Biology subjects

Pruszynski, J. A.

Publications and source records attributed to Pruszynski, J. A..

10 recordsLinked to original sources

bioRxiv2018

Synaptic integration across first-order tactile neurons can discriminate edge orientations with high acuity and speed

Our ability to manipulate objects relies on tactile inputs from first-order tactile neurons that innervate the glabrous skin of the hand. The distal axon of these neurons branches in the skin and innervates many mechanoreceptors, yielding spatially-complex receptive fields. Here we show that synaptic integration across the complex signals from the first-order neuronal population could underlie human ability to accurately (< 3{degrees}) and rapidly process the orientation of edges moving across the fingertip. We first derive spiking models of human first-order tactile neurons that fit and predict responses to moving edges with high accuracy. We then use the model neurons in simulating the peripheral neuronal population that innervates a fingertip. We use machine learning to train classifiers performing synaptic integration across the neuronal population activity, and show that synaptic integration across first-order neurons can process edge orientations with high acuity and speed. In particular, our models suggest that integration of fast-decaying (AMPA-like) synaptic inputs within short timescales is critical for discriminating fine orientations, whereas integration of slow-decaying (NMDA-like) synaptic inputs refine discrimination and maintain robustness over longer timescales. Taken together, our results provide new insight into the computations occurring in the earliest stages of the human tactile processing pathway and how they may be critical for supporting hand function.\n\nAuthor SummaryOur ability to manipulate objects relies on tactile inputs signaled by first-order neurons that innervate mechanoreceptors in the skin of the hand and have spatially-complex receptive fields. Here we show how synaptic integration across the rich inputs from first-order neurons can rapidly and accurately process the orientation of edges moving across the fingertip. We derive spiking models of human first-order tactile neurons, then use the models to simulate the peripheral neuronal population that innervates a fingertip. We use machine learning to train classifiers performing synaptic integration across the neuronal population activity, and show that synaptic integration across first-order neurons could underlie human ability to process edge orientations with high acuity and speed.

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bioRxiv2018

Feedforward and feedback control share an internal model of the arm’s dynamics

Humans have a remarkable capacity to learn novel movement patterns in a wide variety of contexts. Recent work has shown that, when countering external forces, the nervous system adjusts not only voluntary (ie. feedforward) control but also reflex (ie. feedback) responses. Here we show that directly altering the physical properties of the arm (i.e. intersegmental dynamics) causes the nervous system to adjust feedforward control and that this learning also transfers to feedback responses even though they were never directly trained. In our first experiment, we altered intersegmental dynamics by asking participants to generate pure elbow movements with the shoulder joint either free to rotate or locked. Locking the shoulder joint cancels the interaction forces that arise at the shoulder during forearm rotation and thus removes the need to activate shoulder muscles to prevent shoulder joint rotation. We first asked whether the nervous system learns this altered mapping of intersegmental dynamics. In the baseline phase, we found robust activation of shoulder flexor muscles for pure elbow flexion trials prior to movement onset - as required to counter the intersegmental dynamics. After locking the shoulder joint in the adaptation phase, we found a substantial reduction in shoulder muscle activity over many trials. After unlocking the shoulder joint in the post-adaptation phase, we observed after-effects, as participants made systematic hand path errors. In our second experiment, we investigated whether such learning transfers to feedback control. Mechanical perturbations applied to the limb in the baseline phase revealed that feedback responses, like feedforward control, also appropriately countered intersegmental dynamics. In the adaptation phase, we found a substantial reduction in shoulder feedback responses - as appropriate for the altered intersegmental dynamics. We also found that this decay in shoulder feedback responses correlated across subjects with the amount of decay during feedforward control. Our work adds to the growing evidence that feedforward and feedback control share an internal model of the arms dynamics.

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bioRxiv2018

A rapid visuomotor response on the human upper limb is selectively influenced by implicit, but not explicit, motor learning

How do humans learn to adapt their motor actions to achieve task success? Recent behavioral and patient studies have challenged the classic notion that motor learning arises solely from the errors produced during a task, suggesting instead that explicit cognitive strategies can act in concert with the implicit, error\"based, motor learning component. Here, we show that the earliest wave of directionally-tuned neuromuscular activity that occurs within ~100 ms of peripheral visual stimulus onset is selectively influenced by the implicit component of motor learning. In contrast, the voluntary neuromuscular activity associated with reach initiation, which evolves ~100 to 200 ms later is influenced by both the implicit and explicit components of motor learning. The selective influence of the implicit, but not explicit, component of motor learning on the earliest cascade of neuromuscular activity supports the notion that these components of motor learning can differentially influence descending motor pathways.

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bioRxiv2018

Edge orientation perception during active touch

Previous studies investigating the perceptual attributes of tactile edge-orientation processing have applied their stimuli to an immobilized fingertip. Here we tested the perceptual attributes of edge orientation processing when participants actively touched the stimulus. Our participants moved their finger over two pairs of edges-one pair parallel and the other nonparallel to varying degrees-and were asked to identify which of the two pairs was nonparallel. In addition to the psychophysical estimates of edge orientation acuity, we measured the speed at which participants moved their finger and the forces they exerted when moving their finger over the stimulus. We report four main findings. First, edge orientation acuity during active touch averaged 12.4{degrees}, similar to that previously reported during passive touch. Second, on average, participants moved their finger over the stimuli at 23.9 mm/s and exerted contact forces of 0.47 N. Third, across participants, there was no clear relationship between how people moved their finger or how they pressed on the stimulus and their edge orientation acuity. However, within participants, there appeared to be a weak effect of speed on performance whereby error trials included slightly faster finger movements; no equivalent effect was found for contact force. Fourth, consistent with previous work testing spatial acuity, we found significant correlation between fingertip size and orientation acuity such that people with smaller fingertips tended to have better orientation acuity.

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bioRxiv2018

Stability of representational geometry across a wide range of fMRI activity levels

Fine-grained activity patterns, as measured with functional magnetic resonance imaging (fMRI), are thought to reflect underlying neural representations. Multivariate analysis techniques, such as representational similarity analysis (RSA), can be used to test models of brain representation by quantifying the representational geometry (the collection of pair-wise dissimilarities between activity patterns). One important caveat, however, is that non-linearities in the coupling between neural activity and the fMRI signal may lead to significant distortions in the representational geometry estimated from fMRI activity patterns. Here we tested the stability of representational dissimilarity measures in primary sensory-motor (S1 and M1) and early visual regions (V1/V2) across a large range of activation levels. Subjects were visually cued with different letters to perform single finger presses with one of the 5 fingers at a rate of 0.3-2.6 Hz. For each stimulation frequency, we quantified the difference between the 5 activity patterns in M1, S1, and V1/V2. We found that the representational geometry remained stable, even though the average activity increased over a large dynamic range. These results indicate that the representational geometry of fMRI activity patterns can be reliably assessed, largely independent of the average activity in the region. This has important methodological implications for RSA and other multivariate analysis approaches that use the representational geometry to make inferences about brain representations.

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bioRxiv2017

Done in 100 ms: Path-dependent visuomotor transformation in the human upper limb

A core assumption underlying mental chronometry is that more complex tasks increase cortical processing, prolonging reaction times. Here we show that increases in task complexity alter the magnitude, rather than the latency, of the output for a circuit that rapidly transforms visual information into motor actions. We quantified visual stimulus-locked responses (SLRs), which are changes in upper limb muscle recruitment that evolve at a fixed latency [~]100 ms after novel visual stimulus onset. First, we studied the underlying reference frame of the SLR, by dissociating initial eye and hand position. Despite its quick latency, we found that the SLR was expressed in a hand-centric reference frame, suggesting that the circuit mediating the SLR integrated retinotopic visual information with body configuration. Next, we studied the influence of planned movement trajectory, requiring participants to prepare and generate curved or straight reaches in the presence of obstacles to attain the same visual stimulus. We found that SLR magnitude reflected the initial planned movement trajectory, regardless of the ensuing movement curvature. Based on these results, we suggest that the circuit mediating the SLR lies in parallel to other well-studied corticospinal pathways. Although the fixed latency of the SLR precludes extensive cortical processing, inputs conveying information relating to task complexity, such as body configuration and planned movement trajectory, can pre-set nodes within the circuit underlying to the SLR to modulate its magnitude.\n\nSIGNIFICANCE STATEMENTA core assumption underlying mental chronometry is that more complex tasks increase cortical processing, prolonging reaction times. Here, we showed that increases in task complexity altered the magnitude, rather than the latency, of a circuit that rapidly transforms visual information into motor actions. We focus on stimulus-locked responses (SLRs), which are changes in upper limb muscle recruitment that evolve at a fixed latency [~]100 ms after novel visual stimulus onset. We showed that despite is quick latency, the circuitry mediating the SLR transformed a retinotopic visual signal into a hand-centric motor command suitable to contribute to the initial movement trajectory. We suggest that this circuit lies in parallel to other well-studied corticospinal pathways.

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bioRxiv2017

Neural network models of the tactile system develop first-order units with spatially complex receptive fields

First-order tactile neurons have spatially complex receptive fields. Here we use machine learning tools to show that such complexity arises for a wide range of training sets and network architectures, and benefits network performance, especially on more difficult tasks and in the presence of noise. Our work suggests that spatially complex receptive fields are normatively good given the biological constraints of the tactile periphery.

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bioRxiv2017

Fast and accurate edge orientation processing during object manipulation

Quickly and accurately extracting information about a touched objects orientation is a critical aspect of dexterous object manipulation. However, the speed and acuity of tactile edge orientation processing with respect to the fingertips as reported in previous perceptual studies appear inadequate in these respects. Here we directly establish the tactile systems capacity to process edge-orientation information during dexterous manipulation. Participants extracted tactile information about edge orientation very quickly, using it within 200 ms of first touching the object. Participants were also strikingly accurate. With edges spanning the entire fingertip, edge-orientation resolution was better than 3{degrees} in our object manipulation task, which is several times better than reported in previous perceptual studies. Performance remained impressive even with edges as short as 2 mm, consistent with our ability to precisely manipulate very small objects. Taken together, our results radically redefine the spatial processing capacity of the tactile system.

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bioRxiv2017

Rapid feedback responses are flexibly coordinated across arm muscles to support goal-directed reaching

A transcortical pathway helps support goal-directed reaching by processing somatosensory information to produce rapid feedback responses across multiple joints and muscles. Here we tested whether such feedback responses can account for changes in arm configuration and for arbitrary visuomotor transformations - two manipulations that alter how muscles at the elbow and wrist need to be coordinated to achieve task success. Participants used a planar three degree-of-freedom exoskeleton robot to move a cursor to a target following a mechanical perturbation that flexed the elbow. In our first experiment, the cursor was mapped to the veridical position of the robot handle, but participants grasped the handle with two different hand orientations (thumb pointing upward or thumb point downward). We found that large rapid feedback responses were evoked in wrist extensor muscles when wrist extension helped move the cursor to the target (i.e., thumb upward), and in wrist flexor muscles when wrist flexion helped move the cursor to the target (i.e., thumb downward). In our second experiment, participants grasped the robot handle with their thumb pointing upward, but the cursors movement was either veridical, or was mirrored such that flexing the wrist moved the cursor as if the participant extended their wrist, and vice versa. After extensive practice, we found that rapid feedback responses were appropriately tuned to the wrist muscles that supported moving the cursor to the target when the cursor was mapped to the mirrored movement of the wrist, but were not tuned to the appropriate wrist muscles when the cursor was remapped to the wrists veridical movement.

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bioRxiv2017

Compensating for intersegmental dynamics across the shoulder, elbow and wrist joints during feedforward and feedback control

Moving the arm is complicated by mechanical interactions that arise between limb segments. Such intersegmental dynamics cause torques applied at one joint to produce movement at multiple joints and, in turn, the only way to create single joint movement is by applying torques at multiple joints. Here, we investigated whether the nervous system accounts for intersegmental limb dynamics across the shoulder, elbow, and wrist joints during self-initiated reaching and when countering external mechanical perturbations. Our first experiment tested whether the timing and amplitude of shoulder muscle activity accounts for interaction torques produced during single-joint elbow movements from different elbow initial orientations and over a range of movement speeds. We found that shoulder muscle activity reliably preceded movement onset and elbow agonist activity, and was scaled to compensate for the magnitude of interaction torques arising because of forearm rotation. Our second experiment tested whether elbow muscles compensate for interaction torques introduced by single-joint wrist movements. We found that elbow muscle activity preceded movement onset and wrist agonist muscle activity, and thus the nervous system predicted interaction torques arising because of hand rotation. Our third and fourth experiments tested whether shoulder muscles compensate for interaction torques introduced by different hand orientations during self-initiated elbow movements and when countering mechanical perturbations that caused pure elbow motion. We found that the nervous system predicted the amplitude and direction of interaction torques, appropriately scaling the amplitude of shoulder muscle activity during self-initiated elbow movements and rapid feedback control. Taken together, our results demonstrate that the nervous system robustly accounts for intersegmental dynamics, and that the process is similar across the proximal to distal musculature of the arm as well as between feedforward (i.e., self-initiated) and feedback (i.e., reflexive) control.\n\nNEW & NOTEWORTHYIntersegmental dynamics complicate the mapping between applied joint torques and the resulting joint motions. Here, we provide evidence that the nervous system robustly predicts these intersegmental limb dynamics across the shoulder, elbow and wrist joints during reaching and when countering external perturbations.

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