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Computing Arm Movements with a Monkey Brainet.

Ramakrishnan A, Ifft PJ, Pais-Vieira M, Byun YW, Zhuang KZ, Lebedev MA, Nicolelis MA - Sci Rep (2015)

Bottom Line: Here, we introduce a Brainet that utilizes very-large-scale brain activity (VLSBA) from two (B2) or three (B3) nonhuman primates to engage in a common motor behaviour.With long-term training we observed increased coordination of behavior, increased correlations in neuronal activity between different brains, and modifications to neuronal representation of the motor plan.These results suggest that primate brains can be integrated into a Brainet, which self-adapts to achieve a common motor goal.

View Article: PubMed Central - PubMed

Affiliation: 1] Department of Neurobiology, Duke University, Durham, NC, USA [2] Duke University Center for Neuroengineering, Duke University, Durham, NC, USA.

ABSTRACT
Traditionally, brain-machine interfaces (BMIs) extract motor commands from a single brain to control the movements of artificial devices. Here, we introduce a Brainet that utilizes very-large-scale brain activity (VLSBA) from two (B2) or three (B3) nonhuman primates to engage in a common motor behaviour. A B2 generated 2D movements of an avatar arm where each monkey contributed equally to X and Y coordinates; or one monkey fully controlled the X-coordinate and the other controlled the Y-coordinate. A B3 produced arm movements in 3D space, while each monkey generated movements in 2D subspaces (X-Y, Y-Z, or X-Z). With long-term training we observed increased coordination of behavior, increased correlations in neuronal activity between different brains, and modifications to neuronal representation of the motor plan. Overall, performance of the Brainet improved owing to collective monkey behaviour. These results suggest that primate brains can be integrated into a Brainet, which self-adapts to achieve a common motor goal.

No MeSH data available.


Related in: MedlinePlus

Triad (B3) control of 3D movements.(A) Boxplots comparing target acquisition time (left panel), trial duration (centre panel), inter-reward interval (right panel). (B) Reduction in trial duration (left panel) and concurrent increase in the reward rate (right panel) with conjoint training across weeks. (C) Normalized contribution of each of the three monkeys across a representative subset of 30 trials. The relative contribution of each monkey varied from trial to trial. (D) Fraction of trials that were correctly performed by a dyad (black) as a triad (green), or incorrectly (purple) shifted across the 11 triad experiments. The fraction of total trials with a rewarded outcome in which all three monkeys contributed (green), or those in which two monkeys contributed (black) increased significantly within each week and across sessions whereas the fraction of erroneous/unattempted trials reduced significantly. (E,F) Decoded trajectories and neural data from the triad experiment. (E) Mean X,Y,Z traces produced by individual monkeys shown separately by colour among trials where all monkeys contributed (left column) or when only monkeys M and C contributed (right column). Mean X,Y,Z (the value used to move avatar) shown in black. Distance to target in each axis is 5 screen-cm. When one monkey (monkey K) opted out, the working dyad generated higher-amplitude trajectories (Right column, X axis and Z axis) as opposed to when all the members contributed (left column). (F) PETHs aligned on target onset for same trial subsets as in (E) Rows represent individual neurons and colour indicates normalized firing rate (z-score). Neurons from different monkeys marked by colour along right edge (same colours as in (E)). Increased effort by the working dyad also resulted in stronger cortical modulations between the members (right panel) as compared to when all the members contributed (left panel).
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f4: Triad (B3) control of 3D movements.(A) Boxplots comparing target acquisition time (left panel), trial duration (centre panel), inter-reward interval (right panel). (B) Reduction in trial duration (left panel) and concurrent increase in the reward rate (right panel) with conjoint training across weeks. (C) Normalized contribution of each of the three monkeys across a representative subset of 30 trials. The relative contribution of each monkey varied from trial to trial. (D) Fraction of trials that were correctly performed by a dyad (black) as a triad (green), or incorrectly (purple) shifted across the 11 triad experiments. The fraction of total trials with a rewarded outcome in which all three monkeys contributed (green), or those in which two monkeys contributed (black) increased significantly within each week and across sessions whereas the fraction of erroneous/unattempted trials reduced significantly. (E,F) Decoded trajectories and neural data from the triad experiment. (E) Mean X,Y,Z traces produced by individual monkeys shown separately by colour among trials where all monkeys contributed (left column) or when only monkeys M and C contributed (right column). Mean X,Y,Z (the value used to move avatar) shown in black. Distance to target in each axis is 5 screen-cm. When one monkey (monkey K) opted out, the working dyad generated higher-amplitude trajectories (Right column, X axis and Z axis) as opposed to when all the members contributed (left column). (F) PETHs aligned on target onset for same trial subsets as in (E) Rows represent individual neurons and colour indicates normalized firing rate (z-score). Neurons from different monkeys marked by colour along right edge (same colours as in (E)). Increased effort by the working dyad also resulted in stronger cortical modulations between the members (right panel) as compared to when all the members contributed (left panel).

Mentions: Clear behavioural improvements occurred during the brain control epochs over a span of three weeks of training. We observed a significant reduction in target acquisition time (p < 0.02; KS test), trial duration (p < 0.03; KS test) and inter-reward interval (p < 0.002; KS test) between early and late sessions (Fig. 4A). The mean trial duration significantly decreased (4.25 to 3.65 s, 1-way ANOVA: p < 0.05, Fig. 4B panel on the left) over the span of three weeks and the mean reward rate increased from 6 to 10 trials per minute (Fig. 4B, panel on the right). Across 11 sessions, the B3 significantly improved its performance from 20% correct trials in the first session to 78% correct trials in the last session (P < 0.01, 1-way ANOVA with bootstrapping, green+black bars in Fig. 4D).


Computing Arm Movements with a Monkey Brainet.

Ramakrishnan A, Ifft PJ, Pais-Vieira M, Byun YW, Zhuang KZ, Lebedev MA, Nicolelis MA - Sci Rep (2015)

Triad (B3) control of 3D movements.(A) Boxplots comparing target acquisition time (left panel), trial duration (centre panel), inter-reward interval (right panel). (B) Reduction in trial duration (left panel) and concurrent increase in the reward rate (right panel) with conjoint training across weeks. (C) Normalized contribution of each of the three monkeys across a representative subset of 30 trials. The relative contribution of each monkey varied from trial to trial. (D) Fraction of trials that were correctly performed by a dyad (black) as a triad (green), or incorrectly (purple) shifted across the 11 triad experiments. The fraction of total trials with a rewarded outcome in which all three monkeys contributed (green), or those in which two monkeys contributed (black) increased significantly within each week and across sessions whereas the fraction of erroneous/unattempted trials reduced significantly. (E,F) Decoded trajectories and neural data from the triad experiment. (E) Mean X,Y,Z traces produced by individual monkeys shown separately by colour among trials where all monkeys contributed (left column) or when only monkeys M and C contributed (right column). Mean X,Y,Z (the value used to move avatar) shown in black. Distance to target in each axis is 5 screen-cm. When one monkey (monkey K) opted out, the working dyad generated higher-amplitude trajectories (Right column, X axis and Z axis) as opposed to when all the members contributed (left column). (F) PETHs aligned on target onset for same trial subsets as in (E) Rows represent individual neurons and colour indicates normalized firing rate (z-score). Neurons from different monkeys marked by colour along right edge (same colours as in (E)). Increased effort by the working dyad also resulted in stronger cortical modulations between the members (right panel) as compared to when all the members contributed (left panel).
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4497496&req=5

f4: Triad (B3) control of 3D movements.(A) Boxplots comparing target acquisition time (left panel), trial duration (centre panel), inter-reward interval (right panel). (B) Reduction in trial duration (left panel) and concurrent increase in the reward rate (right panel) with conjoint training across weeks. (C) Normalized contribution of each of the three monkeys across a representative subset of 30 trials. The relative contribution of each monkey varied from trial to trial. (D) Fraction of trials that were correctly performed by a dyad (black) as a triad (green), or incorrectly (purple) shifted across the 11 triad experiments. The fraction of total trials with a rewarded outcome in which all three monkeys contributed (green), or those in which two monkeys contributed (black) increased significantly within each week and across sessions whereas the fraction of erroneous/unattempted trials reduced significantly. (E,F) Decoded trajectories and neural data from the triad experiment. (E) Mean X,Y,Z traces produced by individual monkeys shown separately by colour among trials where all monkeys contributed (left column) or when only monkeys M and C contributed (right column). Mean X,Y,Z (the value used to move avatar) shown in black. Distance to target in each axis is 5 screen-cm. When one monkey (monkey K) opted out, the working dyad generated higher-amplitude trajectories (Right column, X axis and Z axis) as opposed to when all the members contributed (left column). (F) PETHs aligned on target onset for same trial subsets as in (E) Rows represent individual neurons and colour indicates normalized firing rate (z-score). Neurons from different monkeys marked by colour along right edge (same colours as in (E)). Increased effort by the working dyad also resulted in stronger cortical modulations between the members (right panel) as compared to when all the members contributed (left panel).
Mentions: Clear behavioural improvements occurred during the brain control epochs over a span of three weeks of training. We observed a significant reduction in target acquisition time (p < 0.02; KS test), trial duration (p < 0.03; KS test) and inter-reward interval (p < 0.002; KS test) between early and late sessions (Fig. 4A). The mean trial duration significantly decreased (4.25 to 3.65 s, 1-way ANOVA: p < 0.05, Fig. 4B panel on the left) over the span of three weeks and the mean reward rate increased from 6 to 10 trials per minute (Fig. 4B, panel on the right). Across 11 sessions, the B3 significantly improved its performance from 20% correct trials in the first session to 78% correct trials in the last session (P < 0.01, 1-way ANOVA with bootstrapping, green+black bars in Fig. 4D).

Bottom Line: Here, we introduce a Brainet that utilizes very-large-scale brain activity (VLSBA) from two (B2) or three (B3) nonhuman primates to engage in a common motor behaviour.With long-term training we observed increased coordination of behavior, increased correlations in neuronal activity between different brains, and modifications to neuronal representation of the motor plan.These results suggest that primate brains can be integrated into a Brainet, which self-adapts to achieve a common motor goal.

View Article: PubMed Central - PubMed

Affiliation: 1] Department of Neurobiology, Duke University, Durham, NC, USA [2] Duke University Center for Neuroengineering, Duke University, Durham, NC, USA.

ABSTRACT
Traditionally, brain-machine interfaces (BMIs) extract motor commands from a single brain to control the movements of artificial devices. Here, we introduce a Brainet that utilizes very-large-scale brain activity (VLSBA) from two (B2) or three (B3) nonhuman primates to engage in a common motor behaviour. A B2 generated 2D movements of an avatar arm where each monkey contributed equally to X and Y coordinates; or one monkey fully controlled the X-coordinate and the other controlled the Y-coordinate. A B3 produced arm movements in 3D space, while each monkey generated movements in 2D subspaces (X-Y, Y-Z, or X-Z). With long-term training we observed increased coordination of behavior, increased correlations in neuronal activity between different brains, and modifications to neuronal representation of the motor plan. Overall, performance of the Brainet improved owing to collective monkey behaviour. These results suggest that primate brains can be integrated into a Brainet, which self-adapts to achieve a common motor goal.

No MeSH data available.


Related in: MedlinePlus