Biology subjects

Gilmer, J. I.

Publications and source records attributed to Gilmer, J. I..

6 recordsLinked to original sources

bioRxiv2026

A Physiologically Detailed Biomechanical Model of the Mouse Distal Forelimb for Simulation of Fine Motor Control

This study presents a physiologically detailed biomechanical model of the mouse distal forelimb that incorporates intrinsic musculature, tendon routing, and digit-level skeletal anatomy, features simplified or omitted in existing musculoskeletal models. Using high-resolution anatomical reconstruction and computational modeling, we created a physiological representation of the wrist and digits capable of simulating complex forelimb movements. The model enables simulation of coordinated distal forelimb movement and digit-level muscle behavior during grasping-related tasks. Simulations were performed for multiple tasks, including grasping, grasping with supination, wrist flexion, and digit I flexion, with analysis focused on the grasping task due to its integration of both intrinsic and extrinsic musculature. Model performance was evaluated through comparisons of marker trajectories between torque-driven reference motion and muscle-driven simulations, temporal shuffle control, and comparisons between experimentally recorded electromyography (EMG) activity and model-predicted muscle excitation profiles. The model successfully reproduced coordinated distal forelimb kinematics, demonstrated strong agreement between torque-driven and muscle-driven simulation approaches, and generated physiologically plausible muscle excitation patterns consistent with experimentally observed EMG activity during grasping-related movement. These findings establish the model as a framework for studying fine motor control, neuromuscular coordination, and movement-related impairments in mice while providing a foundation for future investigation of neurological disorders and their underlying biomechanical mechanisms.

bioengineering↗

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↗

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↗

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↗

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↗