Output-null value distribution change little.

a. Output-potent and output-null space illustration. In our control paradigm, the cursor speed in a dimension is proportional to the difference of two opposing action values (potent), whereas the sum of two opposing action values does not influence cursor speed (null). b. Null-space analysis of neural learning. We averaged the output-null values a1 + a2 across time in each trial, and compare the output-null values from the early sessions versus the late sessions, pooling data from two monkeys and four directions. The null component average of the late sessions was slightly larger than that in the early sessions, (ANOVA, **p 10−10), this effect mostly comes from monkey T, as there was no difference in monkey K.</p

Neuronal PD change across learning, monkey T.

Each column represents a group of neurons and each pie plot corresponds to a session. In each pie plot, the shaded area shows neurons’ assigned direction (AD) and the colored bars show neuron’s tuning (PD). In early learning (session 1), the neurons’ PDs are not close to their AD (except for group 3). However, in the late sessions (15 and 16), neurons’ PDs are closer to the AD. (DOCX)</p

Tuning depth and <i>R</i>² of direct and indirect neurons indicate learning occurred.

a. Tuning depth (Hz) from early learning and late learning periods (first and last 1/4 of sessions) of direct and indirect neurons are shown. No significant difference was found (p = 0.4665, t-test). Note the difference between the direct and indirect groups is due to our neuron inclusion criteria. b. Comparison between early and late learning period shows an increase in tuning R2. The direct neurons R2 ranges from 1 * 10−4 to 0.63 (data pooled between monkeys).</p

High-Performance Flexible Electrochromic Supercapacitor with a Capability of Quantitative Visualization of Its Energy Storage Status through Electrochromic Contrast

Flexible electrochromic supercapacitors (ECSCs) are currently under considerable investigation as potential smart energy storage components in wearable intelligent electronics. However, the lack of a suitable strategy for precisely judging its real-time energy storage status has hindered its development toward practical application. Herein, an optical-energy feedback strategy based on electrochromic contrast as a quantitative indicator of the state of charge (SOC) in flexible ECSCs has been developed using a polypyrrole (PPy)-based electrochromic electrode: carbon nanotubes (CNTs)/Au/PPy/poly­(ethylene terephthalate) (PET). A linear dependence of real-time electrochromic contrast on the SOC is established, enabling a quantitative, accurate, and on-site visualization of the energy level from digitized information. Benefiting from the inherent high color-to-color contrast in PPy during electrochromism, the strategy shows a high detection sensitivity and resolution during charging/discharging. Moreover, the good flexibility, high capacitance of the electrode, and asymmetric device design can synergistically benefit the energy storage performance of the flexible ECSC, resulting in a high energy density of 4.03 μWh cm–2 and high electrochemical stability under deformations. This work largely explores the potential and advantages of PPy for fabricating a high-performance flexible smart supercapacitor and opens up an alternative methodology for realizing a convenient, low-cost, and nondestructive SOC self-monitoring ability in flexible energy storage devices

The tuning change of individual neurons.

a. An example neuron with changing preferred direction. x- and y- axes are the 2D space of movements and the z-axis is session. Each disk represents a session. The blue bar on the disk represents the neuron’s preferred direction, and the shaded area on the disk represents the neuron’s assigned direction. It can be seen that the preferred direction changes towards the assigned direction. b. Changes in direct neurons’ PD over time. The PD of direct neurons in early and late learning periods are compared. Data from two monkeys and all four directions are pooled together. We calculate the normalized |PD − AD|, which represents the directional difference between the neurons’ PD and their assigned direction (AD). It is 1 for opposite directions and 0 for the same direction. When comparing early and late learning stages, this value becomes smaller, indicating that the tuning is shifting towards the assigned direction.</p

Output-potent activity increase significantly.

We calculated the output-potent a1 − a2 value for each monkey and each directions and pool the data. The output-potent distribution for both monkeys changed to more positive from early (blue) to late (red) sessions. Lines indicate distribution mean (horizontal placement). (ANOVA, * * p 10−10).</p

Bio-feedback BMI paradigm, array implantation, and neuronal signals.

a. Schematic illustration of groups. The summed and normalized firing rate of each group of 4 neurons provides an action value. Four action values correspond to four opposing directions in two dimensions of cursor speed. b. Grouping neurons by preferred direction. We divide the 2D space of linear velocity encoding model coefficients (b1, b2 in Eq 4) into four quadrants, corresponding to each direction. We select neurons based on their preferred directions, encoding strength, and signal stability. Inset shows the same data plotted on a larger range so that all recorded neurons are visible. c. We implanted Utah microelectrode arrays into primary motor cortex hand representational area. Photo shows surgery for Monkey T. A: anterior, L: lateral. d. Sample spike waveforms 44 days after implantation for Monkey T. Waveforms of different colors indicate different units (for visualization only, we did not sort units for group weight control), and waveform thickness represents plus and minus one-half standard deviation. Panels are placed according to positions on the Utah array (wire bundle at bottom). Color shading per channel indicates group assignment. e. Experimental task. The monkey sat before a screen displaying the brain-controlled cursor (green dot) and task target (white ring) and uses brain activity to control the cursor. After moving the cursor into the target, the monkey receives a water reward.</p

Distance between neuronal PD and assigned directions AD across learning, both monkeys.

X-axis is time (session number) and Y-axis is the normalized |PD—AD|, which is 1 for opposite direction and 0 for same direction. Thus, the directional difference is mapped from [-π, π] to [0,1]. The sector of the assigned direction [-45°, 45°] (shaded sector in S1 Fig) is mapped to [0,0.25], and values lower than 0.5 indicate the channel contributes to the direction of movement. Most channels’ |PD–AD| is smaller than 0.5, showing that they contribute to the movement direction. (DOCX)</p

Monkey task performance.

a. Trial count and trial success rate. X-axis indicates the session index (two sessions per day), and y-axis indicates the trial count (total: brown, successful: yellow) and trial success rate (black dots). Success rate data were fitted with a logistic function (black curve). Success rate was always greater than shuffled baseline (grey line). The number of successful trials and the success rate for both monkeys significantly increased with session, indicating that both monkeys could learn the group weight control paradigm. b. Success rate changes for each direction. The task of Monkey T had four possible targets; thus, we separate trials according to target. The success rate for each target increased through learning, with up and left learned early in training, whereas right and down were learned later. The task of Monkey K is not categorical, so here we divide target angles into four angular bins. Monkey K’s success rates of different directions also increase at different times in the training period.</p