Distinct roles of neuronal phenotypes during neurofeedback adaptation

Yi Zhao; Hannah M. Stealey; Hung-Yun Lu; Enrique Contreras-Hernandez; Yin-Jui Chang; Philippe N. Tobler; Samantha R. Santacruz

doi:10.1371/journal.pone.0351053

Abstract

Learning adaptation allows the brain to refine motor patterns in response to changing environments rapidly. While population-level neural dynamics and single-neuron activity in motor learning have been widely studied, the contributions of individual neuron types remain poorly understood. Here, we employed a brain-machine interface (BMI) task with perturbations of varying difficulty to investigate single-neuron dynamics underlying neurofeedback adaptation in two rhesus macaques. Cortical neurons were classified based on waveform shape into narrow waveform (NW) and broad waveform (BW) categories, representing putative inhibitory interneurons and excitatory pyramidal neurons, respectively. Compared to BW neurons, NW neurons were more active and more strongly involved in the learning process. Moreover, task difficulty modulated neural responsiveness and coordination within both neuron groups, highlighting differential neuron engagement during neurofeedback adaptation. Our findings provide novel insights into single-neuron mechanisms underlying neurofeedback adaptation and emphasize the distinct functional roles of neuronal phenotypes in rapid learning processes.

Citation: Zhao Y, Stealey HM, Lu H-Y, Contreras-Hernandez E, Chang Y-J, Tobler PN, et al. (2026) Distinct roles of neuronal phenotypes during neurofeedback adaptation. PLoS One 21(7): e0351053. https://doi.org/10.1371/journal.pone.0351053

Editor: Marie-Constance Corsi, Centre Inria de Paris: Inria Centre de Recherche de Paris, FRANCE

Received: June 2, 2025; Accepted: May 21, 2026; Published: July 10, 2026

Copyright: © 2026 Zhao et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: Raw data is available by request from the corresponding author, and processed data from representative sessions is available on GitHub (https://github.com/hstealey/bci_analysis/DEMO_data). The complete dataset will be published in a figshare repository after the review process.

Funding: This study was supported by the Whitehall Foundation in the form of a grant awarded to S.R.S. (2022-12-071) and the National Science Foundation in the form of a grant awarded to S.R.S. (2145412). The specific roles of this author are articulated in the ‘author contributions’ section. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Competing interests: The authors have declared that no competing interests exist.

Introduction

The ability to adapt motor skills to new situations exemplifies the brain’s capacity for motor learning adaptation, a process that enables humans and animals to adjust and refine motor patterns in response to changing environments or physical capabilities. For example, after learning to play tennis, adapting to a mild wind while hitting the ball is more manageable than adjusting to strong wind conditions because the brain can quickly adapt to smaller perturbations [1]. Motor learning adaptation relies on error-driven learning mechanisms that modify neural patterns in motor areas of the brain, facilitating rapid adjustments to external changes [2–6]. Researchers have studied neural mechanisms, such as neuroplasticity, to provide insights into how the brain learns and modifies its structure and function during motor learning adaptation [7–11]. These studies are critical for advancing rehabilitation strategies for individuals with motor function impairments, such as those caused by stroke or spinal cord injury [12–15]. Moreover, understanding motor learning adaptation may inform enhancements to artificial intelligence (AI) systems, as the brain adapts to disruptions more efficiently than current AI models [16–19].

In the past decade, brain-machine interface (BMI) paradigms have emerged as powerful tools for establishing causal links between neural activity and observable behaviors [20–23]. These paradigms enable researchers to investigate how neurons operate both individually and as part of populations to adapt neural activity during motor learning tasks [24–26]. In BMI tasks, individuals control external devices by volitionally modulating their neural activity. Since BMI learning establishes a causal mapping between neural signals (often from motor areas of the brain) and behavior, it can serve as a valuable model for studying motor learning adaptation. The controlled environment of BMI tasks simplifies the context of relevant motor variables to the neural signals and external actuator movements used in the BMI task, minimizing confounding factors and isolating the neural adaptation processes of interest [27]. Additionally, these tasks mimic natural motor learning through error-driven adjustments, providing insights into how the brain refines motor patterns in response to changes or perturbations in the external environment [28]. Although BMI tasks involve error-driven adjustments reminiscent of natural motor learning, the underlying process is not identical. BMI neurofeedback adaptation requires subjects to adjust neural activity to an artificial decoder without proprioceptive feedback, making it a distinct form of neurofeedback learning.

Researchers have found that population activity can often be characterized as having low-dimensional dynamics and that a pre-existing covariance structure constrains neural learning on short timescales [29]. This implies that learning to control existing population dynamics is more easily achieved compared to generating new activity patterns. In addition, other work suggests that we have a fixed repertoire of neural activity patterns, and that changes in population neural activity are induced by the reassociation of a selection of neural subspace latent variables [30]. Consequently, researchers have focused on understanding population-level neural dynamics, as population signals are more robust and better represent distributed cognitive and motor functions within local neuronal networks [31].

While these studies have highlighted the significance of population-level activity, understanding motor learning adaptation at the single-neuron level remains equally critical. Single neurons form the fundamental building blocks of neuronal networks and play a fundamental role in brain activity. Insights into single-neuron dynamics can reveal how individual neurons coordinate and respond to motor learning tasks, bridging the gap between population-level phenomena and individual neural mechanisms. For instance, while previous studies have investigated changes in firing rates at the single-neuron level during motor learning [32–39], questions about how individual neurons contribute to learning adaptation remain unanswered. Preferred direction changes in directionally-tuned single neurons have been observed [40,41], yet the coordination and contribution of individual neurons during motor learning adaptation remain poorly understood.

In this study, we address these gaps and provide additional granularity by examining how motor cortical neuron phenotypes explain the roles of individual neurons in BMI neurofeedback adaptation. Specifically, we categorize single neurons into narrow waveform (NW) neurons and broad waveform (BW) neurons. The classification based on waveform width is motivated by well-established electrophysiological properties of cortical neurons: narrow action potentials are thought to correspond to fast-spiking interneurons, which are predominantly inhibitory, while broad action potentials are associated with pyramidal neurons, which are primarily excitatory [42–46]. We demonstrate that NW neurons exhibit higher contributions to decoder output, greater coordination during task execution, and increased engagement during the learning process relative to BW neurons. By elucidating the distinct roles of NW and BW neurons, this study provides novel insights into the single-neuron mechanisms underlying neurofeedback adaptation, complementing existing knowledge of population-level neural dynamics.

Methods

Experimental models and procedures

All procedures performed were approved by the Institutional Animal Care and Use Committee (IACUC) of the University of Texas at Austion (AUP-2022–00109). These procedures comply with the National Institutes of Health Guide for the Care and Use of Laboratory Animals to ensure animal welfare and the ethical treatment of animal subjects.

Two male rhesus macaques (Macaca mulatta, A: 6 years old, 9.5 kg, B: 6 years old, 8.8 kg) participated in the experiments. Compatible animals may be pair-housed, increasing the opportunity for social interaction. However, the animals are housed individually when they are not actively involved in training or experimental sessions so that we can carefully monitor food and water intake as well as excreta for each animal. The housing arrangement allows for social interaction because all animals have visual, olfactory, and auditory contact with other animals. Environmental enrichment in the enclosure is always provided, rotating through items such as mobile mirrors, swings, perches, and other durable toys. Animals are fed a standard nutritionally balanced non-human primate diet and are offered a variety of food treats including fruits (dried or fresh), vegetables, and grains to promote their psychological well-being. These supplemental foods are provided a minimum of two times per week. Animals receive routine, daily contact with trained animal care personnel from both researchers and veterinary staff. All enrichment, food, fluid, and health monitoring are documented daily. At least every 12 months, the animals are given a complete physical examination by a veterinarian, and baseline criteria recorded to include body condition scoring and clinical values (e.g., electrolyte levels, serum osmolality, hematocrit and total plasma protein) obtained as part of blood sampling to include CBC and clinical chemistry.

Both subjects in this study were implanted with chronic arrays (Innovative Neurophysiology, Inc., Durham, NC) in their left hemisphere primary motor (M1)/ pre-motor (PMd) area of cortex (Monkey A: 64-channel chronic array; Monkey B: 128-channel chronic array). Prior to the surgery, we used structural MR images to determine the targeted implantation area. Appropriate anesthetic and analgesic were used for surgical procedures and post-operative care, with all anesthetic and analgesic administration performed in consultation with veterinary staff. At the end of the study, animals remained in the lab colony for inclusion in future studies. No animals were euthanized in this study.

Neural recordings

Neural activity was recorded by Grapevine Neural Interface Processor (Ripple Neuro, Salt Lake City, UT). Spike activity was recorded with a sampling rate of 30 kHz and we included only units with a firing rate greater than 1 spike/s in our BMI decoders. On average 37.6 ± 6.2 units in monkey A and 91.6 ± 19.3 units in monkey B were directly used in BMI control. Specifically, variability was primarily driven by day-to-day differences in signal quality and long-term stability of the implanted microelectrode arrays. Consistent with prior chronic recording literature, we observed a gradual decline in the total number of isolatable units over the implant duration, although not a substantial drop across the short span of sessions included in this study. All available units in each session were used directly for BMI control. Spikes were binned into 100 ms time windows to generate spike count vectors for decoder inputs and the BMI decoder was output updated at a rate of 10 Hz.

Behavioral tasks

Subjects were trained to perform a 2-D center-out BMI task beginning with holding the cursor (circle, radius 5 mm) at the center target (circle, radius 20 mm) for 200 ms. One of eight peripheral targets which are uniformly distributed around a circle (radium 100 mm) is then shown on screen to cue the cursor destination. The animal volitionally controls their neural activity to move the cursor from the previous holding center position to this shown target. After successfully holding the cursor at the peripheral target for 500 ms, a liquid reward is delivered to the subject’s mouth. If the subject cannot complete this within 10 s, the trial is considered to be unsuccessful.

Each experiment session starts with a 3-minute visual feedback (VFB) task. During the VFB task, the subject passively watches a computer-controlled simulation of the BMI task while receiving the juice reward for all completed trials. We use neural activity recorded during VFB to determine the units used for the BMI decoder in the brain-controlled neurofeedback task. After the VFB task, the subject completed a 10 minute center-out BMI calibration period in which we employed closed-loop decoder adaptation (CLDA) to optimize the decoder parameters. CLDA was implemented using a recursive maximum likelihood adaptation rule [40,47], enabling the Kalman filter to update its parameters based on ongoing neural activity. During this period, cursor velocity was generated from a weighted combination of neural output and a straight-line assistive component pointing from the cursor to the target. The assistive component began at 10% of the total velocity command and decreased linearly to 0% by the end of the calibration. This procedure produced a stable and intuitive decoder mapping. Following CLDA, the main BMI task was initiated, and all subsequent behavior was performed under full neural control without any computer assistance. In the main task, there are two blocks: baseline and perturbation. The baseline block contains 336 trials (42 trials per peripheral target), and the perturbation block contains 384 trials (48 trials per peripheral target). In the baseline block, the decoder used is the well-trained intuitive mapping decoder. In the perturbation block, a defined rotation matrix is applied to the intuitive mapping decoder to impose a visuomotor rotation on the cursor. To gain the liquid juice reward, the animals must adapt their neural activity to compensate for the perturbed output from the decoder. Two perturbation conditions were tested: an easy condition defined by a 50° rotation and a hard condition defined by a 90° rotation applied to the intuitive decoder. Each recording session included only one of these conditions, and the conditions were not intermixed within a session. For both monkeys, all easy-condition sessions were completed prior to hard-condition sessions (Monkey A: 16 easy sessions, 18 hard sessions; Monkey B: 17 easy sessions, 24 hard sessions). Within each session, the main BMI task consisted of a baseline block (336 trials; 42 per target) followed by a perturbation block (384 trials; 48 per target). This structure ensured that adaptation to a single perturbation magnitude could be analyzed independently for each session type.

Kalman filter decoder

We use a Kalman filter as the decoder [24,41,48] to map neural activities and cursor velocities, shown as below:

(1)

(2)

(3)

The cursor state is a 5-by-1 vector and is a 5-by-5 matrix, capturing the physics of cursor position and velocity. The observed neural activity , where N is the number of input neuron units. represents the relationship between neural activity and cursor movement. Gaussian noise is included as and . In Eq. (3), is a 5-by-N Kalman Gain matrix. Since we use the neural activity to decode the cursor velocity, the cursor position is updated by the velocity multiplied by the time interval. The decoder is updated at a rate of 10 Hz, so the time interval used equates to 100 ms.

In the perturbation block, a rotation matrix is applied to to rotate the intuitive mapping decoder, which is shown as below:

(4)

The rotation angle is , which was set to be either 50° or 90°.

Neural waveform classification

To categorize the cells in our recordings into broad and narrow waveform spiking types, we employed a method established in previous research [49–51]. Each cell’s average waveform was subjected to cubic interpolation, enhancing the sampling resolution from 33 μs to 1.65 μs. The interpolated waveforms were then normalized and aligned to the primary trough. The trough-to-peak duration, defined as the interval between the main trough and the subsequent waveform peak, served as the classification metric.

To statistically evaluate the bimodality of the trough-to-peak distribution, we applied two variations of the Hartigan dip test: the original version and a calibrated variant with enhanced sensitivity. Additionally, we used Akaike’s and Bayesian information criteria to compare whether the distribution was better represented by a one- or two-Gaussian model. For the two-Gaussian model, we defined two cutoffs to segment the distribution into three regions. The first cutoff corresponded to the point where the likelihood of classification as a narrow cell was at least 5 times higher than as a broad cell [49,52]. The second cutoff was set where the probability of classification as broad exceeded that of narrow by the same factor. Cells within the region between these cutoffs were deemed unclassifiable. Classification cutoffs for NW and BW neurons were consistent across both monkeys (Supplementary S1 Fig; cutoff for NW neurons: 0.275 ms, cutoff for BW neurons: 0.348 ms in monkey A; cutoff for NW neurons: 0.272 ms, cutoff for BW neurons: 0.36 ms in monkey B), suggesting that these classifications represent distinct neuronal phenotypes across subjects rather than artifacts of feature selection.

Tunning curves

We use a tuning curve to fit the neural responses of different moving directions [40]. The tuning curve is defined as a cosine function, showing below:

(5)

Preferred direction is the movement direction with highest firing rate and modulation depth is the difference in firing rate between peak and trough of the fitted tunning curve.

Factor analysis

Factor analysis (FA) [24] decomposes signals into shared and private components. We apply FA to the collected neural data to obtain shared variance to total variance and partial percent shared variance to total variance (PSOT), which are defined as:

(6)

is the mean percent shared variance per unit. After cross-validation, we determined that 6 factors for both monkey A and B were appropriate.

Percentage of explained variance

We used the percentage of explained variance () to measure the information provided by NW and BW neurons. This method enabled us to determine the proportion of variance in the neurons’ firing rates attributed to the tested variable [52–54]. is defined as:

(7)

(8)

(9)

(10)

(11)

(12)

is the total number of groups, and N represents the total number of trials for the cells. denotes the degrees of freedom.

estimation depends on having enough observations within each task condition to compute stable variance components. For this reason, we calculated using the entire baseline block and the entire perturbation block. Splitting these blocks into early and late segments would substantially reduce the number of trials per target direction, resulting in unreliable and noisy values that could not meaningfully reflect encoding strength.

Results

We trained two rhesus macaques (monkeys A and B) to perform a two-dimensional BMI center-out task (Fig 1A) which is a neurofeedback task. In this task, the animals controlled a cursor on a computer screen by volitionally modulating their neural activity, aiming to move the cursor to one of eight radially distributed peripheral targets to earn a juice reward. Neural activity was quantified as spike counts binned into small time intervals, which served as the high-dimensional input to the BMI decoder, while cursor velocity updates were the low-dimensional decoder output. A Kalman filter-based decoder, a widely used approach in BMI research, mapped the relationship between neural activity inputs and cursor velocity output dynamics [24,29,30,41,55].

Download:

Fig 1. Overview of the BMI task and observed cursor trajectories.

(A) Illustration of the BMI-based center-out task. Monkeys controlled a cursor on a screen by modulating neural activity based on visual feedback of resultant cursor movements. (B) The task was comprised of two blocks: baseline and perturbation. The decoder, which maps neural activity to cursor velocities, was rotated by 50 degrees or 90 degrees during the perturbation block. (C) Representative cursor trajectories to each of the possible target locations, depicted by the dashed line circles. In the baseline block, trajectories were relatively straight (top). During the early phase of the perturbation block (first 40 trials), trajectories were more curved, with longer distances from the center to the targets (middle). In the late phase of the perturbation block, trajectories became straighter again after learning adaptation process.

https://doi.org/10.1371/journal.pone.0351053.g001

The BMI center-out task consisted of two distinct blocks: a baseline block and a perturbation block. Before the task, we trained the decoder using online data to establish an “intuitive mapping” [56,47]. This mapping aligned neural activity patterns with cursor dynamics in a way that mimicked natural motor control, enabling the monkeys to achieve proficient cursor movement control in the baseline block (see Methods). This block consisted of an equal number of trials per peripheral target. In the perturbation block, we introduced a rotation to the intuitive mapping by applying a rotation matrix to the Kalman gain in the BMI decoder (Fig 1B), disrupting the established correspondence between neural activity and cursor movement. This perturbation mapping introduced a rotational mismatch, meaning the direction of cursor movement no longer aligned with the neural activity patterns that previously controlled it. This perturbation resulted in a misalignment between intended and actual cursor trajectories. The perturbation block comprised also consisted of an equal number of trials to the eight possible peripheral targets (Fig 1B), requiring the monkeys to adjust their neural activity to compensate for the rotation for targets in all directions. These adjustments reflect neurofeedback adaptation. We tested two rotation perturbation conditions: an “easy” condition with a 50-degree rotation and a “hard” condition with a 90-degree rotation.

As anticipated, both monkeys displayed fast and stereotypical target acquisition during the baseline block (Fig 1C). In contrast, the early phase of the perturbation block was characterized by prolonged target acquisition times and curved cursor trajectories as the subjects struggled to compensate for the rotation (Fig 1C). Over time, the monkeys adapted to the perturbation, as evidenced by straighter cursor trajectories in the late phase of the perturbation block.

Neuron classification

Neural activity was recorded from the forelimb regions of the dorsal premotor cortex (PMd) and primary motor cortex (M1) using chronically implanted electrode arrays. Neurons with an average firing rate above 1 spike/s and trough-to-peak amplitude greater than 80 µV were selected as inputs to the BMI decoder. For the phenotype classification, we first measured trough-to-peak intervals for all these input neurons across sessions to quantify the typical width of their action potential waveforms (Monkey A: 1,195 neurons; Monkey B: 3,814 neurons). The distributions of trough-to-peak intervals were bimodal for both subjects, suggesting a mixture of activity generated from two categories of neurons based on action potential waveform width (Monkey A: p = 0.0263, Monkey B: p = 0.0138; Hartigan dip test). Fitting a two-component Gaussian mixture model to spike-to-trough interval distributions significantly improved model fit compared to a single-component model (S1 Fig).

Based on these results, neurons were categorized into three groups: narrow waveform (NW) neurons, broad waveform (BW) neurons, and unclassified neurons (Fig 2A,B). Unclassified neurons had mid-range action potential waveform widths which could not confidently be categorized as narrow or broad and thus were excluded from further analysis. Categorization was determined by waveform trough-to-peak thresholds that were derived empirically from the respective distributions for each subject. In Monkey A, 35.98% (430/1195) were classified as NW neurons, 24.46% (933/3814) in Monkey B were identified as NW neurons. These proportions aligned with previous findings, which report NW neurons constituting 15% to 30% of the cortical neuron population [52,57,58]. The ratios of BW neurons in both monkeys were approximately 50% of total input neurons (44.94% in monkey A and 55.24% in monkey B).

Download:

Fig 2. Neuron classification.

(A) Distribution of trough-to-peak durations for three neuron groups: narrow waveform (NW, yellow), broad waveform (BW, blue), and unclassified (UC, green). The histogram represents all input neurons recorded in Monkey A. (B) Representative waveforms of NW (yellow) and BW (blue) neurons, aligned at the trough for comparison. (C). Mean firing rates of NW and BW neurons during easy and hard task conditions for Monkey A. Orange markers indicate the mean firing rates of NW neurons, while blue markers represent those of BW neurons. Error bars denote the standard error of the mean (SEM). Two-way ANOVA indicated significant main effects on firing rate of neuron type for both subjects (monkey A: p = 0.000006; monkey B: p = 8.15 × 10 ⁻ 63) and no significant effect of task condition (monkey A: p = 0.637; monkey B: p = 0.773). Monkey A, whose data is shown, had a significant interaction, while the other subject did not (monkey A: p = 0.0026; monkey B: p = 0.0348). (D) Comparison of preferred direction (PD) changes between well-tuned neurons in NW and BW neurons. No significant differences in mean PD changes were observed between NW and BW neurons (easy condition: t = −0.813, p = 0.416; hard condition: t = −0.361, p = 0.718). However, the variance in PD changes increased significantly under the hard condition compared to the easy condition (Levene’s test: BW neurons, p = 0.003; NW neurons, p = 0.042). The grey dashed line indicated the rotation angle of the tasks. (E) Different proportions of well-tuned neurons in BW and NW neurons (monkey A: t = −4.297, p = 5.82*10⁻⁵; monkey B: t = −7.24, p = 2.46*10⁻¹⁰, two-sample t-tests).

https://doi.org/10.1371/journal.pone.0351053.g002

We evaluated how firing rate activity of neurons differ under this classification and across task conditions. The absence of an effect of task condition (Monkey A: p = 0.637; Monkey B: p = 0.773; main effect of neuron type, two-way ANOVA) and the lack of a reproducible interaction between task condition and neuron type across both subjects (Monkey A: p = 0.0026; Monkey B: p = 0.348; interaction effect, two-way ANOVA) suggest that the primary driver of differences in firing rate is the neuron type (Monkey A: p < 0.001; Monkey B: p < 0.001; main effect of neuron type, two-way ANOVA). The mean firing rate of NW neurons during cursor movements was significantly higher than that of BW neurons (Fig 2C) under both easy and hard task conditions. This high firing rate of NW neurons, which is typical of inhibitory neuron, is also consistent with findings from previous studies [49,57,59].

Previous studies have shown that neurons change their preferred direction (PD) during visuomotor rotation tasks, suggesting that neurons adapt in response to altered movement mappings [39,40]. Despite evidence of PD shifts during visuomotor adaptation, whether distinct neuronal phenotypes exhibit these changes in a uniform or differential manner remains unresolved. We calculated the PD of NW and BW neurons separately for the baseline and perturbation blocks by fitting tuning curves [40] (see Methods). Neurons were well-tuned and included in the PD analysis if their tuning curve fits in both blocks had values greater than 0.5. More than 60% of NW and BW neurons were well-tuned in both blocks (S2 Fig.), and this proportion remained consistent across task difficulty levels, with minor differences likely attributable to session-to-session variations in neuron recordings.

Both NW and BW neurons demonstrated mean PD changes corresponding to the rotation angle (50° for the easy condition and 90° for the hard condition; Fig 2D). There was no significant difference in median PD changes between NW and BW neurons under either task condition (p > 0.05, two-sample t-test). However, the variance in PD changes increased significantly under the hard task conditions for both NW and BW neurons (p < 0.01 for BW neurons; p < 0.05 for NW neurons; Levene’s test). The similar mean PD changes in well-tuned neurons for both types of neurons suggest that these well-tuned neurons were involved in comparable underlying adaptation processes. In both subjects, NW neurons had a significantly higher proportion of well-tuned neurons than BW neurons (Fig 2E, sp < 0.001 for both subjects). While well-tuned neurons in both phenotypes shifted their PD in alignment with the rotation angle in the task, their different proportions suggested that NW and BW neurons may engage distinct mechanisms during neurofeedback adaptation.

Unit contribution

To investigate how the physiological properties of neurons influence their roles in the behavioral task, we evaluated the contributions of BW and NW neurons to neurofeedback control. We quantified these contributions by defining the unit contribution ( as below:

where represents the magnitude of the Kalman gain:

Here, is the unit contribution for neuron averaged across trials; is the magnitude of Kalman gain (see Methods) associated to neuron ; and is mean firing rate of neuron on trial . Given that the intuitive decoder was trained without incorporating neuron phenotype, differences in the Kalman gain magnitude () between NW and BW neurons were not expected. Indeed, our analysis confirmed that the distributions of did not significantly differ between the two neuronal populations in either monkey (Fig 3A, B; p > 0.05 for both), suggesting that the gain magnitude was independent of neuron type.

Download:

Fig 3. NW neurons exhibit higher unit contributions compared to BW Neurons.

(A), (B) Distributions of Kalman Gain magnitudes for narrow waveform (NW) and broad waveform (BW) neurons in Monkey A and Monkey B. No significant differences were observed in the Kalman Gain magnitudes between NW and BW neurons in either monkey (Monkey A: t = 0.014, p = 0.89; Monkey B: t = 0.13, p = 0.83; two-sample t-tests). (C), (D) Comparisons of average unit contributions from NW and BW neurons in Monkey A and Monkey B under easy and hard task conditions. In both conditions, NW neurons exhibited significantly higher contributions than BW neurons. For Monkey A, NW neurons showed greater contributions in the easy condition (z = 16218, p = 2.86 × 10e-9) and the hard condition (z = 30940, p = 0.011). For Monkey B, NW neurons also had significantly higher contributions in the easy condition (z = 130704, p = 5.44 × 10⁻¹⁷) and the hard condition (z = 211484, p = 6.32*10⁻²⁸; Mann-Whitney U tests).

https://doi.org/10.1371/journal.pone.0351053.g003

Despite the similarity in , we found that NW neurons consistently exhibited greater unit contributions than BW neurons across both task difficulty conditions (Fig 3C, D; Monkey A: p < 0.001 in easy condition, p < 0.05 in hard condition, Monkey B: p < 0.001 in both task conditions). These findings suggest that NW neurons play a dominant role in neurofeedback control, which can be attributed to their higher firing rates that amplify their influence within the decoder.

Coordination in BW and NW neurons at population level

Neuron contributions revealed the unique roles of NW and BW neurons in shaping decoder output. However, the mechanisms underlying their adaptation during neurofeedback adaptation remain unclear. Previous research suggests that neural activity during motor adaptation at the population level can be represented in a low-dimensional space [29,30]. Moreover, researchers have shown that motor learning within the same low-dimensional space is induced by reassociation of latent variables [30]. Moreover, previous research indicates that as performance improves, neurons tend to exhibit more synchronized firing patterns [24,48]. These synchronized patterns may reflect the consolidation of neural population dynamics, which can be examined through the variance of neural activity within low-dimensional subspaces. Despite this, how different neuron types coordinate during neurofeedback adaptation has not been explored.

To measure neural coordination, factor analysis (FA) was performed on population neural activity to separate variance into two components: shared variance and private variance [24]. Shared variance reflects correlated firing patterns across the population, while private variance represents variability unique to each neuron [60,61]. The ratio of shared variance over total variance (SOT) is treated as a measurement to quantify the relative amount of coordinated neural activity [24,61]. To evaluate how BW and NW neurons adapt during neurofeedback adaptation, we calculated the partial shared-over-total variance (pSOT) of each phenotypical neuron group (see Methods) since the pSOT ratio assesses the extent to which each subpopulation contributes to the overall change in coordinated activity.

Under both easy and hard task conditions, NW neurons consistently demonstrated significantly higher pSOT ratios compared to BW neurons (three-way ANOVA, p < 0.001 for both monkeys; Fig 4). This indicates that NW neurons demonstrated greater coordination within their subpopulation than BW neurons. Additionally, in Monkey B, pSOT ratios were significantly higher under hard conditions compared to easy conditions (Tukey HSD test, p < 0.05), suggesting that task difficulty may influence neural coordination. Notably, no significant differences in pSOT were observed between baseline and perturbation blocks for either NW or BW neurons. Furthermore, the latent structure of neural activity, as measured by the low-dimensional repertoire, did not differ significantly between baseline and perturbation blocks (S3 Fig.). These findings demonstrate that NW neurons play a crucial role in motor adaptation by exhibiting stronger coordination, especially under challenging task conditions.

Download:

Fig 4. NW neurons show higher coordination than BW neurons through higher pSOT (A), (B) These were pSOT results for monkey A.

(A) was under easy condition while (B) was under hard condition. Three-way ANOVA of neuron type (BW and NW), task blocks (baseline and perturbation) and task condition (easy and hard) was applied to Monkey A. NW neurons had significant higher pSOT values than BW neurons (p = 1.55 × 10⁻⁹). No significant differences in pSOT were observed between the baseline and perturbation blocks (F = p = 0.067). Additionally, in Monkey A, there was no significant difference in pSOT between the easy and hard task conditions (p = 0.17). Same three-way ANOVA was applied to Monkey B data. (C) was Monkey B under easy task condition while (D) was under hard task condition. Significant difference was shown between neuron type (F = 76.75, p = 3.07 × 10⁻¹⁵ for Monkey B) while there was no difference between task blocks (Monkey B: F = 0.96, p = 0.33). However, a significant difference was detected between easy and hard task condition in monkey B (F = 6.68, p = 0.011).

https://doi.org/10.1371/journal.pone.0351053.g004

Differential Responses of BW and NW Neurons at the Single-Neuron Level

While FA provided insights into the coordination of neural subpopulations, the trial-by-trial neural responses at the single-neuron level remained unclear. Previous studies have demonstrated that the percentage of explained variance () is an effective measure of neural coding changes under varying task conditions [52]. Therefore, we applied to quantify single-neuron response across neuron types in both subjects. Neural recordings revealed substantial variability in firing rates across neurons, depending on cursor movement direction. To account for this, was calculated separately for all eight target locations.

To minimize variability in calculations, we analyzed the last 20 trials of the baseline block and the first 20 trials of the perturbation block for each target location. This comparison assessed the sensitivity of neural activity changes to neurofeedback adaptation in response to the perturbation.

High values indicated stronger neural responsiveness during the learning adaptation period. Both BW and NW neurons exhibited significantly stronger encoding of cursor movements during the learning period compared to the center-hold period (Fig 5A). Within trials, values for both neuron types showed an initial rapid increase followed by a slower decay in both easy and hard conditions. Additionally, NW neurons demonstrated higher average values than BW neurons across all target locations and task conditions in both subjects (Fig 5B, 5C; p < 0.001, two-sample t-test in both monkey A and B), suggesting that NW neurons play a more prominent role in representing motor adaptation information. Moreover, both NW and BW neurons had significant higher values in hard condition than in easy condition (p < 0.001, two-sample t-test in both monkey A and monkey B). This indicated that when the task was more challenging, both neurons were more responsive during the task.

Download:

Fig 5. NW neurons had higher

than BW neurons during cursor moving period.

(A) values trends within trial time between BW (blue) and NW (yellow) neurons. The subplots’ locations represent the corresponding target location on the screen. Cursor movement period was from “Hold” state to “Target” state. Here, we restricted the visualization for trial time from 0.2s before “Hold” state and 2s after “Hold” state. (B), (C). NW (yellow) neurons have significantly higher average values than BW (blue) neurons in both task conditions. (monkey A: t = −14.28, p = 5.02 × 10⁻⁴⁵ in easy condition, t = −5.97, p = 2.64 × 10⁻⁹ in hard condition; monkey B: t = −18.28, p = 1.73 × 10⁻⁷³ in easy condition, t = −29.77, p = 9.51 × 10⁻¹⁸⁹).

https://doi.org/10.1371/journal.pone.0351053.g005

We found that NW neurons tended to reach their peak values faster than BW neurons in both subjects, with a significant effect of neuron type on peak for one subject and a strong trend of an effect in the other (Fig 6A, B; monkey A: neuron types: p = 0.052; Monkey B: neuron types: p << 0.001, two-way ANOVA). Moreover, both types of neurons show faster response in the hard task condition compared to the easy task condition (Fig 6A, B; monkey A: task condition: p < 0.001; Monkey B: task condition: p << 0.001, two-way ANOVA). Also, NW neurons shown greater values during the learning period compared to BW neurons. Furthermore, both neurons increased maximum values significantly during more challenging task conditions (Fig 6C,D. monkey A: neuron types: p << 0.001; task conditions: p << 0.001; monkey B: neuron types: p << 0.001; task conditions: p << 0.001, two-way ANOVA). These results suggest that the observed variance in firing rates between the last 20 trials of the baseline block and the first 20 trials of the perturbation block was driven by the induced rotation in the task. In other words, changes in firing rates for both neuron types are likely attributable to neurofeedback adaptation. Overall, served as a valuable metric for quantifying neural responses during neurofeedback adaptation. NW neurons were found to be more responsive and engaged during the learning period compared to BW neurons, and both neuron types demonstrated increased responsiveness during more challenging tasks.

Download:

Fig 6. Neuron phenotype and task difficulty affected neurons’ response.

(A), (B) Time to reach peak 𝜔^2 values were different between NW (yellow) and BW (blue) neurons (monkey A: neuron type: F = 3.77, p = 0.052, task difficulty: F = 29.21, p = 6.69 × 10⁻⁸; monkey B: neuron type: F = 19.53, p = 9.93 × 10⁻⁶ task difficulty: F = 445.50, p = 5.47 × 10⁻⁹⁸, two-way ANOVA test).). (C), (D) Max values comparison between NW and BW neurons (monkey A: neuron type: F = 162.41, p = 8.05 × 10⁻³⁷; task difficulty: F = 305.67, p = 3.99 × 10⁻⁶⁷; monkey B: neuron type: F = 152.63, p = 8.66 × 10⁻²⁴⁷ task difficulty: F = 2263.97, p = 0, two-way ANOVA test).

https://doi.org/10.1371/journal.pone.0351053.g006

Discussion

In this study, we investigated how individual neurons participate in neurofeedback adaptation, focusing on whether these neurons exhibit uniform responses during the adaptation period within a precisely defined BMI task. Neurons were classified into two groups, NW neurons and BW neurons, based on their waveform shapes (trough-to-peak intervals; Fig 2). Our results demonstrate that NW neurons contribute more to the decoder output (Fig 3) and exhibit greater pSOT indicative of higher coordination during task execution (Fig 4). NW neurons also display higher values, reflecting greater engagement during the learning period. Moreover, larger rotations in the intuitive mapping elicited more substantial changes in neural activity in both neuron groups compared to smaller rotations.

Our classification of neurons into NW and BW groups based on their trough-to-peak intervals aligns with previous studies, which have identified a bimodal distribution of spike waveform widths indicative of the separation between excitatory cells and inhibitory interneurons in the cortex [52,57,58,62]. Specifically, NW neurons, which exhibit higher firing rates than BW neurons, are consistent with prior observations [52,63]. These characteristics support the hypothesis that NW neurons are likely inhibitory interneurons. Moreover, approximately 70% of the NW neurons in the dorsal premotor cortex (PMd) and primary motor cortex (M1) exhibited preferred directions, with many adjusting their preferred directions following task rotation. This proposition is further corroborated by findings in the primary visual cortex showing that interneurons also have preferred orientations [64–66]. In this study, inhibitory neurons exhibited directional tuning, which may be caused by the inputs from the pyramidal neurons through the recurrent cortical circuitry. M1 contains extensive recurrent excitatory-inhibitory loops [67,68]. Pyramidal neurons excite inhibitory neurons, which in turn regulate pyramidal neuron activity. This recurrent architecture means that the directional tuning seen in inhibitory neurons is likely driven by the activity of the pyramidal neurons rather than independent computation.

Our findings revealed differences in coordination between NW and BW neurons during neurofeedback adaptation. NW neurons exhibited higher pSOT ratios, indicating greater coordination within their population. Furthermore, we observed no significant changes in the latent variable repertoire between the baseline and perturbation blocks, consistent with previous findings [30]. This stability in the latent variable repertoire likely reflects changes in the preferred direction of individual neurons without significant alterations in local neuronal networks. These results underscore the adaptability of individual neurons in response to task perturbations, while broader network structures remain relatively stable.

There are limitations to this study that should be noted. Firstly, we did not record neural activity from upstream brain regions, which could provide direct insights into the extent of changes that are attributable to upstream inputs. M1 and PMd receive projections from multiple upstream areas, including the somatosensory cortex, supplementary motor area, and thalamus [69,70]. Without direct measurements from these regions, it remains unclear to what extent the observed neural changes are attributable to modifications in upstream inputs versus intrinsic plasticity within local cortical networks. Second, the classification of unclassified neurons remains an open issue, as they account for approximately one-third of all input neurons in the decoder. To ensure accurate classification, we prioritized precision over quantity for the NW neuron type, which led to a higher proportion of unclassified neurons. Notably, previous studies have reported that pyramidal cells constitute 70–85% of all neurons in the mammalian cortex [71,72]. However, in our dataset, BW neurons comprise only about 50% of the total recorded neurons. This discrepancy arises because NW neurons exhibited a much narrower distribution compared to BW neurons. This narrower distribution of NW neurons forces stricter classification criteria, leading to more BW neurons being labeled as unclassified neurons to avoid misclassification. In this study, we relied solely on electrophysiological recordings and lacked additional methods to further differentiate these neuronal types. Future studies incorporating complementary recording techniques may provide greater resolution in distinguishing these two neuronal populations. Also, in this study, neuron categories were identified using trough-to-peak waveform duration, a classification method that is widely used in nonhuman primate electrophysiology and has been shown to correspond well to putative inhibitory and excitatory populations. The proportions of NW neurons observed here (27.2% in Monkey A; 19.3% in Monkey B) fall within the expected 15–30% range reported in prior NHP studies, supporting the validity of this approach. Nonetheless, relying on a single waveform metric has inherent limitations, as rodent studies often incorporate additional features [46,73], such as firing rate, spike shape metrics, or autocorrelogram structure, to improve separation of neuronal classes. While our focus was on within-study comparisons rather than absolute cell-type identification, we acknowledge that a multi-feature classification framework or complementary approaches (e.g., optogenetic tagging or intracellular characterization) would further enhance the precision of cell-type assignments. Future work will benefit from integrating such methods to more fully resolve inhibitory–excitatory distinctions.

Our findings emphasize the differential contributions of NW and BW neurons during BMI motor learning adaptation. Both neuron groups contain well-tuned neurons capable of adjusting their tuning properties in response to varying rotation perturbations, but NW neurons exhibit higher coordination and engagement during neurofeedback adaptation. Additionally, both groups undergo greater changes in neural coordination as task complexity increases. Understanding the distinct roles of different neuronal groups during neural adaptation provides valuable insights into the mechanisms underlying rapid learning. These findings may guide the development of more sophisticated brain-inspired artificial networks and contribute to achieving more stable and precise BMI control systems.

Supporting information

S1 Fig. Two mixed Gaussian models of trough-to-peak intervals.

The reductions in the Akaike Information Criterion (Monkey A: −1592 to −1860; Monkey B: −5265 to −5594) and the Bayesian Information Criterion (Monkey A: −1581 to −1835; Monkey B: −5253 to −5563) show that two mixed Gaussian models were better than a single Gaussian model.

https://doi.org/10.1371/journal.pone.0351053.s001

(TIF)

S2 Fig. Pie plots showing ~60% of all neurons are both tuned neurons.

In both task conditions, around 60% of total neurons are tuned in both baseline and perturbation blocks (yellow). Only around 10% of total neurons were only baseline tuned (red) or perturbation tuned (purple). Around 15% of total neurons were not sensitive to directions in both baseline and perturbation.

https://doi.org/10.1371/journal.pone.0351053.s002

(TIF)

S3 Fig. Example latent repertoire.

There was no significant difference between the latent repertoire between baseline and perturbation (t2 = 1.75e-29, p = 1, Hotelling’s T^2 test).

https://doi.org/10.1371/journal.pone.0351053.s003

(TIF)

References

1. Criscimagna-Hemminger SE, Bastian AJ, Shadmehr R. Size of error affects cerebellar contributions to motor learning. J Neurophysiol. 2010;103(4):2275–84. pmid:20164398
- View Article
- PubMed/NCBI
- Google Scholar
2. Matsuda N, Abe MO. Error size shape relationships between motor variability and implicit motor adaptation. Biology (Basel). 2023;12(3):404. pmid:36979096
- View Article
- PubMed/NCBI
- Google Scholar
3. Wagner I, Schütz AC. Interaction of dynamic error signals in saccade adaptation. J Neurophysiol. 2023;129(3):717–32. pmid:36791071
- View Article
- PubMed/NCBI
- Google Scholar
4. Kim OA, Forrence AD, McDougle SD. Motor learning without movement. Proc Natl Acad Sci U S A. 2022;119(30):e2204379119. pmid:35858450
- View Article
- PubMed/NCBI
- Google Scholar
5. Ikegami T, Flanagan JR, Wolpert DM. Reach adaption to a visuomotor gain with terminal error feedback involves reinforcement learning. PLoS One. 2022;17(6):e0269297. pmid:35648778
- View Article
- PubMed/NCBI
- Google Scholar
6. Albert ST, Blaum EC, Blustein DH. Sensory prediction error drives subconscious motor learning outside of the laboratory. J Neurophysiol. 2023;130(2):427–35. pmid:37435648
- View Article
- PubMed/NCBI
- Google Scholar
7. Darainy M, Vahdat S, Ostry DJ. Neural basis of sensorimotor plasticity in speech motor adaptation. Cereb Cortex. 2019;29(7):2876–89. pmid:29982495
- View Article
- PubMed/NCBI
- Google Scholar
8. Areshenkoff CN, Brouwer AJ, Gale DJ, Nashed JY, Smallwood J, Flanagan JR. The structural-functional neural architectures of implicit and explicit motor learning. 2023.
- View Article
- Google Scholar
9. Wang L, Conner JM, Rickert J, Tuszynski MH. Structural plasticity within highly specific neuronal populations identifies a unique parcellation of motor learning in the adult brain. Proc Natl Acad Sci U S A. 2011;108(6):2545–50. pmid:21257908
- View Article
- PubMed/NCBI
- Google Scholar
10. Debas K, Carrier J, Orban P, Barakat M, Lungu O, Vandewalle G, et al. Brain plasticity related to the consolidation of motor sequence learning and motor adaptation. Proc Natl Acad Sci U S A. 2010;107(41):17839–44. pmid:20876115
- View Article
- PubMed/NCBI
- Google Scholar
11. Marongelli EN, Thoroughman KA. The advantage of flexible neuronal tunings in neural network models for motor learning. Front Comput Neurosci. 2013;7:100. pmid:23888141
- View Article
- PubMed/NCBI
- Google Scholar
12. Wang C, Peng L, Hou Z-G. A control framework for adaptation of training task and robotic assistance for promoting motor learning with an upper limb rehabilitation robot. IEEE Trans Syst Man Cybern, Syst. 2022;52(12):7737–47.
- View Article
- Google Scholar
13. Asín-Prieto G, Martínez-Expósito A, Barroso FO, Urendes EJ, Gonzalez-Vargas J, Alnajjar FS, et al. Haptic adaptive feedback to promote motor learning with a robotic ankle exoskeleton integrated with a video game. Front Bioeng Biotechnol. 2020;8:113. pmid:32154239
- View Article
- PubMed/NCBI
- Google Scholar
14. Wiersma AM, Fouad K, Winship IR. Enhancing spinal plasticity amplifies the benefits of rehabilitative training and improves recovery from stroke. J Neurosci. 2017;37(45):10983–97. pmid:29025926
- View Article
- PubMed/NCBI
- Google Scholar
15. Walker JR, Detloff MR. Plasticity in cervical motor circuits following spinal cord injury and rehabilitation. Biology (Basel). 2021;10(10):976. pmid:34681075
- View Article
- PubMed/NCBI
- Google Scholar
16. Kato K, Sasada S, Nishimura Y. Flexible adaptation to an artificial recurrent connection from muscle to peripheral nerve in man. J Neurophysiol. 2016;115(2):978–91. pmid:26631144
- View Article
- PubMed/NCBI
- Google Scholar
17. Mitchell BA, Marneweck M, Grafton ST, Petzold LR. Motor adaptation via distributional learning. J Neural Eng. 2021;18(4):10.1088/1741-2552/aba6d9. pmid:32674091
- View Article
- PubMed/NCBI
- Google Scholar
18. Zhang Y, Qiu S, Wei W, Ma X, He H. Dynamic weighted filter bank domain adaptation for motor imagery brain–computer interfaces. IEEE Trans Cogn Dev Syst. 2023;15(3):1348–59.
- View Article
- Google Scholar
19. Osuna-Orozco R, Zhao Y, Stealey HM, Lu H-Y, Contreras-Hernandez E, Santacruz SR. Adaptation and learning as strategies to maximize reward in neurofeedback tasks. Front Hum Neurosci. 2024;18:1368115. pmid:38590363
- View Article
- PubMed/NCBI
- Google Scholar
20. Rao YN, Sung-Phil Kim, Sanchez JC, Erdogmus D, Principe JC, Carmena JM, et al. Learning Mappings in Brain Machine Interfaces with Echo State Networks. Proceedings. (ICASSP ’05). IEEE International Conference on Acoustics, Speech, and Signal Processing, 2005. 233–6. https://doi.org/10.1109/icassp.2005.1416283
21. Biswas R, Shlizerman E. Neuro-PC: Causal functional connectivity from neural dynamics. https://arxiv.org/abs/2011.03913v1. 2020. Accessed 2024 April 14.
- View Article
- Google Scholar
22. DiGiovanna J, Marchal L, Rattanatamrong P, Zhao M, Darmanjian S, Mahmoudi B, et al. Towards real-time distributed signal modeling for brain-machine interfaces. Lecture Notes in Computer Science. Springer Berlin Heidelberg. 2007. 964–71. https://doi.org/10.1007/978-3-540-72584-8_127
23. Boi F, Semprini M, Vato A. A non-linear mapping algorithm shaping the control policy of a bidirectional brain machine interface. Annu Int Conf IEEE Eng Med Biol Soc. 2016;2016:3052–5. pmid:28268955
- View Article
- PubMed/NCBI
- Google Scholar
24. Zippi EL, You AK, Ganguly K, Carmena JM. Selective modulation of cortical population dynamics during neuroprosthetic skill learning. Sci Rep. 2022;12(1):15948. pmid:36153356
- View Article
- PubMed/NCBI
- Google Scholar
25. Wu Y, Faisal AA. Towards an integrative spiking neuron model of motor control - from cortex & basal ganglia to muscles & sensory feedback. 2015 7th International IEEE/EMBS Conference on Neural Engineering (NER), 2015. 378–81. https://doi.org/10.1109/ner.2015.7146638
26. Chen H, Kunimatsu J, Oya T, Imaizumi Y, Hori Y, Matsumoto M, et al. Comparison of neural population dynamics in the regression subspace between continuous and categorical task parameters. 2022.
- View Article
- Google Scholar
27. Dadarlat MC, Canfield RA, Orsborn AL. Neural plasticity in sensorimotor brain-machine interfaces. Annu Rev Biomed Eng. 2023;25:51–76. pmid:36854262
- View Article
- PubMed/NCBI
- Google Scholar
28. Orsborn AL, Carmena JM. Creating new functional circuits for action via brain-machine interfaces. Front Comput Neurosci. 2013;7:157. pmid:24204342
- View Article
- PubMed/NCBI
- Google Scholar
29. Sadtler PT, Quick KM, Golub MD, Chase SM, Ryu SI, Tyler-Kabara EC, et al. Neural constraints on learning. Nature. 2014;512(7515):423–6. pmid:25164754
- View Article
- PubMed/NCBI
- Google Scholar
30. Golub MD, Sadtler PT, Oby ER, Quick KM, Ryu SI, Tyler-Kabara EC, et al. Learning by neural reassociation. Nat Neurosci. 2018;21(4):607–16. pmid:29531364
- View Article
- PubMed/NCBI
- Google Scholar
31. Chung S, Abbott LF. Neural population geometry: An approach for understanding biological and artificial neural networks. Curr Opin Neurobiol. 2021;70:137–44. pmid:34801787
- View Article
- PubMed/NCBI
- Google Scholar
32. Mitz AR, Godschalk M, Wise SP. Learning-dependent neuronal activity in the premotor cortex: Activity during the acquisition of conditional motor associations. J Neurosci. 1991;11(6):1855–72. pmid:2045890
- View Article
- PubMed/NCBI
- Google Scholar
33. Asaad WF, Rainer G, Miller EK. Neural activity in the primate prefrontal cortex during associative learning. Neuron. 1998;21(6):1399–407. pmid:9883732
- View Article
- PubMed/NCBI
- Google Scholar
34. Li CS, Padoa-Schioppa C, Bizzi E. Neuronal correlates of motor performance and motor learning in the primary motor cortex of monkeys adapting to an external force field. Neuron. 2001;30(2):593–607. pmid:11395017
- View Article
- PubMed/NCBI
- Google Scholar
35. Paz R, Boraud T, Natan C, Bergman H, Vaadia E. Preparatory activity in motor cortex reflects learning of local visuomotor skills. Nat Neurosci. 2003;6(8):882–90. pmid:12872127
- View Article
- PubMed/NCBI
- Google Scholar
36. Rokni U, Richardson AG, Bizzi E, Seung HS. Motor learning with unstable neural representations. Neuron. 2007;54(4):653–66. pmid:17521576
- View Article
- PubMed/NCBI
- Google Scholar
37. Ganguly K, Carmena JM. Emergence of a stable cortical map for neuroprosthetic control. PLoS Biol. 2009;7(7):e1000153. pmid:19621062
- View Article
- PubMed/NCBI
- Google Scholar
38. Ganguly K, Dimitrov DF, Wallis JD, Carmena JM. Reversible large-scale modification of cortical networks during neuroprosthetic control. Nat Neurosci. 2011;14(5):662–7. pmid:21499255
- View Article
- PubMed/NCBI
- Google Scholar
39. Chase SM, Kass RE, Schwartz AB. Behavioral and neural correlates of visuomotor adaptation observed through a brain-computer interface in primary motor cortex. J Neurophysiol. 2012;108(2):624–44. pmid:22496532
- View Article
- PubMed/NCBI
- Google Scholar
40. Orsborn AL, Moorman HG, Overduin SA, Shanechi MM, Dimitrov DF, Carmena JM. Closed-loop decoder adaptation shapes neural plasticity for skillful neuroprosthetic control. Neuron. 2014;82(6):1380–93. pmid:24945777
- View Article
- PubMed/NCBI
- Google Scholar
41. Shenoy KV, Carmena JM. Combining decoder design and neural adaptation in brain-machine interfaces. Neuron. 2014;84(4):665–80. pmid:25459407
- View Article
- PubMed/NCBI
- Google Scholar
42. Mitchell JF, Sundberg KA, Reynolds JH. Differential attention-dependent response modulation across cell classes in macaque visual area V4. Neuron. 2007;55(1):131–41. pmid:17610822
- View Article
- PubMed/NCBI
- Google Scholar
43. Constantinidis C, Goldman-Rakic PS. Correlated discharges among putative pyramidal neurons and interneurons in the primate prefrontal cortex. J Neurophysiol. 2002;88(6):3487–97. pmid:12466463
- View Article
- PubMed/NCBI
- Google Scholar
44. Bruno RM, Simons DJ. Feedforward mechanisms of excitatory and inhibitory cortical receptive fields. J Neurosci. 2002;22(24):10966–75. pmid:12486192
- View Article
- PubMed/NCBI
- Google Scholar
45. Swadlow HA. Fast-spike interneurons and feedforward inhibition in awake sensory neocortex. Cereb Cortex. 2003;13(1):25–32. pmid:12466212
- View Article
- PubMed/NCBI
- Google Scholar
46. Frank LM, Brown EN, Wilson MA. A comparison of the firing properties of putative excitatory and inhibitory neurons from CA1 and the entorhinal cortex. J Neurophysiol. 2001;86(4):2029–40. pmid:11600659
- View Article
- PubMed/NCBI
- Google Scholar
47. Dangi S, Gowda S, Moorman HG, Orsborn AL, So K, Shanechi M, et al. Continuous closed-loop decoder adaptation with a recursive maximum likelihood algorithm allows for rapid performance acquisition in brain-machine interfaces. Neural Comput. 2014;26(9):1811–39. pmid:24922501
- View Article
- PubMed/NCBI
- Google Scholar
48. Athalye VR, Ganguly K, Costa RM, Carmena JM. Emergence of coordinated neural dynamics underlies neuroprosthetic learning and skillful control. Neuron. 2017;93:955-970.e5.
- View Article
- Google Scholar
49. Ardid S, Vinck M, Kaping D, Marquez S, Everling S, Womelsdorf T. Mapping of functionally characterized cell classes onto canonical circuit operations in primate prefrontal cortex. J Neurosci. 2015;35(7):2975–91. pmid:25698735
- View Article
- PubMed/NCBI
- Google Scholar
50. Oemisch M, Westendorff S, Azimi M, Hassani SA, Ardid S, Tiesinga P, et al. Feature-specific prediction errors and surprise across macaque fronto-striatal circuits. Nat Commun. 2019;10(1):176. pmid:30635579
- View Article
- PubMed/NCBI
- Google Scholar
51. Oemisch M, Westendorff S, Everling S, Womelsdorf T. Interareal spike-train correlations of anterior cingulate and dorsal prefrontal cortex during attention shifts. J Neurosci. 2015;35(38):13076–89. pmid:26400938
- View Article
- PubMed/NCBI
- Google Scholar
52. Ceccarelli F, Ferrucci L, Londei F, Ramawat S, Brunamonti E, Genovesio A. Static and dynamic coding in distinct cell types during associative learning in the prefrontal cortex. Nat Commun. 2023;14(1):8325. pmid:38097560
- View Article
- PubMed/NCBI
- Google Scholar
53. Wasmuht DF, Spaak E, Buschman TJ, Miller EK, Stokes MG. Intrinsic neuronal dynamics predict distinct functional roles during working memory. Nat Commun. 2018;9(1):3499. pmid:30158572
- View Article
- PubMed/NCBI
- Google Scholar
54. Parthasarathy A, Herikstad R, Bong JH, Medina FS, Libedinsky C, Yen S-C. Mixed selectivity morphs population codes in prefrontal cortex. Nat Neurosci. 2017;20(12):1770–9. pmid:29184197
- View Article
- PubMed/NCBI
- Google Scholar
55. Wu W, Black M, Gao Y, Serruya M, Shaikhouni A, Donoghue J. Neural decoding of cursor motion using a Kalman filter. Adv Neural Inf Process Syst. 2002;15.
- View Article
- Google Scholar
56. Dangi S, Orsborn AL, Moorman HG, Carmena JM. Design and analysis of closed-loop decoder adaptation algorithms for brain-machine interfaces. Neural Comput. 2013;25(7):1693–731. pmid:23607558
- View Article
- PubMed/NCBI
- Google Scholar
57. Trainito C, von Nicolai C, Miller EK, Siegel M. Extracellular spike waveform dissociates four functionally distinct cell classes in primate cortex. Curr Biol. 2019;29(18):2973-2982.e5. pmid:31447374
- View Article
- PubMed/NCBI
- Google Scholar
58. Kaufman MT, Churchland MM, Santhanam G, Yu BM, Afshar A, Ryu SI, et al. Roles of monkey premotor neuron classes in movement preparation and execution. J Neurophysiol. 2010;104(2):799–810. pmid:20538784
- View Article
- PubMed/NCBI
- Google Scholar
59. Ferroni CG, Albertini D, Lanzilotto M, Livi A, Maranesi M, Bonini L. Local and system mechanisms for action execution and observation in parietal and premotor cortices. Curr Biol. 2021;31(13):2819-2830.e4. pmid:33984266
- View Article
- PubMed/NCBI
- Google Scholar
60. Churchland MM, Cunningham JP, Kaufman MT, Ryu SI, Shenoy KV. Cortical preparatory activity: representation of movement or first cog in a dynamical machine?. Neuron. 2010;68(3):387–400. pmid:21040842
- View Article
- PubMed/NCBI
- Google Scholar
61. Yu BM, Cunningham JP, Santhanam G, Ryu SI, Shenoy KV, Sahani M. Gaussian-process factor analysis for low-dimensional single-trial analysis of neural population activity. J Neurophysiol. 2009;102(1):614–35. pmid:19357332
- View Article
- PubMed/NCBI
- Google Scholar
62. Kaufman MT, Churchland MM, Shenoy KV. The roles of monkey M1 neuron classes in movement preparation and execution. J Neurophysiol. 2013;110(4):817–25. pmid:23699057
- View Article
- PubMed/NCBI
- Google Scholar
63. Connors BW, Gutnick MJ. Intrinsic firing patterns of diverse neocortical neurons. Trends Neurosci. 1990;13(3):99–104. pmid:1691879
- View Article
- PubMed/NCBI
- Google Scholar
64. Scholl B, Wilson DE, Jaepel J, Fitzpatrick D. Functional logic of layer 2/3 inhibitory connectivity in the ferret visual cortex. Neuron. 2019;104(3):451-457.e3. pmid:31495646
- View Article
- PubMed/NCBI
- Google Scholar
65. Freeman AW. A model for the origin of motion direction selectivity in visual cortex. J Neurosci. 2021;41(1):89–102. pmid:33203740
- View Article
- PubMed/NCBI
- Google Scholar
66. Monier C, Chavane F, Baudot P, Graham LJ, Frégnac Y. Orientation and direction selectivity of synaptic inputs in visual cortical neurons: A diversity of combinations produces spike tuning. Neuron. 2003;37(4):663–80. pmid:12597863
- View Article
- PubMed/NCBI
- Google Scholar
67. Tremblay R, Lee S, Rudy B. GABAergic interneurons in the neocortex: from cellular properties to circuits. Neuron. 2016;91(2):260–92. pmid:27477017
- View Article
- PubMed/NCBI
- Google Scholar
68. Kinnischtzke AK, Simons DJ, Fanselow EE. Motor cortex broadly engages excitatory and inhibitory neurons in somatosensory barrel cortex. Cereb Cortex. 2014;24(8):2237–48. pmid:23547136
- View Article
- PubMed/NCBI
- Google Scholar
69. Akkal D, Dum RP, Strick PL. Supplementary motor area and presupplementary motor area: Targets of basal ganglia and cerebellar output. J Neurosci. 2007;27(40):10659–73. pmid:17913900
- View Article
- PubMed/NCBI
- Google Scholar
70. Takahashi N, Moberg S, Zolnik TA, Catanese J, Sachdev RNS, Larkum ME, et al. Thalamic input to motor cortex facilitates goal-directed action initiation. Curr Biol. 2021;31(18):4148-4155.e4. pmid:34302741
- View Article
- PubMed/NCBI
- Google Scholar
71. DeFelipe J, Fariñas I. The pyramidal neuron of the cerebral cortex: Morphological and chemical characteristics of the synaptic inputs. Prog Neurobiol. 1992;39(6):563–607. pmid:1410442
- View Article
- PubMed/NCBI
- Google Scholar
72. Markram H, Muller E, Ramaswamy S, Reimann MW, Abdellah M, Sanchez CA, et al. Reconstruction and simulation of neocortical microcircuitry. Cell 2015;163:456–92.
- View Article
- Google Scholar
73. Snyder AC, Morais MJ, Smith MA. Dynamics of excitatory and inhibitory networks are differentially altered by selective attention. J Neurophysiol. 2016;116(4):1807–20. pmid:27466133
- View Article
- PubMed/NCBI
- Google Scholar

[ref1] 1. Criscimagna-Hemminger SE, Bastian AJ, Shadmehr R. Size of error affects cerebellar contributions to motor learning. J Neurophysiol. 2010;103(4):2275–84. pmid:20164398
View Article
PubMed/NCBI
Google Scholar

[2] View Article

[3] PubMed/NCBI

[4] Google Scholar

[ref2] 2. Matsuda N, Abe MO. Error size shape relationships between motor variability and implicit motor adaptation. Biology (Basel). 2023;12(3):404. pmid:36979096
View Article
PubMed/NCBI
Google Scholar

[6] View Article

[7] PubMed/NCBI

[8] Google Scholar

[ref3] 3. Wagner I, Schütz AC. Interaction of dynamic error signals in saccade adaptation. J Neurophysiol. 2023;129(3):717–32. pmid:36791071
View Article
PubMed/NCBI
Google Scholar

[10] View Article

[11] PubMed/NCBI

[12] Google Scholar

[ref4] 4. Kim OA, Forrence AD, McDougle SD. Motor learning without movement. Proc Natl Acad Sci U S A. 2022;119(30):e2204379119. pmid:35858450
View Article
PubMed/NCBI
Google Scholar

[14] View Article

[15] PubMed/NCBI

[16] Google Scholar

[ref5] 5. Ikegami T, Flanagan JR, Wolpert DM. Reach adaption to a visuomotor gain with terminal error feedback involves reinforcement learning. PLoS One. 2022;17(6):e0269297. pmid:35648778
View Article
PubMed/NCBI
Google Scholar

[18] View Article

[19] PubMed/NCBI

[20] Google Scholar

[ref6] 6. Albert ST, Blaum EC, Blustein DH. Sensory prediction error drives subconscious motor learning outside of the laboratory. J Neurophysiol. 2023;130(2):427–35. pmid:37435648
View Article
PubMed/NCBI
Google Scholar

[22] View Article

[23] PubMed/NCBI

[24] Google Scholar

[ref7] 7. Darainy M, Vahdat S, Ostry DJ. Neural basis of sensorimotor plasticity in speech motor adaptation. Cereb Cortex. 2019;29(7):2876–89. pmid:29982495
View Article
PubMed/NCBI
Google Scholar

[26] View Article

[27] PubMed/NCBI

[28] Google Scholar

[ref8] 8. Areshenkoff CN, Brouwer AJ, Gale DJ, Nashed JY, Smallwood J, Flanagan JR. The structural-functional neural architectures of implicit and explicit motor learning. 2023.
View Article
Google Scholar

[30] View Article

[31] Google Scholar

[ref9] 9. Wang L, Conner JM, Rickert J, Tuszynski MH. Structural plasticity within highly specific neuronal populations identifies a unique parcellation of motor learning in the adult brain. Proc Natl Acad Sci U S A. 2011;108(6):2545–50. pmid:21257908
View Article
PubMed/NCBI
Google Scholar

[33] View Article

[34] PubMed/NCBI

[35] Google Scholar

[ref10] 10. Debas K, Carrier J, Orban P, Barakat M, Lungu O, Vandewalle G, et al. Brain plasticity related to the consolidation of motor sequence learning and motor adaptation. Proc Natl Acad Sci U S A. 2010;107(41):17839–44. pmid:20876115
View Article
PubMed/NCBI
Google Scholar

[37] View Article

[38] PubMed/NCBI

[39] Google Scholar

[ref11] 11. Marongelli EN, Thoroughman KA. The advantage of flexible neuronal tunings in neural network models for motor learning. Front Comput Neurosci. 2013;7:100. pmid:23888141
View Article
PubMed/NCBI
Google Scholar

[41] View Article

[42] PubMed/NCBI

[43] Google Scholar

[ref12] 12. Wang C, Peng L, Hou Z-G. A control framework for adaptation of training task and robotic assistance for promoting motor learning with an upper limb rehabilitation robot. IEEE Trans Syst Man Cybern, Syst. 2022;52(12):7737–47.
View Article
Google Scholar

[45] View Article

[46] Google Scholar

[ref13] 13. Asín-Prieto G, Martínez-Expósito A, Barroso FO, Urendes EJ, Gonzalez-Vargas J, Alnajjar FS, et al. Haptic adaptive feedback to promote motor learning with a robotic ankle exoskeleton integrated with a video game. Front Bioeng Biotechnol. 2020;8:113. pmid:32154239
View Article
PubMed/NCBI
Google Scholar

[48] View Article

[49] PubMed/NCBI

[50] Google Scholar

[ref14] 14. Wiersma AM, Fouad K, Winship IR. Enhancing spinal plasticity amplifies the benefits of rehabilitative training and improves recovery from stroke. J Neurosci. 2017;37(45):10983–97. pmid:29025926
View Article
PubMed/NCBI
Google Scholar

[52] View Article

[53] PubMed/NCBI

[54] Google Scholar

[ref15] 15. Walker JR, Detloff MR. Plasticity in cervical motor circuits following spinal cord injury and rehabilitation. Biology (Basel). 2021;10(10):976. pmid:34681075
View Article
PubMed/NCBI
Google Scholar

[56] View Article

[57] PubMed/NCBI

[58] Google Scholar

[ref16] 16. Kato K, Sasada S, Nishimura Y. Flexible adaptation to an artificial recurrent connection from muscle to peripheral nerve in man. J Neurophysiol. 2016;115(2):978–91. pmid:26631144
View Article
PubMed/NCBI
Google Scholar

[60] View Article

[61] PubMed/NCBI

[62] Google Scholar

[ref17] 17. Mitchell BA, Marneweck M, Grafton ST, Petzold LR. Motor adaptation via distributional learning. J Neural Eng. 2021;18(4):10.1088/1741-2552/aba6d9. pmid:32674091
View Article
PubMed/NCBI
Google Scholar

[64] View Article

[65] PubMed/NCBI

[66] Google Scholar

[ref18] 18. Zhang Y, Qiu S, Wei W, Ma X, He H. Dynamic weighted filter bank domain adaptation for motor imagery brain–computer interfaces. IEEE Trans Cogn Dev Syst. 2023;15(3):1348–59.
View Article
Google Scholar

[68] View Article

[69] Google Scholar

[ref19] 19. Osuna-Orozco R, Zhao Y, Stealey HM, Lu H-Y, Contreras-Hernandez E, Santacruz SR. Adaptation and learning as strategies to maximize reward in neurofeedback tasks. Front Hum Neurosci. 2024;18:1368115. pmid:38590363
View Article
PubMed/NCBI
Google Scholar

[71] View Article

[72] PubMed/NCBI

[73] Google Scholar

[ref20] 20. Rao YN, Sung-Phil Kim, Sanchez JC, Erdogmus D, Principe JC, Carmena JM, et al. Learning Mappings in Brain Machine Interfaces with Echo State Networks. Proceedings. (ICASSP ’05). IEEE International Conference on Acoustics, Speech, and Signal Processing, 2005. 233–6. https://doi.org/10.1109/icassp.2005.1416283

[ref21] 21. Biswas R, Shlizerman E. Neuro-PC: Causal functional connectivity from neural dynamics. https://arxiv.org/abs/2011.03913v1. 2020. Accessed 2024 April 14.
View Article
Google Scholar

[76] View Article

[77] Google Scholar

[ref22] 22. DiGiovanna J, Marchal L, Rattanatamrong P, Zhao M, Darmanjian S, Mahmoudi B, et al. Towards real-time distributed signal modeling for brain-machine interfaces. Lecture Notes in Computer Science. Springer Berlin Heidelberg. 2007. 964–71. https://doi.org/10.1007/978-3-540-72584-8_127

[ref23] 23. Boi F, Semprini M, Vato A. A non-linear mapping algorithm shaping the control policy of a bidirectional brain machine interface. Annu Int Conf IEEE Eng Med Biol Soc. 2016;2016:3052–5. pmid:28268955
View Article
PubMed/NCBI
Google Scholar

[80] View Article

[81] PubMed/NCBI

[82] Google Scholar

[ref24] 24. Zippi EL, You AK, Ganguly K, Carmena JM. Selective modulation of cortical population dynamics during neuroprosthetic skill learning. Sci Rep. 2022;12(1):15948. pmid:36153356
View Article
PubMed/NCBI
Google Scholar

[84] View Article

[85] PubMed/NCBI

[86] Google Scholar

[ref25] 25. Wu Y, Faisal AA. Towards an integrative spiking neuron model of motor control - from cortex & basal ganglia to muscles & sensory feedback. 2015 7th International IEEE/EMBS Conference on Neural Engineering (NER), 2015. 378–81. https://doi.org/10.1109/ner.2015.7146638

[ref26] 26. Chen H, Kunimatsu J, Oya T, Imaizumi Y, Hori Y, Matsumoto M, et al. Comparison of neural population dynamics in the regression subspace between continuous and categorical task parameters. 2022.
View Article
Google Scholar

[89] View Article

[90] Google Scholar

[ref27] 27. Dadarlat MC, Canfield RA, Orsborn AL. Neural plasticity in sensorimotor brain-machine interfaces. Annu Rev Biomed Eng. 2023;25:51–76. pmid:36854262
View Article
PubMed/NCBI
Google Scholar

[92] View Article

[93] PubMed/NCBI

[94] Google Scholar

[ref28] 28. Orsborn AL, Carmena JM. Creating new functional circuits for action via brain-machine interfaces. Front Comput Neurosci. 2013;7:157. pmid:24204342
View Article
PubMed/NCBI
Google Scholar

[96] View Article

[97] PubMed/NCBI

[98] Google Scholar

[ref29] 29. Sadtler PT, Quick KM, Golub MD, Chase SM, Ryu SI, Tyler-Kabara EC, et al. Neural constraints on learning. Nature. 2014;512(7515):423–6. pmid:25164754
View Article
PubMed/NCBI
Google Scholar

[100] View Article

[101] PubMed/NCBI

[102] Google Scholar

[ref30] 30. Golub MD, Sadtler PT, Oby ER, Quick KM, Ryu SI, Tyler-Kabara EC, et al. Learning by neural reassociation. Nat Neurosci. 2018;21(4):607–16. pmid:29531364
View Article
PubMed/NCBI
Google Scholar

[104] View Article

[105] PubMed/NCBI

[106] Google Scholar

[ref31] 31. Chung S, Abbott LF. Neural population geometry: An approach for understanding biological and artificial neural networks. Curr Opin Neurobiol. 2021;70:137–44. pmid:34801787
View Article
PubMed/NCBI
Google Scholar

[108] View Article

[109] PubMed/NCBI

[110] Google Scholar

[ref32] 32. Mitz AR, Godschalk M, Wise SP. Learning-dependent neuronal activity in the premotor cortex: Activity during the acquisition of conditional motor associations. J Neurosci. 1991;11(6):1855–72. pmid:2045890
View Article
PubMed/NCBI
Google Scholar

[112] View Article

[113] PubMed/NCBI

[114] Google Scholar

[ref33] 33. Asaad WF, Rainer G, Miller EK. Neural activity in the primate prefrontal cortex during associative learning. Neuron. 1998;21(6):1399–407. pmid:9883732
View Article
PubMed/NCBI
Google Scholar

[116] View Article

[117] PubMed/NCBI

[118] Google Scholar

[ref34] 34. Li CS, Padoa-Schioppa C, Bizzi E. Neuronal correlates of motor performance and motor learning in the primary motor cortex of monkeys adapting to an external force field. Neuron. 2001;30(2):593–607. pmid:11395017
View Article
PubMed/NCBI
Google Scholar

[120] View Article

[121] PubMed/NCBI

[122] Google Scholar

[ref35] 35. Paz R, Boraud T, Natan C, Bergman H, Vaadia E. Preparatory activity in motor cortex reflects learning of local visuomotor skills. Nat Neurosci. 2003;6(8):882–90. pmid:12872127
View Article
PubMed/NCBI
Google Scholar

[124] View Article

[125] PubMed/NCBI

[126] Google Scholar

[ref36] 36. Rokni U, Richardson AG, Bizzi E, Seung HS. Motor learning with unstable neural representations. Neuron. 2007;54(4):653–66. pmid:17521576
View Article
PubMed/NCBI
Google Scholar

[128] View Article

[129] PubMed/NCBI

[130] Google Scholar

[ref37] 37. Ganguly K, Carmena JM. Emergence of a stable cortical map for neuroprosthetic control. PLoS Biol. 2009;7(7):e1000153. pmid:19621062
View Article
PubMed/NCBI
Google Scholar

[132] View Article

[133] PubMed/NCBI

[134] Google Scholar

[ref38] 38. Ganguly K, Dimitrov DF, Wallis JD, Carmena JM. Reversible large-scale modification of cortical networks during neuroprosthetic control. Nat Neurosci. 2011;14(5):662–7. pmid:21499255
View Article
PubMed/NCBI
Google Scholar

[136] View Article

[137] PubMed/NCBI

[138] Google Scholar

[ref39] 39. Chase SM, Kass RE, Schwartz AB. Behavioral and neural correlates of visuomotor adaptation observed through a brain-computer interface in primary motor cortex. J Neurophysiol. 2012;108(2):624–44. pmid:22496532
View Article
PubMed/NCBI
Google Scholar

[140] View Article

[141] PubMed/NCBI

[142] Google Scholar

[ref40] 40. Orsborn AL, Moorman HG, Overduin SA, Shanechi MM, Dimitrov DF, Carmena JM. Closed-loop decoder adaptation shapes neural plasticity for skillful neuroprosthetic control. Neuron. 2014;82(6):1380–93. pmid:24945777
View Article
PubMed/NCBI
Google Scholar

[144] View Article

[145] PubMed/NCBI

[146] Google Scholar

[ref41] 41. Shenoy KV, Carmena JM. Combining decoder design and neural adaptation in brain-machine interfaces. Neuron. 2014;84(4):665–80. pmid:25459407
View Article
PubMed/NCBI
Google Scholar

[148] View Article

[149] PubMed/NCBI

[150] Google Scholar

[ref42] 42. Mitchell JF, Sundberg KA, Reynolds JH. Differential attention-dependent response modulation across cell classes in macaque visual area V4. Neuron. 2007;55(1):131–41. pmid:17610822
View Article
PubMed/NCBI
Google Scholar

[152] View Article

[153] PubMed/NCBI

[154] Google Scholar

[ref43] 43. Constantinidis C, Goldman-Rakic PS. Correlated discharges among putative pyramidal neurons and interneurons in the primate prefrontal cortex. J Neurophysiol. 2002;88(6):3487–97. pmid:12466463
View Article
PubMed/NCBI
Google Scholar

[156] View Article

[157] PubMed/NCBI

[158] Google Scholar

[ref44] 44. Bruno RM, Simons DJ. Feedforward mechanisms of excitatory and inhibitory cortical receptive fields. J Neurosci. 2002;22(24):10966–75. pmid:12486192
View Article
PubMed/NCBI
Google Scholar

[160] View Article

[161] PubMed/NCBI

[162] Google Scholar

[ref45] 45. Swadlow HA. Fast-spike interneurons and feedforward inhibition in awake sensory neocortex. Cereb Cortex. 2003;13(1):25–32. pmid:12466212
View Article
PubMed/NCBI
Google Scholar

[164] View Article

[165] PubMed/NCBI

[166] Google Scholar

[ref46] 46. Frank LM, Brown EN, Wilson MA. A comparison of the firing properties of putative excitatory and inhibitory neurons from CA1 and the entorhinal cortex. J Neurophysiol. 2001;86(4):2029–40. pmid:11600659
View Article
PubMed/NCBI
Google Scholar

[168] View Article

[169] PubMed/NCBI

[170] Google Scholar

[ref47] 47. Dangi S, Gowda S, Moorman HG, Orsborn AL, So K, Shanechi M, et al. Continuous closed-loop decoder adaptation with a recursive maximum likelihood algorithm allows for rapid performance acquisition in brain-machine interfaces. Neural Comput. 2014;26(9):1811–39. pmid:24922501
View Article
PubMed/NCBI
Google Scholar

[172] View Article

[173] PubMed/NCBI

[174] Google Scholar

[ref48] 48. Athalye VR, Ganguly K, Costa RM, Carmena JM. Emergence of coordinated neural dynamics underlies neuroprosthetic learning and skillful control. Neuron. 2017;93:955-970.e5.
View Article
Google Scholar

[176] View Article

[177] Google Scholar

[ref49] 49. Ardid S, Vinck M, Kaping D, Marquez S, Everling S, Womelsdorf T. Mapping of functionally characterized cell classes onto canonical circuit operations in primate prefrontal cortex. J Neurosci. 2015;35(7):2975–91. pmid:25698735
View Article
PubMed/NCBI
Google Scholar

[179] View Article

[180] PubMed/NCBI

[181] Google Scholar

[ref50] 50. Oemisch M, Westendorff S, Azimi M, Hassani SA, Ardid S, Tiesinga P, et al. Feature-specific prediction errors and surprise across macaque fronto-striatal circuits. Nat Commun. 2019;10(1):176. pmid:30635579
View Article
PubMed/NCBI
Google Scholar

[183] View Article

[184] PubMed/NCBI

[185] Google Scholar

[ref51] 51. Oemisch M, Westendorff S, Everling S, Womelsdorf T. Interareal spike-train correlations of anterior cingulate and dorsal prefrontal cortex during attention shifts. J Neurosci. 2015;35(38):13076–89. pmid:26400938
View Article
PubMed/NCBI
Google Scholar

[187] View Article

[188] PubMed/NCBI

[189] Google Scholar

[ref52] 52. Ceccarelli F, Ferrucci L, Londei F, Ramawat S, Brunamonti E, Genovesio A. Static and dynamic coding in distinct cell types during associative learning in the prefrontal cortex. Nat Commun. 2023;14(1):8325. pmid:38097560
View Article
PubMed/NCBI
Google Scholar

[191] View Article

[192] PubMed/NCBI

[193] Google Scholar

[ref53] 53. Wasmuht DF, Spaak E, Buschman TJ, Miller EK, Stokes MG. Intrinsic neuronal dynamics predict distinct functional roles during working memory. Nat Commun. 2018;9(1):3499. pmid:30158572
View Article
PubMed/NCBI
Google Scholar

[195] View Article

[196] PubMed/NCBI

[197] Google Scholar

[ref54] 54. Parthasarathy A, Herikstad R, Bong JH, Medina FS, Libedinsky C, Yen S-C. Mixed selectivity morphs population codes in prefrontal cortex. Nat Neurosci. 2017;20(12):1770–9. pmid:29184197
View Article
PubMed/NCBI
Google Scholar

[199] View Article

[200] PubMed/NCBI

[201] Google Scholar

[ref55] 55. Wu W, Black M, Gao Y, Serruya M, Shaikhouni A, Donoghue J. Neural decoding of cursor motion using a Kalman filter. Adv Neural Inf Process Syst. 2002;15.
View Article
Google Scholar

[203] View Article

[204] Google Scholar

[ref56] 56. Dangi S, Orsborn AL, Moorman HG, Carmena JM. Design and analysis of closed-loop decoder adaptation algorithms for brain-machine interfaces. Neural Comput. 2013;25(7):1693–731. pmid:23607558
View Article
PubMed/NCBI
Google Scholar

[206] View Article

[207] PubMed/NCBI

[208] Google Scholar

[ref57] 57. Trainito C, von Nicolai C, Miller EK, Siegel M. Extracellular spike waveform dissociates four functionally distinct cell classes in primate cortex. Curr Biol. 2019;29(18):2973-2982.e5. pmid:31447374
View Article
PubMed/NCBI
Google Scholar

[210] View Article

[211] PubMed/NCBI

[212] Google Scholar

[ref58] 58. Kaufman MT, Churchland MM, Santhanam G, Yu BM, Afshar A, Ryu SI, et al. Roles of monkey premotor neuron classes in movement preparation and execution. J Neurophysiol. 2010;104(2):799–810. pmid:20538784
View Article
PubMed/NCBI
Google Scholar

[214] View Article

[215] PubMed/NCBI

[216] Google Scholar

[ref59] 59. Ferroni CG, Albertini D, Lanzilotto M, Livi A, Maranesi M, Bonini L. Local and system mechanisms for action execution and observation in parietal and premotor cortices. Curr Biol. 2021;31(13):2819-2830.e4. pmid:33984266
View Article
PubMed/NCBI
Google Scholar

[218] View Article

[219] PubMed/NCBI

[220] Google Scholar

[ref60] 60. Churchland MM, Cunningham JP, Kaufman MT, Ryu SI, Shenoy KV. Cortical preparatory activity: representation of movement or first cog in a dynamical machine?. Neuron. 2010;68(3):387–400. pmid:21040842
View Article
PubMed/NCBI
Google Scholar

[222] View Article

[223] PubMed/NCBI

[224] Google Scholar

[ref61] 61. Yu BM, Cunningham JP, Santhanam G, Ryu SI, Shenoy KV, Sahani M. Gaussian-process factor analysis for low-dimensional single-trial analysis of neural population activity. J Neurophysiol. 2009;102(1):614–35. pmid:19357332
View Article
PubMed/NCBI
Google Scholar

[226] View Article

[227] PubMed/NCBI

[228] Google Scholar

[ref62] 62. Kaufman MT, Churchland MM, Shenoy KV. The roles of monkey M1 neuron classes in movement preparation and execution. J Neurophysiol. 2013;110(4):817–25. pmid:23699057
View Article
PubMed/NCBI
Google Scholar

[230] View Article

[231] PubMed/NCBI

[232] Google Scholar

[ref63] 63. Connors BW, Gutnick MJ. Intrinsic firing patterns of diverse neocortical neurons. Trends Neurosci. 1990;13(3):99–104. pmid:1691879
View Article
PubMed/NCBI
Google Scholar

[234] View Article

[235] PubMed/NCBI

[236] Google Scholar

[ref64] 64. Scholl B, Wilson DE, Jaepel J, Fitzpatrick D. Functional logic of layer 2/3 inhibitory connectivity in the ferret visual cortex. Neuron. 2019;104(3):451-457.e3. pmid:31495646
View Article
PubMed/NCBI
Google Scholar

[238] View Article

[239] PubMed/NCBI

[240] Google Scholar

[ref65] 65. Freeman AW. A model for the origin of motion direction selectivity in visual cortex. J Neurosci. 2021;41(1):89–102. pmid:33203740
View Article
PubMed/NCBI
Google Scholar

[242] View Article

[243] PubMed/NCBI

[244] Google Scholar

[ref66] 66. Monier C, Chavane F, Baudot P, Graham LJ, Frégnac Y. Orientation and direction selectivity of synaptic inputs in visual cortical neurons: A diversity of combinations produces spike tuning. Neuron. 2003;37(4):663–80. pmid:12597863
View Article
PubMed/NCBI
Google Scholar

[246] View Article

[247] PubMed/NCBI

[248] Google Scholar

[ref67] 67. Tremblay R, Lee S, Rudy B. GABAergic interneurons in the neocortex: from cellular properties to circuits. Neuron. 2016;91(2):260–92. pmid:27477017
View Article
PubMed/NCBI
Google Scholar

[250] View Article

[251] PubMed/NCBI

[252] Google Scholar

[ref68] 68. Kinnischtzke AK, Simons DJ, Fanselow EE. Motor cortex broadly engages excitatory and inhibitory neurons in somatosensory barrel cortex. Cereb Cortex. 2014;24(8):2237–48. pmid:23547136
View Article
PubMed/NCBI
Google Scholar

[254] View Article

[255] PubMed/NCBI

[256] Google Scholar

[ref69] 69. Akkal D, Dum RP, Strick PL. Supplementary motor area and presupplementary motor area: Targets of basal ganglia and cerebellar output. J Neurosci. 2007;27(40):10659–73. pmid:17913900
View Article
PubMed/NCBI
Google Scholar

[258] View Article

[259] PubMed/NCBI

[260] Google Scholar

[ref70] 70. Takahashi N, Moberg S, Zolnik TA, Catanese J, Sachdev RNS, Larkum ME, et al. Thalamic input to motor cortex facilitates goal-directed action initiation. Curr Biol. 2021;31(18):4148-4155.e4. pmid:34302741
View Article
PubMed/NCBI
Google Scholar

[262] View Article

[263] PubMed/NCBI

[264] Google Scholar

[ref71] 71. DeFelipe J, Fariñas I. The pyramidal neuron of the cerebral cortex: Morphological and chemical characteristics of the synaptic inputs. Prog Neurobiol. 1992;39(6):563–607. pmid:1410442
View Article
PubMed/NCBI
Google Scholar

[266] View Article

[267] PubMed/NCBI

[268] Google Scholar

[ref72] 72. Markram H, Muller E, Ramaswamy S, Reimann MW, Abdellah M, Sanchez CA, et al. Reconstruction and simulation of neocortical microcircuitry. Cell 2015;163:456–92.
View Article
Google Scholar

[270] View Article

[271] Google Scholar

[ref73] 73. Snyder AC, Morais MJ, Smith MA. Dynamics of excitatory and inhibitory networks are differentially altered by selective attention. J Neurophysiol. 2016;116(4):1807–20. pmid:27466133
View Article
PubMed/NCBI
Google Scholar

[273] View Article

[274] PubMed/NCBI

[275] Google Scholar

Figures

Abstract

Introduction

Methods

Experimental models and procedures

Neural recordings

Behavioral tasks

Kalman filter decoder

Neural waveform classification

Tunning curves

Factor analysis

Percentage of explained variance

Results

Neuron classification

Unit contribution

Coordination in BW and NW neurons at population level

Differential Responses of BW and NW Neurons at the Single-Neuron Level

Discussion

Supporting information

S1 Fig. Two mixed Gaussian models of trough-to-peak intervals.

S2 Fig. Pie plots showing ~60% of all neurons are both tuned neurons.

S3 Fig. Example latent repertoire.

References