Biology subjects

Kaufman, M. T.

Publications and source records attributed to Kaufman, M. T..

12 recordsLinked to original sources

bioRxiv2026

Flexible reorientation of conserved neural dynamics underlies grasp control

Grasping objects is a complex behavior requiring high-dimensional hand control and dynamic interaction with the environment, yet its neural mechanisms remain poorly understood. Dynamical systems approaches have provided key insights into how neural populations generate control signals for arm movements such as reaching and cycling but have proven insufficient to account for neural population activity during grasping. Here, we re-analyzed multi-area neural recordings from rhesus monkeys performing a reach-to-grasp task to many objects and found two key population-level features. First, the component of the population trajectory that was common to all grasps formed a small angle with the subspace containing grasp condition-specific (object) tuning. Second, the neural activity was well fitted by a recent, flexible dynamical model developed for reaching, called location-dependent rotations (LDR). Neural activity in M1 and F5 during grasping exhibited conserved population-level rotational frequencies, but the state-space planes in which these rotations occurred varied systematically with grasp condition. The rotational center (the location) and the orientation of the rotations related both to each other and to the kinematics of movement. While these rotations were reoriented in high dimensional space, reflecting the high-dimensional nature of hand control, the extent of their tilt into additional dimensions were small, in contrast to reaching. This structure may reflect the nature of grasping as modulations of an overall open-close motif. Together, these results indicate that the LDR dynamics framework applies to grasping as well as reaching and provides an entry point to understanding how grasp commands are generated.

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bioRxiv2025

Routing of task-relevant information in mouse PPC during continuousvisuomotor control

Posterior Parietal Cortex (PPC) exhibits tuning to many variables, including strong representations of visual information, movement, and behavioral biases. Whether PPC communicates all these variables to other areas is less clear. We examined PPC activity in mice performing a novel, closed-loop, 2D visuomotor joystick task that required animals to act exclusively on a task-relevant axis of visual motion. To determine what components of PPCs representation were sent to M1, we performed two-photon calcium imaging of layer 2/3 neurons in contralateral PPC of expert mice with PPC-M1 projection neurons identified via retrograde tracing. Consistent with previous results, PPC neurons exhibited random mixed selectivity and were typically most strongly modulated by joystick movement. Most of the visually responsive neurons were more strongly modulated by task-relevant than task-irrelevant visual motion. Encoding in labeled PPC-M1 neurons was similar to encoding in unlabeled neurons, with one major exception: unlike the task-relevant visual enrichment in unlabeled PPC neurons, task-relevant and task-irrelevant visual motion were encoded at similarly weak levels in PPC-M1 neurons. This argues that although PPC encodes a mix of visual, movement and other information, the PPC-M1 pathway is dominated by movement information and does not propagate PPCs learned enrichment of task-relevant visual signals.

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bioRxiv2025

Neural activity profiles reveal overlapping, intermingled subpopulations spanning area borders in mouse sensorimotor cortex

Cortical control of movement is a distributed computation spanning multiple densely-interconnected regions. Although we have rich anatomical atlases and a coarse understanding of how function maps to areas and subregions, we lack a detailed account of how behaviorally-relevant activity is organized across the cortical sheet. Here, we trained head-fixed mice to perform a 15-target reach-to-grasp task while we performed cellular-resolution, two-photon calcium imaging across five regions of sensorimotor cortex (>39,000 layer 2/3 neurons). We characterized each neurons trial-averaged peri-event activity with interpretable metrics and mapped these response properties across areas, revealing large-scale spatial structure. Neuronal response profiles often shifted abruptly at anatomical borders: motor areas showed sharper tuning and more linear relationships with target location, whereas somatosensory areas displayed more heterogeneous response patterns. Neural response properties also differed according to somatotopic representation. Nonlinear dimensionality reduction of the neural feature matrix revealed that areas varied in their average response profiles, but that areas did not have well-separated feature distributions; instead, each area contained subpopulations. Neurons in each subpopulation had characteristic response profiles and were distributed across multiple cortical areas. The spatial distributions of the subpopulations overlapped, with neurons from different subpopulations salt-and-pepper intermingled in the overlap zones. Together, these results describe novel activity structure across sensorimotor cortex and identify several distinct but spatially-overlapping subpopulations with characteristic activity patterns during reach-to-grasp behavior.

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bioRxiv2025

Multiple scales of coordination along the body axis during Drosophila larval locomotion

Coordinated movement along the body axis is critical to locomotion. In segmented, limbless animals, anterior (head) and posterior (tail) segments play different roles in locomotion, leading to a need for flexible coordination across body regions. Larval Drosophila melanogaster present a tractable experimental model for limbless, segmented crawling given the extensive genetic tools available and the optical clarity of the body. Prior work has suggested that, during crawling, all larval body segments contract similarly, despite the fact that each crawl cycle comprises two overlapping phases: a piston involving the most posterior segments, and a peristaltic wave involving all body segments. To test whether coordination varies regionally during locomotion, we expressed GCaMP in body wall muscles of larvae of either sex, and recorded segmental contraction kinematics and muscle recruitment during many cycles of locomotion in linear channels. Facilitated by machine vision techniques, we discovered new features of larval crawling at multiple scales. First, the propagation of both contraction and recruitment waves slowed approaching mid-body segments, then sped up towards the head. Second, the timing relationship between contraction and recruitment waves could be highly variable in anterior segments. Third, contraction durations showed particularly strong intersegmental correlations among posterior segments. These data suggest posterior segments coordinate to power the piston phase while anterior segments tolerate greater flexibility to enable reorienting behaviors. Our results depict an unanticipated degree of axial heterogeneity in the coordination of limbless crawling, opening new avenues to study the origins of whole-body coordination and the consequences of segmental diversity for locomotion. SIGNIFICANCE STATEMENTMost animals move through the world by coordinating muscle contractions across the bodys many segments. Studying how segments are coordinated in limbless animals can uncover principles that are fundamental for movement across the animal kingdom. However, in one of the best genetic model organisms to address this question, the larval fruit fly, it was unknown how the nearly identical body segments are coordinated across the body. We used machine vision techniques to discover an unanticipated degree of heterogeneity: the timings of muscle activity and movements are more coordinated among tail-end than head-end segments. This provides new insights into animal movements, and opens opportunities to study how interactions of the body and nervous system coordinate segmental movements across an entire animal.

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bioRxiv2022

Microstimulation of human somatosensory cortex evokes task-dependent, spatially patterned responses in motor cortex

AO_SCPLOWBSTRACTC_SCPLOWMotor (M1) and somatosensory (S1) cortex play a critical role in motor control but the nature of the signaling between these structures is not known. To fill this gap, we recorded - in three human participants whose hands were paralyzed as a result of a spinal cord injury - the responses evoked in the hand and arm representations of primary motor cortex (M1) while we delivered ICMS to the somatosensory cortex (S1). We found that ICMS of S1 activated some M1 neurons at short, fixed latencies, locked to each pulse in a manner consistent with monosynaptic activation. However, most of the changes in M1 firing rates were much more variable in time, suggesting a more indirect effect of the stimulation. The spatial pattern of M1 activation varied systematically depending on the stimulating electrode: S1 electrodes that elicited percepts at a given hand location tended to activate M1 neurons with movement fields at the same location. However, the indirect effects of S1 ICMS on M1 were strongly context dependent, such that the magnitude and even sign relative to baseline varied across tasks. We tested the implications of these effects for brain-control of a virtual hand, in which ICMS was used to convey tactile feedback about object interactions. While ICMS-evoked activation of M1 disrupted decoder performance, this disruption could be minimized with biomimetic stimulation, which emphasizes contact transients at the onset and offset of grasp, reduces sustained stimulation, and has been shown to convey useful contact-related information. SO_SCPLOWIGNIFICANCEC_SCPLOWMotor (M1) and somatosensory (S1) cortex play a critical role in motor control but the nature of the signaling between these structures is not known. To fill this gap, we recorded from M1 while delivering intracortical microstimulation (ICMS) to S1 of three human participants, whose hands were paralyzed by spinal cord injury. We found that ICMS activates M1 and that the motor fields of activated M1 neurons match the sensory fields of the stimulated S1 electrodes. These findings have important implications for using ICMS to convey tactile feedback for brain-controlled bionic hands. Indeed, the ICMS-evoked M1 activity worsens control of the hand. Fortunately, this effect is minimized by using biomimetic tactile feedback, which emphasizes contact transients and reduces sustained ICMS.

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bioRxiv2021

A deep learning framework for inference of single-trial neural population activity from calcium imaging with sub-frame temporal resolution

In many brain areas, neural populations act as a coordinated network whose state is tied to behavior on a moment-by-moment basis and millisecond timescale. Two-photon (2p) calcium imaging is a powerful tool to probe network-scale computation, as it can measure the activity of many individual neurons, monitor multiple cortical layers simultaneously, and sample from identified cell types. However, estimating network state and dynamics from 2p measurements has proven challenging because of noise, inherent nonlinearities, and limitations on temporal resolution. Here we describe RADICaL, a deep learning method to overcome these limitations at the population level. RADICaL extends methods that exploit dynamics in spiking activity for application to deconvolved calcium signals, whose statistics and temporal dynamics are quite distinct from electrophysiologically-recorded spikes. It incorporates a novel network training strategy that capitalizes on the timing of 2p sampling to recover network dynamics with high temporal precision. In synthetic tests, RADICaL infers network state more accurately than previous methods, particularly for high-frequency components. In real 2p recordings from sensorimotor areas in mice performing a "water grab" task, RADICaL infers network state with close correspondence to single-trial variations in behavior, and maintains high-quality inference even when neuronal populations are substantially reduced.

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bioRxiv2021

M1 dynamics share similar inputs for initiating and correcting movement

Primary motor cortex (M1) plays a central role in voluntary movement, but how it integrates sensory-driven corrective instructions is unclear. We analyzed population activity recorded from M1 of macaques during a sequential arm movement task with target updates requiring online adjustments to the motor plan. Using Latent Factor Analysis via Dynamical Systems (LFADS), we separated neural activity into two components: intrinsic dynamics and inferred external inputs influencing those dynamics. Inferred input timing was more strongly locked to target appearance than to movement onset, suggesting that variable reaction times reflect interactions between inputs and ongoing dynamics. Inferred inputs were tuned similarly for both initial and corrective movements, suggesting a shared input encoding across visually-instructed and corrective movements that was previously obscured by M1 dynamics. Because input inference can suffer from the challenge of nonidentifiability, where different models fit the data indistinguishably, we used ensembles of models with varied hyperparameters to diagnose when inputs are identifiable or nonidentifiable. In the monkey data, ensembles produced consistently similar results, suggesting that inputs could be meaningfully inferred and that their encoding was not simply a result of model bias. These results highlight the challenges of nonidentifiability and the potential of model ensembles to identify inputs in ongoing dynamics, at least in some cases.

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bioRxiv2021

A curved manifold orients rotational dynamics in motor cortex

During reaching, neurons in motor cortex exhibit complex, time-varying activity patterns. Though single-neuron activity correlates with movement parameters, movement correlations explain neural activity only partially. Neural responses also reflect population-level dynamics thought to generate outputs. These dynamics have previously been described as "rotational", such that activity orbits in neural state space. Here, we find two essential features previously missed. First, the planes in which rotations occur differ for different reaches. Second, this variation in planes reflects the overall location of activity in neural state space. Our "location-dependent rotations" model fits nearly all motor cortex activity during reaching and enables higher-quality decoding of reach kinematics from single-trial spiking. Varying rotational planes allows motor cortex to more simply produce richer outputs than possible under previous models. Finally, our model links representational and dynamical ideas: a representation-like signal is present as the state space location, which dynamics then convert into time-varying command signals.

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bioRxiv2019

NEURAL POPULATION DYNAMICS IN MOTOR CORTEX ARE DIFFERENT FOR REACH AND GRASP

AO_SCPLOWBSTRACTC_SCPLOWRotational dynamics are observed in neuronal population activity in primary motor cortex (M1) when monkeys make reaching movements. This population-level behavior is consistent with a role for M1 as an autonomous pattern generator that drives muscles to produce movement. Here, we show that M1 does not exhibit smooth dynamics during grasping movements, suggesting a more input-driven circuit.

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bioRxiv2018

High-throughput mapping of mesoscale connectomes in individual mice

Comprehensive analysis of neuronal networks requires brain-wide measurement of connectivity, activity, and gene expression. Although high-throughput methods are available for mapping brain-wide activity and transcriptomes, comparable methods for mapping region-to-region connectivity remain slow and expensive because they require averaging across hundreds of brains. Here we describe BRICseq, which leverages DNA barcoding and sequencing to map connectivity from single individuals in a few weeks and at low cost. Applying BRICseq to the mouse neocortex, we find that region-to-region connectivity provides a simple bridge relating transcriptome to activity: The spatial expression patterns of a few genes predict region-to-region connectivity, and connectivity predicts activity correlations. We also exploited BRICseq to map the mutant BTBR mouse brain, which lacks a corpus callosum, and recapitulated its known connectopathies. BRICseq allows individual laboratories to compare how age, sex, environment, genetics and species affect neuronal wiring, and to integrate these with functional activity and gene expression.

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bioRxiv2018

Movement-related activity dominates cortex during sensory-guided decision making

When experts are immersed in a task, do their brains prioritize task-related activity? Most efforts to understand neural activity during well-learned tasks focus on cognitive computations and specific task-related movements. We wondered whether task-performing animals explore a broader movement landscape, and how this impacts neural activity. We characterized movements using video and other sensors and measured neural activity using widefield and two-photon imaging. Cortex-wide activity was dominated by movements, especially uninstructed movements, reflecting unknown priorities of the animal. Some uninstructed movements were aligned to trial events. Accounting for them revealed that neurons with similar trial-averaged activity often reflected utterly different combinations of cognitive and movement variables. Other movements occurred idiosyncratically, accounting for trial-by-trial fluctuations that are often considered \"noise\". This held true for extracellular Neuropixels recordings in cortical and subcortical areas. Our observations argue that animals execute expert decisions while performing richly varied, uninstructed movements that profoundly shape neural activity.

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bioRxiv2017

Inferring single-trial neural population dynamics using sequential auto-encoders

Neuroscience is experiencing a data revolution in which simultaneous recording of many hundreds or thousands of neurons is revealing structure in population activity that is not apparent from single-neuron responses. This structure is typically extracted from trial-averaged data. Single-trial analyses are challenging due to incomplete sampling of the neural population, trial-to-trial variability, and fluctuations in action potential timing. Here we introduce Latent Factor Analysis via Dynamical Systems (LFADS), a deep learning method to infer latent dynamics from single-trial neural spiking data. LFADS uses a nonlinear dynamical system (a recurrent neural network) to infer the dynamics underlying observed population activity and to extract de-noised single-trial firing rates from neural spiking data. We apply LFADS to a variety of monkey and human motor cortical datasets, demonstrating its ability to predict observed behavioral variables with unprecedented accuracy, extract precise estimates of neural dynamics on single trials, infer perturbations to those dynamics that correlate with behavioral choices, and combine data from non-overlapping recording sessions (spanning months) to improve inference of underlying dynamics. In summary, LFADS leverages all observations of a neural populations activity to accurately model its dynamics on single trials, opening the door to a detailed understanding of the role of dynamics in performing computation and ultimately driving behavior.

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