publications | Saxena Lab

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2024

\textmuSim: A goal-driven framework for elucidating the neural control of movement through musculoskeletal modeling

Muhammad Noman Almani, John Lazzari, Andrea Chacon, and 1 more author

bioRxiv, 2024

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How does the motor cortex (MC) produce purposeful and generalizable movements from the complex musculoskeletal system in a dynamic environment? To elucidate the underlying neural dynamics, we use a goal-driven approach to model MC by considering its goal as a controller driving the musculoskeletal system through desired states to achieve movement. Specifically, we formulate the MC as a recurrent neural network (RNN) controller producing muscle commands while receiving sensory feedback from biologically accurate musculoskeletal models. Given this real-time simulated feedback implemented in advanced physics simulation engines, we use deep reinforcement learning to train the RNN to achieve desired movements under specified neural and musculoskeletal constraints. Activity of the trained model can accurately decode experimentally recorded neural population dynamics and single-unit MC activity, while generalizing well to testing conditions significantly different from training. Simultaneous goal- and data-driven modeling in which we use the recorded neural activity as observed states of the MC further enhances direct and generalizable single-unit decoding. Finally, we show that this framework elucidates computational principles of how neural dynamics enable flexible control of movement and make this framework easy-to-use for future experiments.Competing Interest StatementThe authors have declared no competing interest.

2023

Disentangled multi-subject and social behavioral representations through a constrained subspace variational autoencoder (CS-VAE)

Daiyao Yi, Simon Musall, Anne Churchland, and 2 more authors

Jul 2023

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Motor deficit and lack of overt dystonia in Dlx conditional Dyt1 knockout mice

David Berryman, Jake Barrett, Canna Liu, and 7 more authors

Behavioural Brain Research, Jul 2023

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DYT1 or DYT-TOR1A dystonia is early-onset generalized dystonia caused by a trinucleotide deletion of GAG in the TOR1A or DYT1 gene leads to the loss of a glutamic acid residue in the resulting torsinA protein. A mouse model with overt dystonia is of unique importance to better understand the DYT1 pathophysiology and evaluate preclinical drug efficacy. DYT1 dystonia is likely a network disorder involving multiple brain regions, particularly the basal ganglia. Tor1a conditional knockout in the striatum or cerebral cortex leads to motor deficits, suggesting the importance of corticostriatal connection in the pathogenesis of dystonia. Indeed, corticostriatal long-term depression impairment has been demonstrated in multiple targeted DYT1 mouse models. Pappas and colleagues developed a conditional knockout line (Dlx-CKO) that inactivated Tor1a in the forebrain and surprisingly displayed overt dystonia. We set out to validate whether conditional knockout affecting both cortex and striatum would lead to overt dystonia and whether machine learning-based video behavioral analysis could be used to facilitate high throughput preclinical drug screening. We generated Dlx-CKO mice and found no overt dystonia or motor deficits at 4 months. At 8 months, retesting revealed motor deficits in rotarod, beam walking, grip strength, and hyperactivity in the open field; however, no overt dystonia was visually discernible or through the machine learning-based video analysis. Consistent with other targeted DYT1 mouse models, we observed age-dependent deficits in the beam walking test, which is likely a better motor behavioral test for preclinical drug testing but more labor-intensive when overt dystonia is absent.
Region-based conversion of neural activity across sessions

Woohyun Eum, Carlton Smith, and Shreya Saxena

In 2023 11th International IEEE/EMBS Conference on Neural Engineering (NER), Jul 2023

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Neural Correlations Across Mice During Spontaneous and Task-Related Behaviors

Daiyao Yi, and Shreya Saxena

In 2023 11th International IEEE/EMBS Conference on Neural Engineering (NER), Jul 2023

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Unsupervised Human Activity Recognition Learning for Disassembly Tasks

Xinyao Zhang, Daiyao Yi, Sara Behdad, and 1 more author

IEEE Transactions on Industrial Informatics, Jul 2023

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Artificial neural network-based rapid predictor of biological nerve fiber activation for DBS applications

Justin Golabek, Matthew Schiefer, Joshua K Wong, and 2 more authors

Journal of Neural Engineering, Jan 2023

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Objective. Computational models are powerful tools that can enable the optimization of deep brain stimulation (DBS). To enhance the clinical practicality of these models, their computational expense and required technical expertise must be minimized. An important aspect of DBS models is the prediction of neural activation in response to electrical stimulation. Existing rapid predictors of activation simplify implementation and reduce prediction runtime, but at the expense of accuracy. We sought to address this issue by leveraging the speed and generalization abilities of artificial neural networks (ANNs) to create a novel predictor of neural fiber activation in response to DBS. Approach. We developed six variations of an ANN-based predictor to predict the response of individual, myelinated axons to extracellular electrical stimulation. ANNs were trained using datasets generated from a finite-element model of an implanted DBS system together with multi-compartment cable models of axons. We evaluated the ANN-based predictors using three white matter pathways derived from group-averaged connectome data within a patient-specific tissue conductivity field, comparing both predicted stimulus activation thresholds and pathway recruitment across a clinically relevant range of stimulus amplitudes and pulse widths. Main results. The top-performing ANN could predict the thresholds of axons with a mean absolute error (MAE) of 0.037 V, and pathway recruitment with an MAE of 0.079%, across all parameters. The ANNs reduced the time required to predict the thresholds of 288 axons by four to five orders of magnitude when compared to multi-compartment cable models. Significance. We demonstrated that ANNs can be fast, accurate, and robust predictors of neural activation in response to DBS.
Behavioral Classification of Sequential Neural Activity Using Time Varying Recurrent Neural Networks

Yongxu Zhang, Catalin Mitelut, David J. Arpin, and 3 more authors

bioRxiv, Jan 2023

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Shifts in data distribution across time can strongly affect early classification of time-series data. When decoding behavior from neural activity, early detection of behavior may help in devising corrective neural stimulation before the onset of behavior. Recurrent Neural Networks (RNNs) are common models for sequence data. However, standard RNNs are not able to handle data with temporal distributional shifts to guarantee robust classification across time. To enable the network to utilize all temporal features of the neural input data, and to enhance the memory of an RNN, we propose a novel approach: RNNs with time-varying weights, here termed Time-Varying RNNs (TV-RNNs). These models are able to not only predict the class of the time-sequence correctly but also lead to accurate classification earlier in the sequence than standard RNNs. In this work, we focus on early sequential classification of brain-wide neural activity across time using TV-RNNs applied to a variety of neural data from mice and humans, as subjects perform motor tasks. Finally, we explore the contribution of different brain regions on behavior classification using SHapley Additive exPlanation (SHAP) value, and find that the somatosensory and premotor regions play a large role in behavioral classification.Competing Interest StatementThe authors have declared no competing interest.

2022

Mesoscale cortex-wide neural dynamics predict self-initiated actions in mice several seconds prior to movement

Catalin Mitelut, Yongxu Zhang, Yuki Sekino, and 6 more authors

eLife, Nov 2022

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Volition – the sense of control or agency over one’s voluntary actions – is widely recognized as the basis of both human subjective experience and natural behavior in nonhuman animals. Several human studies have found peaks in neural activity preceding voluntary actions, for example the readiness potential (RP), and some have shown upcoming actions could be decoded even before awareness. Others propose that random processes underlie and explain pre-movement neural activity. Here, we seek to address these issues by evaluating whether pre-movement neural activity in mice contains structure beyond that present in random neural activity. Implementing a self-initiated water-rewarded lever-pull paradigm in mice while recording widefield [Ca++] neural activity we find that cortical activity changes in variance seconds prior to movement and that upcoming lever pulls could be predicted between 3 and 5 s (or more in some cases) prior to movement. We found inhibition of motor cortex starting at approximately 5 s prior to lever pulls and activation of motor cortex starting at approximately 2 s prior to a random unrewarded left limb movement. We show that mice, like humans, are biased toward commencing self-initiated actions during specific phases of neural activity but that the pre-movement neural code changes over time in some mice and is widely distributed as behavior prediction improved when using all vs. single cortical areas. These findings support the presence of structured multi-second neural dynamics preceding self-initiated action beyond that expected from random processes. Our results also suggest that neural mechanisms underlying self-initiated action could be preserved between mice and humans.
Motor cortex activity across movement speeds is predicted by network-level strategies for generating muscle activity

Shreya Saxena, Abigail A Russo, John Cunningham, and 1 more author

eLife, May 2022

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Learned movements can be skillfully performed at different paces. What neural strategies produce this flexibility? Can they be predicted and understood by network modeling? We trained monkeys to perform a cycling task at different speeds, and trained artificial recurrent networks to generate the empirical muscle-activity patterns. Network solutions reflected the principle that smooth well-behaved dynamics require low trajectory tangling. Network solutions had a consistent form, which yielded quantitative and qualitative predictions. To evaluate predictions, we analyzed motor cortex activity recorded during the same task. Responses supported the hypothesis that the dominant neural signals reflect not muscle activity, but network-level strategies for generating muscle activity. Single-neuron responses were better accounted for by network activity than by muscle activity. Similarly, neural population trajectories shared their organization not with muscle trajectories, but with network solutions. Thus, cortical activity could be understood based on the need to generate muscle activity via dynamics that allow smooth, robust control over movement speed.
@article{10.7554/eLife.67620, article_type = {journal}, title = {Motor cortex activity across movement speeds is predicted by network-level strategies for generating muscle activity}, author = {Saxena, Shreya and Russo, Abigail A and Cunningham, John and Churchland, Mark M}, editor = {Pruszynski, J Andrew and Gold, Joshua I}, volume = {11}, year = {2022}, month = may, pub_date = {2022-05-27}, pages = {e67620}, citation = {eLife 2022;11:e67620}, doi = {10.7554/eLife.67620}, url = {https://doi.org/10.7554/eLife.67620}, keywords = {motor cortex, speed, tangling, dynamical systems, neural networks, movement}, journal = {eLife}, issn = {2050-084X}, publisher = {eLife Sciences Publications, Ltd}, }
Neuroscience Cloud Analysis As a Service: An open-source platform for scalable, reproducible data analysis

Taiga Abe, Ian Kinsella, Shreya Saxena, and 8 more authors

Neuron, May 2022

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Summary A key aspect of neuroscience research is the development of powerful, general-purpose data analyses that process large datasets. Unfortunately, modern data analyses have a hidden dependence upon complex computing infrastructure (e.g., software and hardware), which acts as an unaddressed deterrent to analysis users. Although existing analyses are increasingly shared as open-source software, the infrastructure and knowledge needed to deploy these analyses efficiently still pose significant barriers to use. In this work, we develop Neuroscience Cloud Analysis As a Service (NeuroCAAS): a fully automated open-source analysis platform offering automatic infrastructure reproducibility for any data analysis. We show how NeuroCAAS supports the design of simpler, more powerful data analyses and that many popular data analysis tools offered through NeuroCAAS outperform counterparts on typical infrastructure. Pairing rigorous infrastructure management with cloud resources, NeuroCAAS dramatically accelerates the dissemination and use of new data analyses for neuroscientific discovery.
Next-generation brain observatories

Christof Koch, Karel Svoboda, Amy Bernard, and 13 more authors

Neuron, May 2022

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We propose centralized brain observatories for large-scale recordings of neural activity in mice and non-human primates coupled with cloud-based data analysis and sharing. Such observatories will advance reproducible systems neuroscience and democratize access to the most advanced tools and data.
Motor cortical influence relies on task-specific activity covariation

Claire L. Warriner, Samaher Fageiry, Shreya Saxena, and 2 more authors

Cell Reports, May 2022

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Summary During limb movement, spinal circuits facilitate the alternating activation of antagonistic flexor and extensor muscles. Yet antagonist cocontraction is often required to stabilize joints, like when loads are handled. Previous results suggest that these different muscle activation patterns are mediated by separate flexion- and extension-related motor cortical output populations, while others suggest recruitment of task-specific populations. To distinguish between hypotheses, we developed a paradigm in which mice toggle between forelimb tasks requiring antagonist alternation or cocontraction and measured activity in motor cortical layer 5b. Our results conform to neither hypothesis: consistent flexion- and extension-related activity is not observed across tasks, and no task-specific populations are observed. Instead, activity covariation among motor cortical neurons dramatically changes between tasks, thereby altering the relation between neural and muscle activity. This is also observed specifically for corticospinal neurons. Collectively, our findings indicate that motor cortex drives different muscle activation patterns via task-specific activity covariation.
Recurrent neural networks controlling musculoskeletal models predict motor cortex activity during novel limb movements

Muhammad Noman Almani, and Shreya Saxena

In 2022 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), May 2022

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Modeling the behavior of multiple subjects using a Cauchy-Schwarz regularized Partitioned Subspace Variational AutoEncoder (CS-PS-VAE)

Daiyao Yi, and Shreya Saxena

In 2022 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), May 2022

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2021

Uncovering the effect of different brain regions on behavioral classification using recurrent neural networks

Yongxu Zhang, Catalin Mitelut, Greg Silasi, and 4 more authors

In 2021 43rd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), May 2021

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2020

Localized semi-nonnegative matrix factorization (LocaNMF) of widefield calcium imaging data

Shreya Saxena, Ian Kinsella, Simon Musall, and 8 more authors

PLOS Computational Biology, May 2020

https://journals.plos.org/ploscompbiol/article/file?id=10.1371/journal.pcbi.1007791&type=printable

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Widefield calcium imaging enables recording of large-scale neural activity across the mouse dorsal cortex. In order to examine the relationship of these neural signals to the resulting behavior, it is critical to demix the recordings into meaningful spatial and temporal components that can be mapped onto well-defined brain regions. However, no current tools satisfactorily extract the activity of the different brain regions in individual mice in a data-driven manner, while taking into account mouse-specific and preparation-specific differences. Here, we introduce Localized semi-Nonnegative Matrix Factorization (LocaNMF), a method that efficiently decomposes widefield video data and allows us to directly compare activity across multiple mice by outputting mouse-specific localized functional regions that are significantly more interpretable than more traditional decomposition techniques. Moreover, it provides a natural subspace to directly compare correlation maps and neural dynamics across different behaviors, mice, and experimental conditions, and enables identification of task- and movement-related brain regions.
@article{pub.1126648831, author = {Saxena, Shreya and Kinsella, Ian and Musall, Simon and Kim, Sharon H. and Meszaros, Jozsef and Thibodeaux, David N. and Kim, Carla and Cunningham, John and Hillman, Elizabeth M. C. and Churchland, Anne and Paninski, Liam}, doi = {10.1371/journal.pcbi.1007791}, journal = {PLOS Computational Biology}, keywords = {}, note = {https://journals.plos.org/ploscompbiol/article/file?id=10.1371/journal.pcbi.1007791&type=printable}, number = {4}, pages = {e1007791}, title = {Localized semi-nonnegative matrix factorization (LocaNMF) of widefield calcium imaging data}, url = {https://app.dimensions.ai/details/publication/pub.1126648831}, volume = {16}, year = {2020}, }
Chronic, cortex-wide imaging of specific cell populations during behavior

João Couto, Simon Musall, Xiaonan R. Sun, and 8 more authors

Nature Protocols, May 2020

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Performance Limitations in Sensorimotor Control: Trade-Offs Between Neural Computation and Accuracy in Tracking Fast Movements

Shreya Saxena, Sridevi V. Sarma, and Munther Dahleh

Neural Computation, May 2020

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The ability to move fast and accurately track moving objects is fundamentally constrained by the biophysics of neurons and dynamics of the muscles involved. Yet the corresponding trade-offs between these factors and tracking motor commands have not been rigorously quantified. We use feedback control principles to quantify performance limitations of the sensorimotor control system (SCS) to track fast periodic movements. We show that (1) linear models of the SCS fail to predict known undesirable phenomena, including skipped cycles, overshoot and undershoot, produced when tracking signals in the “fast regime,” while nonlinear pulsatile control models can predict such undesirable phenomena, and (2) tools from nonlinear control theory allow us to characterize fundamental limitations in this fast regime. Using a validated and tractable nonlinear model of the SCS, we derive an analytical upper bound on frequencies that the SCS model can reliably track before producing such undesirable phenomena as a function of the neurons’ biophysical constraints and muscle dynamics. The performance limitations derived here have important implications in sensorimotor control. For example, if the primary motor cortex is compromised due to disease or damage, the theory suggests ways to manipulate muscle dynamics by adding the necessary compensatory forces using an assistive neuroprosthetic device to restore motor performance and, more important, fast and agile movements. Just how one should compensate can be informed by our SCS model and the theory developed here.

2019

Towards the neural population doctrine

Shreya Saxena, and John P Cunningham

Current Opinion in Neurobiology, May 2019

Machine Learning, Big Data, and Neuroscience

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Across neuroscience, large-scale data recording and population-level analysis methods have experienced explosive growth. While the underlying hardware and computational techniques have been well reviewed, we focus here on the novel science that these technologies have enabled. We detail four areas of the field where the joint analysis of neural populations has significantly furthered our understanding of computation in the brain: correlated variability, decoding, neural dynamics, and artificial neural networks. Together, these findings suggest an exciting trend towards a new era where neural populations are understood to be the essential unit of computation in many brain regions, a classic idea that has been given new life.
BehaveNet: nonlinear embedding and Bayesian neural decoding of behavioral videos

Eleanor Batty, Matthew Whiteway, Shreya Saxena, and 10 more authors

In Advances in Neural Information Processing Systems, May 2019

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Modulations in Oscillatory Activity of Globus Pallidus Internus Neurons During a Directed Hand Movement Task—A Primary Mechanism for Motor Planning

Shreya Saxena, Sridevi V. Sarma, Shaun R. Patel, and 3 more authors

Frontiers in Systems Neuroscience, May 2019

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Globus pallidus internus (GPi) neurons in the basal ganglia are traditionally thought to play a significant role in the promotion and suppression of movement via a change in firing rates. Here, we hypothesize that a primary mechanism of movement control by GPi neurons is through specific modulations in their oscillatory patterns. We analyzed neuronal spiking activity of 83 GPi neurons recorded from two healthy nonhuman primates executing a radial center-out motor task. We found that, in directionally tuned neurons, the power in the gamma band is significantly (p < 0.05) greater than that in the beta band (a “cross-over” effect), during the planning stages of movements in their preferred direction. This cross-over effect is not observed in the non-directionally tuned neurons. These data suggest that, during movement planning, information encoding by GPi neurons may be governed by a sudden emergence and suppression of oscillatory activities, rather than simply by a change in average firing rates.

2018

A novel variational family for hidden nonlinear markov models

Daniel Hernandez, Antonio Khalil Moretti, Ziqiang Wei, and 3 more authors

arXiv preprint arXiv:1811.02459, May 2018

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Reconstructing Neural Activity and Kinematics Using a Systems-Level Model of Sensorimotor Control

Shreya Saxena, Raina D’Aleo, Marc Schieber, and 2 more authors

In 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), May 2018

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2016

Weighted Population Code for low power neuromorphic image classification

Antonio Jimeno Yepes, Jianbin Tang, Shreya Saxena, and 2 more authors

In 2016 International Joint Conference on Neural Networks (IJCNN), May 2016

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2015

A systematic approach to selecting task relevant neurons

Kevin Kahn, Shreya Saxena, Emad Eskandar, and 6 more authors

Journal of Neuroscience Methods, May 2015

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Background Since task related neurons cannot be specifically targeted during surgery, a critical decision to make is to select which neurons are task-related when performing data analysis. Including neurons unrelated to the task degrade decoding accuracy and confound neurophysiological results. Traditionally, task-related neurons are selected as those with significant changes in firing rate when a stimulus is applied. However, this assumes that neurons’ encoding of stimuli are dominated by their firing rate with little regard to temporal dynamics. New method This paper proposes a systematic approach for neuron selection, which uses a likelihood ratio test to capture the contribution of stimulus to spiking activity while taking into account task-irrelevant intrinsic dynamics that affect firing rates. This approach is denoted as the model deterioration excluding stimulus (MDES) test. Results MDES is compared to firing rate selection in four case studies: a simulation, a decoding example, and two neurophysiology examples. Comparison with existing methods The MDES rankings in the simulation match closely with ideal rankings, while firing rate rankings are skewed by task-irrelevant parameters. For decoding, 95% accuracy is achieved using the top 8 MDES-ranked neurons, while the top 12 firing-rate ranked neurons are needed. In the neurophysiological examples, MDES matches published results when firing rates do encode salient stimulus information, and uncovers oscillatory modulations in task-related neurons that are not captured when neurons are selected using firing rates. Conclusions These case studies illustrate the importance of accounting for intrinsic dynamics when selecting task-related neurons and following the MDES approach accomplishes that. MDES selects neurons that encode task-related information irrespective of these intrinsic dynamics which can bias firing rate based selection.

2014

Analyzing the effect of an integrate and fire encoder and decoder in feedback

Shreya Saxena, and Munther Dahleh

In 53rd IEEE Conference on Decision and Control, May 2014

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Real-Time Decoding of an Integrate and Fire Encoder

Shreya Saxena, and Munther Dahleh

In Advances in Neural Information Processing Systems, May 2014

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2012

Aggregate Input-Output Models of Neuronal Populations

Shreya Saxena, Marc H. Schieber, Nitish V. Thakor, and 1 more author

IEEE Transactions on Biomedical Engineering, May 2012

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2011

Modulations in the oscillatory activity of the Globus Pallidus internus neurons during a behavioral task-A point process analysis

Shreya Saxena, John T. Gale, Emad N. Eskandar, and 1 more author

In 2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, May 2011

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2010

Point process models show temporal dependencies of basal ganglia nuclei under Deep Brain Stimulation

Shreya Saxena, Sabato Santaniello, Erwin B. Montgomery, and 2 more authors

In 2010 Annual International Conference of the IEEE Engineering in Medicine and Biology, May 2010

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2009

Measuring micromotion around a loaded hip stem using μCT imaging

M. Gortchacow, S. Saxena, M. Wettstein, and 2 more authors

Computer Methods in Biomechanics and Biomedical Engineering, May 2009

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