Biology subjects

Greenspon, C. M.

Publications and source records attributed to Greenspon, C. M..

4 recordsLinked to original sources

bioRxiv2023

Biomimetic multi-channel microstimulation of somatosensory cortex conveys high resolution force feedback for bionic hands

AO_SCPLOWBSTRACTC_SCPLOWManual interactions with objects are supported by tactile signals from the hand. This tactile feedback can be restored in brain-controlled bionic hands via intracortical microstimulation (ICMS) of somatosensory cortex (S1). In ICMS-based tactile feedback, contact force can be signaled by modulating the stimulation intensity based on the output of force sensors on the bionic hand, which in turn modulates the perceived magnitude of the sensation. In the present study, we gauged the dynamic range and precision of ICMS-based force feedback in three human participants implanted with arrays of microelectrodes in S1. To this end, we measured the increases in sensation magnitude resulting from increases in ICMS amplitude and participants ability to distinguish between different intensity levels. We then assessed whether we could improve the fidelity of this feedback by implementing "biomimetic" ICMS-trains, designed to evoke patterns of neuronal activity that more closely mimic those in natural touch, and by delivering ICMS through multiple channels at once. We found that multi-channel biomimetic ICMS gives rise to stronger and more distinguishable sensations than does its single-channel counterpart. Finally, we implemented biomimetic multi-channel feedback in a bionic hand and had the participant perform a compliance discrimination task. We found that biomimetic multi-channel tactile feedback yielded improved discrimination over its single-channel linear counterpart. We conclude that multi-channel biomimetic ICMS conveys finely graded force feedback that more closely approximates the sensitivity conferred by natural touch.

neuroscience↗

bioRxiv2022

Texture Coding In Higher Order Somatosensory Cortices

AO_SCPLOWBSTRACTC_SCPLOWOur sense of touch confers to us the ability to perceive textural features over a broad range of spatial scales and material properties, giving rise to a complex sensory experience. To understand the neural basis of texture perception requires that the responses of somatosensory neurons be probed with stimuli that tile the space of spatial scales and material properties experienced during everyday interactions with objects. We have previously shown that neurons in early stages of somatosensory processing - the nerves and somatosensory cortex (S1) - are highly sensitive to texture and carry a representation of texture that is highly informative about the surface but also predicts the evoked sensory experience. In contrast, the texture signals in higher order areas - secondary somatosensory cortex (S2) and the parietal ventral area (PV) - have never been investigated with a rich and naturalistic textural set. To fill this gap, we recorded single-unit activity in S2/PV of macaques while they performed a texture discrimination task. We then characterized the neural responses to texture and compared these to their counterparts in somatosensory cortex (S1). We found that the representation of texture in S2/PV differs markedly from its counterpart in S1. In particular, S2/PV neurons carry a much sparser representation of texture identity and also information about task variables, including the animals eventual perceptual decision. S2/PV thus seems to carry a labile representation of texture that reflects task demands rather than faithfully encoding the stimulus.

neuroscience↗

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.

neuroscience↗

bioRxiv2021

Sensory computations in the cuneate nucleus of macaques

AO_SCPLOWBSTRACTC_SCPLOWTactile nerve fibers fall into a few classes that can be readily distinguished based on their spatiotemporal response properties. Because nerve fibers reflect local skin deformations, they individually carry ambiguous signals about object features. In contrast, cortical neurons exhibit heterogeneous response properties that reflect computations applied to convergent input from multiple classes of afferents, which confer to them a selectivity for behaviorally relevant features of objects. The conventional view is that these complex response properties arise within the cortex itself, implying that sensory signals are not processed to any significant extent in the two intervening structures - the cuneate nucleus (CN) and the thalamus. To test this hypothesis, we recorded the responses evoked in CN to a battery of stimuli that have been extensively used to characterize tactile coding in both the periphery and cortex, including skin indentations, vibrations, random dot patterns, and scanned edges. We found that CN responses are more similar to their cortical counterparts than they are to their inputs: CN neurons receive input from multiple classes of nerve fibers, they have spatially complex receptive fields, and they exhibit selectivity for object features. Contrary to consensus, then, CN plays a key role in processing tactile information. SO_SCPLOWIGNIFICANCEC_SCPLOWPerception is the outcome of the sequential processing of sensory signals at multiple stages along the neuraxis. The conventional view is that tactile signals are processed predominantly in the cerebral cortex. We tested this view by investigating the response properties of neurons in the cuneate nucleus (CN), the first potential stage of processing along the primary touch neuraxis. We found that CN responses more nearly resemble those of cortical neurons than they do those of nerve fibers: CN neurons have spatially complex receptive fields reflecting convergent input from multiple classes of nerve fibers and exhibit a selectivity for object features, absent in the nerve. We conclude that CN plays a key, early role in the processing of tactile information.

neuroscience↗