended to be similar within a local group. The groupings extend further in the parasagittal dimension (up to about a third of a lobule) than in the transverse dimension (about 1 mm), and they may form functional modules. A principal component analysis also showed that the responses were composed of a four basis waveforms (principal components) that explained about 80% of the response waveform variance that were nearly identical to those derived from dorsal spinocerebellar tract (DSCT) responses to similar stepping movements. We reconstructed the locations of the recorded neurons on a 2D map of the cerebellar cortex showing the spatial distribution of responsive cells according to their response characteristics. We propose, on the basis of these results, that the sensory input to the cerebellum from the hindlimbs is distributed to multiple zones that may each contribute to a different component of cerebellar function.

We recorded from over 250 single cortical neurons throughout the medial anterior lobe of the cat cerebellum during passive movements of the ipsilateral hindlimb resembling stepping on a moving treadmill. We applied three different quantitative analysis techniques to determine the extent of neuronal modulation that could be accounted for by the stepping movements. The analyses all indicated that up to half the recorded neurons in all five lobules responded to these passive hindlimb movements. We reconstructed the locations of the recorded neurons on a 2-D map of the cerebellar cortex in order to determine the spatial distribution of responsive cells. Cells that were located in the classical hindlimb projection areas of the anterior lobe (in lobules 2 and 3) were generally most responsive to the limb movement with activity patterns that generally had a linear relationship to hindlimb kinematics. Cells in lobules 4 and 5, considered as classical forelimb areas of the cerebellum, were also responsive. Although these cells tended to have noisier firing patterns, many were found to be modulated nevertheless by the hindlimb movements. We also found a clear demarcation between zones b and c, with a higher fraction of responsive cells in all lobules located in zone b.

The dorsal spinocerebellar tract (DSCT) provides a major mossy fiber input to the spinocerebellum, which plays a significant role in the control of posture and locomotion. Recent work from our laboratory has provided evidence that DSCT neurons encode a global representation of hindlimb mechanics during passive limb movements. The framework that most successfully accounts for passive DSCT behavior is kinematics-based having the coordinates of the limb axis, limb-axis length and orientation. Here we examined the responses of DSCT neurons in decerebrate cats as they walked on a moving treadmill and compared them with the responses passive step-like movements of the hindlimb produced manually. We found that DSCT responses to active locomotion were quantitatively different from the responses to kinematically similar passive limb movements on the treadmill. The differences could not be simply accounted for by the difference in limb-axis kinematics in the two conditions, nor could they be accounted for by new or different response components. Instead, differences could be attributed to an increased relative prominence of specific response components occurring during the stance phase of active stepping, which may reflect a difference in the behavior of the sensory receptors and/or of the DSCT circuitry during active stepping. We propose from these results that DSCT neurons encode two global aspects of limb mechanics that are also important in controlling locomotion at the spinal level, namely the orientation angle of the limb axis and limb loading. Although limb-axis length seemed to be an independent predictor of DSCT activity during passive limb movements, we argue that it is not independent of limb loading, which is likely to be proportional to limb length under passive conditions.

Previous work from this laboratory has shown that activity in the dorsal spinocerebellar tract (DSCT) relates strongly to global hindlimb kinematics variables during passive displacements of the hindlimb. A linear relationship to limb axis orientation and length variables accounts for most of the response variance for passive limb positioning and movement. Here we extend those observations to more natural movements by examining the information carried by the DSCT during passive stepping movements on a treadmill, and we compare it to information transmitted during passive robot-driven hindlimb movements. Using a principal component analysis approach, we found that a linear relationship between the responses and hindlimb kinematics was comparable across experimental conditions. We also observed systematic non-linearities in this relationship for both types of movement that could be attributed to events corresponding to the touch-down and lift-off phases of the movement. We concluded that proprioceptive information transmitted to the cerebellum by the DSCT during locomotion has at least two major components. One component is associated with limb kinematics (limb orientation) and may be more or less related to the metrics of the step (stride length, for example) or its velocity. The other component is associated with limb length and/or limb loading, and it may signal some aspect of limb stiffness.

Proprioceptive sensory information carried by spinocerebellar tracts provides a major input to the spinocerebellum, which has an important role in coordinating motor output for posture and locomotion. Until recently it was assumed that the information transmitted by the dorsal spinocerebellar tract (DSCT) was organized to represent single muscles or single joints in the ipsilateral hindlimb. Recent studies have shown, however, that DSCT activity represents global kinematic parameters of the hindlimb. We now present evidence that the DSCT neurons are also modulated by passive step-like movements of either hindlimb, implying they receive a bilateral sensory input. About two-thirds of 78 cells studied had significant responses to movements of the contralateral limb alone and about 70% responded differently to bipedal movements than to ipsilateral movement alone. The same basic behavior was observed in anesthetized, paralyzed cats and in unanesthetized, decerebrate cats, although decerebrate cats may have had larger responses on average. The results suggest that many DSCT cells may encode information about interlimb coordination.

We describe a novel method for recovering the three-dimensional (3D) point geometry of an object from images acquired with a single-camera. Typically, multiple cameras are used to record 3D geometry. Occasionally, however, there is a need to record 3D geometry when the use of multiple cameras is either too costly or impractical. The algorithm described here uses single-camera images and requires in addition that each marker on the object be linked to at least one other marker by a known distance. The linkage distances are used to recover information about the third dimension that would otherwise be lost in single-camera two-dimensional images. The utilities of the method are its low-cost, simplicity, and ease of calibration and implementation. We were able to estimate 3D distances and positions as accurately as with a commercially available multi-camera 3D system. This method may be useful in pilot studies to determine whether 3D imaging systems are required, or, it can serve as a low-cost alternative to multi-camera systems.

Many sensorimotor neurons in the CNS encode global parameters of limb movement and posture rather than specific muscle or joint parameters. Our investigations of spinocerebellar activity have demonstrated that these second-order spinal neurons also may encode proprioceptive information in a limb-based rather than joint-based reference frame. However, our finding that each foot position was determined by a unique combination of joint angles in the passive limb made it difficult to distinguish unequivocally between a limb-based and a joint-based representation. In this study, we decoupled foot position from limb geometry by applying mechanical constraints to individual hindlimb joints in anesthetized cats. We quantified the effect of the joint constraints on limb geometry by analyzing joint-angle covariance in the free and constrained conditions. One type of constraint, a rigid constraint of the knee angle, both changed the covariance pattern and significantly reduced the strength of joint-angle covariance. The other type, an elastic constraint of the ankle angle, changed only the covariance pattern and not its overall strength. We studied the effect of these constraints on the activity in 70 dorsal spinocerebellar tract (DSCT) neurons using a multivariate regression model, with limb axis length and orientation as predictors of neuronal activity. This model also included an experimental condition indicator variable that allowed significant intercept or slope changes in the relationships between foot position parameters and neuronal activity to be determined across conditions. The result of this analysis was that the spatial tuning of 37/70 neurons (53%) was unaffected by the constraints, suggesting that they were somehow able to signal foot position independently from the specific joint angles. We also investigated the extent to which cell activity represented individual joint angles by means of a regression model based on a linear combination of joint angles. A backward elimination of the insignificant predictors determined the set of independent joint angles that best described the neuronal activity for each experimental condition. Finally, by comparing the results of these two approaches, we could determine whether a DSCT neuron represented foot position, specific joint angles, or none of these variables consistently. We found that 10/70 neurons (14%) represented one or more specific joint-angles. The activity of another 27 neurons (39%) was significantly affected by limb geometry changes, but 33 neurons (47%) consistently elaborated a foot position representation in the coordinates of the limb axis.