Operant conditioning of motor cortex neurons reveals neuron-subtype-specific responses in a brain-machine interface task

Garcia-Garcia, Martha G; Márquez-Chin, César; Popović, Miloš R.

doi:10.1038/s41598-020-77090-2

Cited by 7 publications

(8 citation statements)

References 71 publications

(100 reference statements)

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“…high amplitude calcium events) following conditioning, which they hypothesized was evidence of plasticity mechanisms in CNs [15]. Although a recent study found that an increase in the amplitude of the single-neuron response during conditioning in a thresholded activity task was only found in neurons with bursting propensity, and not in neuron types which do not produce bursts of activity [11], suggesting that changes in single neurons in a BMI are also neuron-subtype-specific.…”

Section: Discussionmentioning

confidence: 99%

“…Operant conditioning has been implemented down to the single-neuron level and in small groups of neurons (i.e. 2-10) in humans [2] as well as various animal models, including the original experiments by Fetz and colleagues in monkeys [3][4][5][6][7][8], rats [1,[9][10][11], and in mice using calcium imaging [12][13][14][15]. In addition to BMI applications, operant conditioning has been recently used to study the cortical and subcortical circuits involved in neuroprosthetic skill learning because of its spatial specificity to alter relevant circuits [1,7,16].…”

Section: Introductionmentioning

confidence: 99%

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Operant conditioning reveals task-specific responses of single neurons in a brain–machine interface

Garcia-Garcia¹,

Márquez-Chin²,

Popović³

2021

J. Neural Eng.

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Objective. Volitional modulation of single cortical neurons holds great potential for the implementation of brain–machine interfaces (BMIs) because it can induce a rapid acquisition of arbitrary associations between machines and neural activity. It can also be used as a framework to study the limits of single-neuron control in BMIs. Approach. We tested the control of a one-dimensional actuator in two BMI tasks which differed only in the neural contingency that determined when a reward was dispensed. A thresholded activity task, commonly implemented in single-neuron BMI control, consisted of reaching or exceeding a neuron activity level, while the second task consisted of reaching and maintaining a narrow neuron activity level (i.e. windowed activity task). Main findings. Single neurons in layer V of the motor cortex of rats improved performance during both the thresholded activity and windowed activity BMI tasks. However, correct performance during the windowed activity task was accompanied by activation of neighboring neurons, not in direct control of the BMI. In contrast, only neurons in direct control of the BMI were active at the time of reward during the thresholded activity task. Significance. These results suggest that thresholded activity single-neuron BMI implementations are more appropriate compared to windowed activity BMI tasks to capitalize on the adaptability of cortical circuits to acquire novel arbitrary skills.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Operant conditioning reveals task-specific responses of single neurons in a brain–machine interface

Garcia-Garcia¹,

Márquez-Chin²,

Popović³

2021

J. Neural Eng.

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“…They may be impaired in auditory processing disorders and dyslexia [ 107 – 109 ], autism [ 110 ], or aging [ 111 ]. Lastly, given these differences in coding between unit types, neuroprosthetic interfaces in the auditory system [ 112 ] and beyond [ 5 , 113 ] may benefit from considering unit type if single-neuron resolution can be achieved.…”

Section: Discussionmentioning

confidence: 99%

Distinct neuronal types contribute to hybrid temporal encoding strategies in primate auditory cortex

Liu

Wang

2022

PLoS Biol

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Studies of the encoding of sensory stimuli by the brain often consider recorded neurons as a pool of identical units. Here, we report divergence in stimulus-encoding properties between subpopulations of cortical neurons that are classified based on spike timing and waveform features. Neurons in auditory cortex of the awake marmoset (Callithrix jacchus) encode temporal information with either stimulus-synchronized or nonsynchronized responses. When we classified single-unit recordings using either a criteria-based or an unsupervised classification method into regular-spiking, fast-spiking, and bursting units, a subset of intrinsically bursting neurons formed the most highly synchronized group, with strong phase-locking to sinusoidal amplitude modulation (SAM) that extended well above 20 Hz. In contrast with other unit types, these bursting neurons fired primarily on the rising phase of SAM or the onset of unmodulated stimuli, and preferred rapid stimulus onset rates. Such differentiating behavior has been previously reported in bursting neuron models and may reflect specializations for detection of acoustic edges. These units responded to natural stimuli (vocalizations) with brief and precise spiking at particular time points that could be decoded with high temporal stringency. Regular-spiking units better reflected the shape of slow modulations and responded more selectively to vocalizations with overall firing rate increases. Population decoding using time-binned neural activity found that decoding behavior differed substantially between regular-spiking and bursting units. A relatively small pool of bursting units was sufficient to identify the stimulus with high accuracy in a manner that relied on the temporal pattern of responses. These unit type differences may contribute to parallel and complementary neural codes.

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“…Generally, BMIs use neuronal action potentials (spikes) recorded by implanted multichannel depth-type microelectrode arrays [1][2][3][4][5][6][7][8][9][15][16][17]. In general, this invasive method has a higher spatial resolution and a better quality of neural signals than the non-invasive method like scalp electroencephalography.…”

Section: Discussionmentioning

confidence: 99%

“…supplementary motor area [5][6][7][8][9][10][11]. Traditionally, the motor cortex has been the main target of BMIs; however, other brain areas including the prefrontal (PFC) or parietal cortex also have the potential for BMI applications [10][11][12][13][14][15][16][17][18].…”

Section: Introductionmentioning

confidence: 99%

Somatosensory ECoG-based brain–machine interface with electrical stimulation on medial forebrain bundle

et al. 2022

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Brain–machine interface (BMI) provides an alternative route for controlling an external device with one’s intention. For individuals with motor-related disability, the BMI technologies can be used to replace or restore motor functions. Therefore, BMIs for movement restoration generally decode the neural activity from the motor-related brain regions. In this study, however, we designed a BMI system that uses sensory-related neural signals for BMI combined with electrical stimulation for reward. Four-channel electrocorticographic (ECoG) signals were recorded from the whisker-related somatosensory cortex of rats and converted to extract the BMI signals to control the one-dimensional movement of a dot on the screen. At the same time, we used operant conditioning with electrical stimulation on medial forebrain bundle (MFB), which provides a virtual reward to motivate the rat to move the dot towards the desired center region. The BMI task training was performed for 7 days with ECoG recording and MFB stimulation. Animals successfully learned to move the dot location to the desired position using S1BF neural activity. This study successfully demonstrated that it is feasible to utilize the neural signals from the whisker somatosensory cortex for BMI system. In addition, the MFB electrical stimulation is effective for rats to learn the behavioral task for BMI.

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Operant conditioning of motor cortex neurons reveals neuron-subtype-specific responses in a brain-machine interface task

Cited by 7 publications

References 71 publications

Operant conditioning reveals task-specific responses of single neurons in a brain–machine interface

Operant conditioning reveals task-specific responses of single neurons in a brain–machine interface

Distinct neuronal types contribute to hybrid temporal encoding strategies in primate auditory cortex

Somatosensory ECoG-based brain–machine interface with electrical stimulation on medial forebrain bundle

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