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Brain-machine interfaces (BMIs) provide a framework for studying how cortical population dynamics evolve over learning in a task in which the mapping between neural activity and behavior is precisely defined. Learning to control a BMI is associated with the emergence of coordinated neural dynamics in populations of neurons whose activity serves as direct input to the BMI decoder (direct subpopulation). While previous work shows differential modification of firing rate modulation in this population relative to a population whose activity was not directly input to the BMI decoder (indirect subpopulation), little is known about how learning-related changes in cortical population dynamics within these groups compare.To investigate this, we monitored both direct and indirect subpopulations as two macaque monkeys learned to control a BMI. We found that while the combined population increased coordinated neural dynamics, this increase in coordination was primarily driven by changes in the direct subpopulation. These findings suggest that motor cortex refines cortical dynamics by increasing neural variance throughout the entire population during learning, with a more pronounced coordination of firing activity in subpopulations that are causally linked to behavior.

During motor learning,1 as well as during neuroprosthetic learning,2-4 animals learn to control motor cortex activity in order to generate behavior. Two different populations of motor cortex neurons, intra-telencephalic (IT) and pyramidal tract (PT) neurons, convey the resulting cortical signals within and outside the telencephalon. Although a large amount of evidence demonstrates contrasting functional organization among both populations,5,6 it is unclear whether the brain can equally learn to control the activity of either class of motor cortex neurons. To answer this question, we used a calcium-imaging-based brain-machine interface (CaBMI)3 and trained different groups of mice to modulate the activity of either IT or PT neurons in order to receive a reward. We found that the animals learned to control PT neuron activity faster and better than IT neuron activity. Moreover, our findings show that the advantage of PT neurons is the result of characteristics inherent to this population as well as their local circuitry and cortical depth location. Taken together, our results suggest that the motor cortex is more efficient at controlling the activity of pyramidal tract neurons, which are embedded deep in the cortex, and relaying motor commands outside the telencephalon.

Advancements in novel neurotechnologies, such as brain computer interfaces (BCI) and neuromodulatory devices such as deep brain stimulators (DBS), will have profound implications for society and human rights. While these technologies are improving the diagnosis and treatment of mental and neurological diseases, they can also alter individual agency and estrange those using neurotechnologies from their sense of self, challenging basic notions of what it means to be human. As an international coalition of interdisciplinary scholars and practitioners, we examine these challenges and make recommendations to mitigate negative consequences that could arise from the unregulated development or application of novel neurotechnologies. We explore potential ethical challenges in four key areas: identity and agency, privacy, bias, and enhancement. To address them, we propose (1) democratic and inclusive summits to establish globally-coordinated ethical and societal guidelines for neurotechnology development and application, (2) new measures, including "Neurorights," for data privacy, security, and consent to empower neurotechnology users' control over their data, (3) new methods of identifying and preventing bias, and (4) the adoption of public guidelines for safe and equitable distribution of neurotechnological devices.

Microendoscopic calcium imaging with one-photon miniature microscopes enables unprecedented readout of neural circuit dynamics during active behavior in rodents. In this study, we describe successful application of this technology in the rhesus macaque, demonstrating plug-and-play, head-mounted recordings of cellular-resolution calcium dynamics from large populations of neurons simultaneously in bilateral dorsal premotor cortices during performance of a naturalistic motor reach task. Imaging is stable over several months, allowing us to longitudinally track individual neurons and monitor their relationship to motor behavior over time. We observe neuronal calcium dynamics selective for reach direction, which we could use to decode the animal's trial-by-trial motor behavior. This work establishes head-mounted microendoscopic calcium imaging in macaques as a powerful approach for studying the neural circuit mechanisms underlying complex and clinically relevant behaviors, and it promises to greatly advance our understanding of human brain function, as well as its dysfunction in neurological disease.

Miniaturization of implantable neural recording systems to micron-scale volumes will enable minimally invasive implantation and alleviate cortical scarring, gliosis, and resulting signal degradation. Ultrasound (US) power transmission has been demonstrated to have high efficiency and low tissue attenuation for mm-scale implants at depth in tissue [1, 2, 3], but has not been demonstrated with precision recording circuitry. We present an US implantable wireless neural recording system scaled to 0.8mm3, verified to safely operate at 5cm depth with state of the art neural recording performance an average circuit power dissipation of 13μW, and 28.8μW including power conversion efficiency. Sub-mm scale is achieved through single-link power and communication on a single piezocrystal (Lead Zirconate Titanate, PZT) utilizing linear analog backscattering, small die area, and eliminating all other off-chip components.

Closed-loop neuromodulation systems aim to treat a variety of neurological conditions by delivering and adjusting therapeutic electrical stimulation in response to a patient's neural state, recorded in real time. Existing systems are limited by low channel counts, lack of algorithmic flexibility, and the distortion of recorded signals by large and persistent stimulation artefacts. Here, we describe an artefact-free wireless neuromodulation device that enables research applications requiring high-throughput data streaming, low-latency biosignal processing, and simultaneous sensing and stimulation. The device is a miniaturized neural interface capable of closed-loop recording and stimulation on 128 channels, with on-board processing to fully cancel stimulation artefacts. In addition, it can detect neural biomarkers and automatically adjust stimulation parameters in closed-loop mode. In a behaving non-human primate, the device enabled long-term recordings of local field potentials and the real-time cancellation of stimulation artefacts, as well as closed-loop stimulation to disrupt movement preparatory activity during a delayed-reach task. The neuromodulation device may help advance neuroscientific discovery and preclinical investigations of stimulation-based therapeutic interventions.

Neural stimulation is a powerful technique for modulating physiologicalfunctions and for writing information into the nervous system as part ofbrain-machine interfaces. Current clinically approved neural stimulatorsrequire batteries and are many cubic centimetres in size -- typically muchlarger than their intended targets. We present a complete wireless neuralstimulation system consisting of a 1.7 mm3 wireless, batteryless, leadlessimplantable stimulator (the "mote"), an ultrasonic wireless link for power andbi-directional communication, and a hand-held external transceiver. The moteconsists of a piezoceramic transducer, an energy storage capacitor, and astimulator integrated circuit (IC). The IC harvests ultrasonic power with highefficiency, decodes stimulation parameter downlink data, and generatescurrent-controlled stimulation pulses. Stimulation parameters are time-encodedon the fly through the wireless link rather than being programmed and stored onthe mote, reducing power consumption and on-chip memory requirements andenabling complex stimulation protocols with high-temporal resolution andlow-latency feedback for use in closed-loop stimulation. Uplink data indicateswhether the mote is currently stimulating; it is encoded by the mote viabackscatter modulation and is demodulated at the external transceiver. We showthat the mote operates at an acoustic intensity that is 7.8% of the FDA limitfor diagnostic ultrasound and characterize the acoustic wireless link'srobustness to expected real-world misalignment. We demonstrate the in vivoperformance of the system with motes acutely implanted with a cuff on thesciatic nerve of anesthetized rats and show highly repeatable stimulationacross a wide range of physiological responses.

We present a 6.5mm3, 10mg, wireless peripheral nerve stimulator. The stimulator is powered and controlled through ultrasound from an external transducer and utilizes a single 750×750×750μm3 piezocrystal for downlink communication, powering, and readout, reducing implant volume and mass. An IC with 0.06mm2 active circuit area, designed in TSMC 65nm LPCMOS process, converts harvested ultrasound to stimulation charge with a peak efficiency of 82%. A custom wireless protocol that does not require a clock or memory circuits reduces on-chip power to 4μW when not stimulating. The encapsulated stimulator was cuffed to the sciatic nerve of an anesthetized rodent and demonstrated full-scale nerve activation in vivo. We achieve a highly efficient and temporally precise wireless peripheral nerve stimulator that is the smallest and lightest to our knowledge.

Artificial intelligence and brain-computer interfaces must respect and preserve people’s privacy, identity, agency and equality, say Rafael Yuste, Sara Goering and colleagues.

Value-based decision-making involves an assessment of the value of items available and the actions required to obtain them. The basal ganglia are highly implicated in action selection and goal-directed behavior [1-4], and the striatum in particular plays a critical role in arbitrating between competing choices [5-9]. Previous work has demonstrated that neural activity in the caudate nucleus is modulated by task-relevant action values [6, 8]. Nonetheless, how value is represented and maintained in the striatum remains unclear since decision-making in these tasks relied on spatially lateralized responses, confounding the ability to generalize to a more abstract choice task [6, 8, 9]. Here, we investigate striatal value representations by applying caudate electrical stimulation in macaque monkeys (n = 3) to bias decision-making in a task that divorces the value of a stimulus from motor action. Electrical microstimulation is known to induce neural plasticity [10, 11], and caudate microstimulation in primates has been shown to accelerate associative learning [12, 13]. Our results indicate that stimulation paired with a particular stimulus increases selection of that stimulus, and this effect was stimulus dependent and action independent. The modulation of choice behavior using microstimulation was best modeled as resulting from changes in stimulus value. Caudate neural recordings (n = 1) show that changes in value-coding neuron activity are stimulus value dependent. We argue that caudate microstimulation can differentially increase stimulus values independent of action, and unilateral manipulations of value are sufficient to mediate choice behavior. These results support potential future applications of microstimulation to correct maladaptive plasticity underlying dysfunctional decision-making related to neuropsychiatric conditions.

Parkinson's disease (PD) is characterized by motor symptoms such as rigidity and bradykinesia that prevent normal movement. Beta band oscillations (13-30 Hz) in neural local field potentials (LFPs) have been associated with these motor symptoms. Here, three PD patients implanted with a therapeutic deep brain neural stimulator that can also record and wirelessly stream neural data played a neurofeedback game where they modulated their beta band power from sensorimotor cortical areas. Patients' beta band power was streamed in real-time to update the position of a cursor that they tried to drive into a cued target. After playing the game for 1-2 hours each, all three patients exhibited above chance-level performance regardless of subcortical stimulation levels. This study, for the first time, demonstrates using an invasive neural recording system for at-home neurofeedback training. Future work will investigate chronic neurofeedback training as a potentially therapeutic tool for patients with neurological disorders.

We present an 180nm HV CMOS IC for concurrent neural stimulation and recording that combines 64 low-noise recording front-ends and 4 independent stimulators multiplexed to any of the 64 channels. The stimulators have 5mA peak current, 12V compliance and dynamic power management to maximize efficiency. Co-design of the stimulation and recording subsystems resulted in 100mV of recording linear range, 70nV/rtHz noise, and a rapid 1ms (single-sample) artifact recovery during stimulation.

Emerging applications in brain-machine interface systems require high-resolution, chronic multisite cortical recordings, which cannot be obtained with existing technologies due to high power consumption, high invasiveness, or inability to transmit data wirelessly. In this paper, we describe a microsystem based on electrocorticography (ECoG) that overcomes these difficulties, enabling chronic recording and wireless transmission of neural signals from the surface of the cerebral cortex. The device is comprised of a highly flexible, high-density, polymer-based 64-channel electrode array and a flexible antenna, bonded to 2.4 mm × 2.4 mm CMOS integrated circuit (IC) that performs 64-channel acquisition, wireless power and data transmission. The IC digitizes the signal from each electrode at 1 kS/s with 1.2 μV input referred noise, and transmits the serialized data using a 1 Mb/s backscattering modulator. A dual-mode power-receiving rectifier reduces data-dependent supply ripple, enabling the integration of small decoupling capacitors on chip and eliminating the need for external components. Design techniques in the wireless and baseband circuits result in over 16× reduction in die area with a simultaneous 3× improvement in power efficiency over the state of the art. The IC consumes 225 μW and can be powered by an external reader transmitting 12 mW at 300 MHz, which is over 3× lower than IEEE and FCC regulations.

Brain-machine interfaces are not only promising for neurological applications, but also powerful for investigating neuronal ensemble dynamics during learning. We trained mice to operantly control an auditory cursor using spike-related calcium signals recorded with two-photon imaging in motor and somatosensory cortex. Mice rapidly learned to modulate activity in layer 2/3 neurons, evident both across and within sessions. Learning was accompanied by modifications of firing correlations in spatially localized networks at fine scales.

We present a method for decreasing the duration of artifacts present during intra-cortical microstimulation (ICMS) recordings by using techniques developed for digital communications. We replace the traditional monophasic or biphasic current stimulation pulse with a patterned pulse stream produced by a Zero Forcing Equalizer (ZFE) filter after characterizing the artifact as a communications channel. The results find that using the ZFE stimulus has the potential to reduce artifact width by more than 70%. Considerations for the hardware implementation of the equalizer are presented.

Cortical control of neuroprosthetic devices is known to require neuronal adaptations. It remains unclear whether a stable cortical representation for prosthetic function can be stored and recalled in a manner that mimics our natural recall of motor skills. Especially in light of the mixed evidence for a stationary neuron-behavior relationship in cortical motor areas, understanding this relationship during long-term neuroprosthetic control can elucidate principles of neural plasticity as well as improve prosthetic function. Here, we paired stable recordings from ensembles of primary motor cortex neurons in macaque monkeys with a constant decoder that transforms neural activity to prosthetic movements. Proficient control was closely linked to the emergence of a surprisingly stable pattern of ensemble activity, indicating that the motor cortex can consolidate a neural representation for prosthetic control in the presence of a constant decoder. The importance of such a cortical map was evident in that small perturbations to either the size of the neural ensemble or to the decoder could reversibly disrupt function. Moreover, once a cortical map became consolidated, a second map could be learned and stored. Thus, long-term use of a neuroprosthetic device is associated with the formation of a cortical map for prosthetic function that is stable across time, readily recalled, resistant to interference, and resembles a putative memory engram.