An Electric Field Model for Prediction of Somatosensory (S1) Cortical Field Potentials Induced by Ventral Posterior Lateral (VPL) Thalamic Microstimulation

Choi, J. S.; DiStasio, Marcello; Brockmeier, Austin J.; Francis, Joseph T.

doi:10.1109/tnsre.2011.2181417

Cited by 13 publications

(11 citation statements)

References 18 publications

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“…We briefly introduce and present results from this model, and show that these results are broadly consistent with the in vivo findings. In future, we will explore the possibility of using this model as a supplement to experimental investigations of microstimulation, particularly with respect to the development of a somatosensory neuroprosthesis [17], [19], [43].…”

Section: Introductionmentioning

confidence: 99%

Cortical Plasticity Induced by Spike-Triggered Microstimulation in Primate Somatosensory Cortex

et al. 2013

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Electrical stimulation of the nervous system for therapeutic purposes, such as deep brain stimulation in the treatment of Parkinson’s disease, has been used for decades. Recently, increased attention has focused on using microstimulation to restore functions as diverse as somatosensation and memory. However, how microstimulation changes the neural substrate is still not fully understood. Microstimulation may cause cortical changes that could either compete with or complement natural neural processes, and could result in neuroplastic changes rendering the region dysfunctional or even epileptic. As part of our efforts to produce neuroprosthetic devices and to further study the effects of microstimulation on the cortex, we stimulated and recorded from microelectrode arrays in the hand area of the primary somatosensory cortex (area 1) in two awake macaque monkeys. We applied a simple neuroprosthetic microstimulation protocol to a pair of electrodes in the area 1 array, using either random pulses or pulses time-locked to the recorded spiking activity of a reference neuron. This setup was replicated using a computer model of the thalamocortical system, which consisted of 1980 spiking neurons distributed among six cortical layers and two thalamic nuclei. Experimentally, we found that spike-triggered microstimulation induced cortical plasticity, as shown by increased unit-pair mutual information, while random microstimulation did not. In addition, there was an increased response to touch following spike-triggered microstimulation, along with decreased neural variability. The computer model successfully reproduced both qualitative and quantitative aspects of the experimental findings. The physiological findings of this study suggest that even simple microstimulation protocols can be used to increase somatosensory information flow.

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Section: Introductionmentioning

confidence: 99%

Cortical Plasticity Induced by Spike-Triggered Microstimulation in Primate Somatosensory Cortex

et al. 2013

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“…Much recent effort in the BMI field has been towards “closing the loop” - the provision of somatosensory feedback via electrical microstimulation along the sensory pathway during operation [11–14]. We have shown that S1 can also be used to decode reward state information.…”

Section: Discussionmentioning

confidence: 96%

Reward value is encoded in primary somatosensory cortex and can be decoded from neural activity during performance of a psychophysical task

McNiel

Choi

Hessburg

et al. 2016

2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

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Encoding of reward valence has been shown in various brain regions, including deep structures such as the substantia nigra as well as cortical structures such as the orbitofrontal cortex. While the correlation between these signals and reward valence have been shown in aggregated data comprised of many trials, little work has been done investigating the feasibility of decoding reward valence on a single trial basis. Towards this goal, one non-human primate (macaca radiata) was trained to grip and hold a target level of force in order to earn zero, one, two, or three juice rewards. The animal was informed of the impending result before reward delivery by means of a visual cue. Neural data was recorded from primary somatosensory cortex (S1) during these experiments and firing rate histograms were created following the appearance of the visual cue and used as input to a variety of classifiers. Reward valence was decoded with high levels of accuracy from S1 both in the post-cue and post-reward periods. Additionally, the proportion of units showing significant changes in their firing rates was influenced in a predictable way based on reward valence. The existence of a signal within S1 cortex that encodes reward valence could have utility for implementing reinforcement learning algorithms for brain machine interfaces. The ability to decode this reward signal in real time with limited data is paramount to the usability of such a signal in practical applications.

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“…Recent years have seen extraordinary progress in technologies that not only allow us to read the brain signals, but also to artificially stimulate neural circuits. Neurostimulation will be the best way to demonstrate our understanding of neural codes (Stanley, 2013 ), by injecting signals that produce specific natural sensory sensations (O'Doherty et al, 2011 ; Choi et al, 2012 ; Klaes et al, 2014 ), motor behaviors (Overduin et al, 2012 ; Van Acker et al, 2013 ), or memory (Hampson et al, 2013 ) and cognitive (Hampson et al, 2012 ) influences. Clinically, a major challenge for the next generation of motor-function-restoring brain-machine interfaces (BMIs) is the incorporation of realistic somatosensory feedback via cortical stimulation (Bensmaia and Miller, 2014 ).…”

Section: Introductionmentioning

confidence: 99%

Restoring Behavior via Inverse Neurocontroller in a Lesioned Cortical Spiking Model Driving a Virtual Arm

Durá-Bernal

Neymotin

et al. 2016

Front. Neurosci.

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Neural stimulation can be used as a tool to elicit natural sensations or behaviors by modulating neural activity. This can be potentially used to mitigate the damage of brain lesions or neural disorders. However, in order to obtain the optimal stimulation sequences, it is necessary to develop neural control methods, for example by constructing an inverse model of the target system. For real brains, this can be very challenging, and often unfeasible, as it requires repeatedly stimulating the neural system to obtain enough probing data, and depends on an unwarranted assumption of stationarity. By contrast, detailed brain simulations may provide an alternative testbed for understanding the interactions between ongoing neural activity and external stimulation. Unlike real brains, the artificial system can be probed extensively and precisely, and detailed output information is readily available. Here we employed a spiking network model of sensorimotor cortex trained to drive a realistic virtual musculoskeletal arm to reach a target. The network was then perturbed, in order to simulate a lesion, by either silencing neurons or removing synaptic connections. All lesions led to significant behvaioral impairments during the reaching task. The remaining cells were then systematically probed with a set of single and multiple-cell stimulations, and results were used to build an inverse model of the neural system. The inverse model was constructed using a kernel adaptive filtering method, and was used to predict the neural stimulation pattern required to recover the pre-lesion neural activity. Applying the derived neurostimulation to the lesioned network improved the reaching behavior performance. This work proposes a novel neurocontrol method, and provides theoretical groundwork on the use biomimetic brain models to develop and evaluate neurocontrollers that restore the function of damaged brain regions and the corresponding motor behaviors.

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An Electric Field Model for Prediction of Somatosensory (S1) Cortical Field Potentials Induced by Ventral Posterior Lateral (VPL) Thalamic Microstimulation

Cited by 13 publications

References 18 publications

Cortical Plasticity Induced by Spike-Triggered Microstimulation in Primate Somatosensory Cortex

Cortical Plasticity Induced by Spike-Triggered Microstimulation in Primate Somatosensory Cortex

Reward value is encoded in primary somatosensory cortex and can be decoded from neural activity during performance of a psychophysical task

Restoring Behavior via Inverse Neurocontroller in a Lesioned Cortical Spiking Model Driving a Virtual Arm

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