Biology subjects

Person, A. L.

Publications and source records attributed to Person, A. L..

11 recordsLinked to original sources

bioRxiv2025

Corrective sub-movements link feedback to feedforward control in the cerebellum

The ability to execute accurate movements is thought to rely on both anticipatory feedforward commands and rapid feedback corrections, yet how these control systems are integrated within cerebellar circuits remains unclear. Here, we show that corrective sub-movements (CSMs) - structured, feedback-driven adjustments occurring spontaneously during naturalistic mouse reaching - are not only encoded in anterior interposed (IntA) output neurons, but also act as instructive signals for feedforward learning. Closed-loop perturbations that trigger CSMs lead to learned shifts in the timing of future corrections, even in the absence of further perturbation. Strikingly, this learning depends on the timing of corrective responses, rather than the timing of the error itself, and is accompanied by physiological adaptation in cerebellar output neuronal firing rates. These findings reveal a cerebellar mechanism by which feedback responses train future anticipatory control signals, bridging the gap between reactive and anticipatory motor control in the cerebellum. Key Findings/ HighlightsO_LIMice generate rapid, precise corrective sub-movements (CSMs) during reach that counter both spontaneous variability and induced errors C_LIO_LICerebellar output neurons encode both predictive and corrective movements, mechanistically linking feedforward and feedback control C_LIO_LIClosed-loop optogenetic disruption of cerebellar output induced reach errors that were entirely compensated by CSMs C_LIO_LILearning was driven by the timing of corrective responses, not the timing of the initial error, suggesting an instructive role for CSMs C_LIO_LIBoth reach kinematics and cerebellar nuclear activity adapted over trials of repeated perturbation C_LIO_LIOptogenetic activation of cerebellar output drove errors and subsequent learning, but also blocked expression of that learning acutely, pinpointing cerebellar output as a key site of motor learning expression C_LIO_LIFindings suggest a neural mechanism by which feedback corrections influence learning of feedforward control policies in cerebellar circuits C_LI

neuroscience↗

bioRxiv2024

A novel biomechanical model of the mouse forelimb predicts muscle activity in optimal control simulations of reaching movements

Mice are key model organisms in neuroscience and motor systems physiology. Fine motor control tasks performed by mice have become widely used in assaying neural and biophysical motor system mechanisms. Although fine motor tasks provide useful insights into behaviors which require complex multi-joint motor control, there is no previously developed physiological biomechanical model of the adult mouse forelimb available for estimating kinematics nor muscle activity or kinetics during behaviors. Here, we developed a musculoskeletal model based on high-resolution imaging of the mouse forelimb that includes muscles spanning the neck, trunk, shoulder, and limbs. Physics-based optimal control simulations of the forelimb model were used to estimate in vivo muscle activity present when constrained to the tracked kinematics during reaching movements. The activity of a subset of muscles was recorded and used to assess the accuracy of the muscle patterning in simulation. We found that the synthesized muscle patterning in the forelimb model had a strong resemblance to empirical muscle patterning, suggesting that our model has utility in providing a realistic set of estimated muscle excitations over time when given a kinematic template. The strength of the similarity between empirical muscle activity and optimal control predictions increases as mice performance improves throughout learning of the reaching task. Our computational tools are available as open-source in the OpenSim physics and modeling platform. Our model can enhance research into limb control across broad research topics and can inform analyses of motor learning, muscle synergies, neural patterning, and behavioral research that would otherwise be inaccessible. NEW & NOTEWORTHYInvestigations into motor planning and execution lack an accurate and complete model of the forelimb, which could bolster or expand on findings. We sought to construct such a model using high detail scans of murine anatomy and prior research into muscle physiology. We then used the model to predict muscle excitations in a set of reaching movements and found that it provided accurate estimations and provided insight into an optimal-control framework of motor learning.

bioengineering↗

bioRxiv2023

A dual Purkinje cell rate and synchrony code sculpts reach kinematics

Cerebellar Purkinje cells (PCs) encode movement kinematics in their population firing rates. Firing rate suppression is hypothesized to disinhibit neurons in the cerebellar nuclei, promoting adaptive movement adjustments. Debates persist, however, about whether a second disinhibitory mechanism, PC simple spike synchrony, is a relevant population code. We addressed this question by relating PC rate and synchrony patterns recorded with high density probes, to mouse reach kinematics. We discovered behavioral correlates of PC synchrony that align with a known causal relationship between activity in cerebellar output. Reach deceleration was positively correlated with both Purkinje firing rate decreases and synchrony, consistent with both mechanisms disinhibiting target neurons, which are known to adjust reach velocity. Direct tests of the contribution of each coding scheme to nuclear firing using dynamic clamp, combining physiological rate and synchrony patterns ex vivo, confirmed that physiological levels of PC simple spike synchrony are highly facilitatory for nuclear firing. These findings suggest that PC firing rate and synchrony collaborate to exert fine control of movement.

neuroscience↗

bioRxiv2022

An emergent temporal basis set robustly supports cerebellar time-series learning

Learning plays a key role in the function of many neural circuits. The cerebellum is considered a learning machine essential for time interval estimation underlying motor coordination and other behaviors. Theoretical work has proposed that the cerebellums input recipient structure, the granule cell layer (GCL), performs pattern separation of inputs that facilitates learning in Purkinje cells (P-cells). However, the relationship between input reformatting and learning outcomes has remained debated, with roles emphasized for pattern separation features from sparsification to decorrelation. We took a novel approach by training a minimalist model of the cerebellar cortex to learn complex time-series data from naturalistic inputs, in contrast to traditional classification tasks. The model robustly produced temporal basis sets from naturalistic inputs, and the resultant GCL output supported learning of temporally complex target functions. Learning favored surprisingly dense granule cell activity, yet the key statistical features in GCL population activity that drove learning differed from those seen previously for classification tasks. Moreover, different cerebellar tasks were supported by diverse pattern separation features that matched the demands of the tasks. These findings advance testable hypotheses for mechanisms of temporal basis set formation and predict that population statistics of granule cell activity may differ across cerebellar regions to support distinct behaviors.

neuroscience↗

bioRxiv2021

Cerebellar associative learning underlies skilled reach adaptation

Cerebellar output has been shown to enhance movement precision by scaling the decelerative phase of reaching movements in mice. We hypothesized that during reach, initial kinematics cue late-phase adjustments through cerebellar associative learning. We identify a population-level response in mouse PCs that scales inversely with reach velocity, suggesting a candidate mechanism for anticipatory control to target limb endpoint. We next interrogate how such a response is generated by combining high-density neural recordings with closed-loop optogenetic stimulation of cerebellar mossy fiber afferents originating in the pontine nuclei during reach, using perturbation schedules reminiscent of classic adaptation paradigms. We found that reach kinematics and PC electrophysiology adapt to position-locked mossy fiber perturbations and exhibit aftereffects when stimulation is removed. Surprisingly, we observed partial adaptation to position-randomized stimulation schedules but no opposing aftereffect. A model that recapitulated these findings provided novel insight into how the cerebellum deciphers cause-and-effect relationships to adapt.

neuroscience↗

bioRxiv2021

Widespread inhibitory projections from the interposed cerebellar nucleus

The cerebellum consists of parallel parasagittal modules that contribute to diverse behaviors, spanning motor to cognitive. Recent work employing cell-type specific tracing has identified circumscribed output channels of the cerebellar nuclei that could confer tight functional specificity. These studies have largely focused on excitatory projections of the cerebellar nuclei, however, leaving open the question of whether inhibitory neurons also constitute multiple output modules. We mapped output and input patterns to intersectionally restricted cell types of the interposed and adjacent interstitial nuclei. In contrast to the widespread assumption of primarily excitatory outputs and restricted inferior olive-targeting inhibitory output, we found that inhibitory neurons from this region ramified widely within the brainstem, targeting both motor- and sensory-related nuclei, distinct from excitatory output targets. Despite differences in output targeting, monosynaptic rabies tracing revealed largely shared afferents to both cell classes. We discuss the potential novel functional roles for inhibitory outputs in the context of cerebellar theory.

neuroscience↗

bioRxiv2020

QUANTIFICATION OF MOUSE REACH KINEMATICS AS A FOUNDATION FOR MECHANISTIC INTERROGATION OF MOTOR CONTROL

Mice use reaching movements to grasp and manipulate objects in their environment, similar to primates. Thus, many recent studies use mouse reach to uncover neural control mechanisms, but quantification of mouse reach kinematics remains lacking, limiting understanding. Here we implement several analytical frameworks, from basic kinematic relationships to statistical machine learning, to quantify mouse reach kinematics across freely-behaving and head-fixed conditions. Overall, we find that many canonical features of primate reaches are conserved in mice, with some notable differences. Our results highlight the decelerative phase of reach as important in driving successful outcome. Late-phase kinematic adjustments are yoked to mid-flight position and velocity of the limb, allowing dynamic correction of initial variability, with head-fixed reaches being less dependent on position. Furthermore, consecutive reaches exhibit positional error-correction but not hot-handedness, implying opponent regulation of motor variability. Overall, our results establish foundational mouse reach kinematics in the context of neuroscientific investigation.

neuroscience↗

bioRxiv2019

MONOSYNAPTIC INPUTS TO EXCITATORY AND INHIBITORY NEURONS OF THE INTERMEDIATE AND DEEP LAYERS OF THE SUPERIOR COLLICULUS

The intermediate and deep layers of the midbrain superior colliculus (SC) are a key locus for several critical functions, including spatial attention, multisensory integration and behavioral responses. While the SC is known to integrate input from a variety of brain regions, progress in understanding how these inputs contribute to SC-dependent functions has been hindered by the paucity of data on innervation patterns to specific types of SC neurons. Here, we use G-deleted rabies virus-mediated monosynaptic tracing to identify inputs to excitatory and inhibitory neurons of the intermediate and deep SC. We observed stronger and more numerous projections to excitatory than inhibitory SC neurons. However, a subpopulation of excitatory neurons thought to mediate behavioral output received weaker inputs, from far fewer brain regions, than the overall population of excitatory neurons. Additionally, extrinsic inputs tended to target rostral excitatory and inhibitory SC neurons more strongly than their caudal counterparts, and commissural SC neurons tended to project to similar rostrocaudal positions in the other SC. Our findings support the view that active intrinsic processes are critical to SC-dependent functions, and will enable the examination of how specific inputs contribute to these functions.

neuroscience↗

bioRxiv2018

Pathway specific drive of cerebellar Golgi cells reveals integrative rules of cortical inhibition

Cerebellar granule cells (GrCs) constitute over half of all neurons in the vertebrate brain and are proposed to decorrelate convergent mossy fiber inputs in service of learning. Interneurons within the granule cell layer, Golgi cells (GoCs), are the primary inhibitors of this vast population and therefore play a major role in influencing the computations performed within the layer. Despite this central function for GoCs, few studies have directly examined how GoCs integrate inputs from specific afferents which vary in density to regulate GrC population activity. We used a variety of methods in mice of either sex to study feedforward inhibition recruited by identified MFs, focusing on features that would influence integration by GrCs. Comprehensive 3D reconstruction and quantification of GoC axonal boutons revealed tightly clustered boutons that focus feedforward inhibition in the neighborhood of GoC somata. Acute whole cell patch clamp recordings from GrCs in brain slices showed that despite high bouton density, fast phasic inhibition was very sparse relative to slow spillover mediated inhibition. Furthermore, dynamic clamp simulating inhibition combined with optogenetic mossy fiber activation supported the predominant role of slow spillover mediated inhibition. Whole cell recordings from GoCs revealed a role for the density of active MFs in preferentially driving them. Thus, our data provide empirical conformation of predicted rules by which MFs activate GoCs to regulate GrC activity levels.

neuroscience↗

bioRxiv2018

Graded and bidirectional control of real-time reach kinematics by the cerebellum

The rules governing the relationship between cerebellar output and movement production remain unknown despite the well-recognized importance of the cerebellum in motor learning and precision. In this study, we investigated how cerebellar output sculpts reach behavior in mice by manipulating neural activity in the anterior interposed nucleus (IntA) in closed-loop with ongoing behavior. Optogenetic modulation of cerebellar output revealed monotonically graded and bidirectional control of real-time reach velocity by IntA. Furthermore, kinematic effects were relatively context invariant, suggesting that cerebellar output summates with ongoing motor commands generated elsewhere throughout the reaching movement. These results characterize the relationship between cerebellar output modulation and reach behavior as a bidirectional and scalable kinematic command signal. Our findings illustrate how learned, predictive coding in the cerebellar cortex could be actuated through the cerebellar nuclei to contribute in real time to purposive motor control.

neuroscience↗

bioRxiv2017

Morphological constraints on cerebellar granule cell combinatorial diversity

Combinatorial expansion by the cerebellar granule cell layer (GCL) is fundamental to theories of cerebellar contributions to motor control and learning. Granule cells sample approximately four mossy fiber inputs and are thought to form a combinatorial code useful for pattern separation and learning. We constructed a spatially realistic model of the cerebellar granule cell layer and examined how GCL architecture contributes to granule cell (GrC) combinatorial diversity. We found that GrC combinatorial diversity saturates quickly as mossy fiber input diversity increases, and that this saturation is in part a consequence of short dendrites, which limit access to diverse inputs and favor dense sampling of local inputs. This local sampling also produced GrCs that were combinatorially redundant, even when input diversity was extremely high. In addition, we found that mossy fibers clustering, which is a common anatomical pattern, also led to increased redundancy of GrC input combinations. We related this redundancy to hypothesized roles of temporal expansion of GrC information encoding in service of learned timing, and show that GCL architecture produces GrC populations that support both temporal and combinatorial expansion. Finally, we used novel anatomical measurements from mice of either sex to inform modeling of sparse and filopodia-bearing mossy fibers, finding that these circuit features uniquely contribute to enhancing GrC diversification and redundancy. Our results complement information theoretic studies of granule layer structure and provide insight into the contributions of granule layer anatomical features to afferent mixing.\n\nSignificance StatementCerebellar granule cells are among the simplest neurons, with tiny somata and on average just four dendrites. These characteristics, along with their dense organization, inspired influential theoretical work on the granule cell layer (GCL) as a combinatorial expander, where each granule cell represents a unique combination of inputs. Despite the centrality of these theories to cerebellar physiology, the degree of expansion supported by anatomically realistic patterns of inputs is unknown. Using modeling and anatomy, we show that realistic input patterns constrain combinatorial diversity by producing redundant combinations, which nevertheless could support temporal diversification of like-combinations, suitable for learned timing. Our study suggests a neural substrate for producing high levels of both combinatorial and temporal diversity in the GCL.

neuroscience↗