Pronounced activity is observed in both hemispheres of the motor cortex during preparation and execution of unimanual movements. The organizational principles of bi-hemispheric signals and the functions they serve throughout motor planning remain unclear. Using an instructed-delay reaching task in monkeys, we identified two components in population responses spanning PMd and M1. A "dedicated" component, which segregated activity at the level of individual units, emerged in PMd during preparation. It was most prominent following movement when M1 became strongly engaged, and principally involved the contralateral hemisphere. In contrast to recent reports, these dedicated signals solely accounted for divergence of arm-specific neural subspaces. The other "distributed" component mixed signals for each arm within units, and the subspace containing it did not discriminate between arms at any stage. The statistics of the population response suggest two functional aspects of the cortical network: one that spans both hemispheres for supporting preparatory and ongoing processes, and another that is predominantly housed in the contralateral hemisphere and specifies unilateral output.

We believe that this special issue will serve to increase the public awareness and foster discussions on the multiple worldwide BRAIN initiatives, both within and outside the IEEE, providing an impetus for development of long-term cost-effective healthcare solutions. We also believe that the topics presented in this special issue will serve as scientific evidence for health and policy advocates of the value of neurotechnologies for improving the neurological and mental health and wellbeing of the general population. Below we briefly highlight the papers and technologies in this special issue.

This review focuses on recent directions stemming from work by the authors and collaborators in the emerging field of neurotechnology. Neurotechnology has the potential to provide a greater understanding of the structure and function of the complex neural circuits in the brain, as well as impacting the field of brain-machine interfaces (BMI). We envision ultralow-power wireless neural interface systems that are life-lasting, fully integrated, and that supports bidirectional data flow with high bandwidth. Moreover, we believe in the importance of building neural interface technology that is truly tetherless, has a very small recording footprint, and little to no mechanical coupling between the sensor and the external world. We believe these developments will impact both neuroscience and neurology, revealing fundamental insight about how the nervous system functions in health and disease.

Much progress has been made in brain-machine interfaces (BMI) using decoders such as Kalman filters and finding their parameters with closed-loop decoder adaptation (CLDA). However, current decoders do not model the spikes directly, and hence may limit the processing time-scale of BMI control and adaptation. Moreover, while specialized CLDA techniques for intention estimation and assisted training exist, a unified and systematic CLDA framework that generalizes across different setups is lacking. Here we develop a novel closed-loop BMI training architecture that allows for processing, control, and adaptation using spike events, enables robust control and extends to various tasks. Moreover, we develop a unified control-theoretic CLDA framework within which intention estimation, assisted training, and adaptation are performed. The architecture incorporates an infinite-horizon optimal feedback-control (OFC) model of the brain's behavior in closed-loop BMI control, and a point process model of spikes. The OFC model infers the user's motor intention during CLDA-a process termed intention estimation. OFC is also used to design an autonomous and dynamic assisted training technique. The point process model allows for neural processing, control and decoder adaptation with every spike event and at a faster time-scale than current decoders; it also enables dynamic spike-event-based parameter adaptation unlike current CLDA methods that use batch-based adaptation on much slower adaptation time-scales. We conducted closed-loop experiments in a non-human primate over tens of days to dissociate the effects of these novel CLDA components. The OFC intention estimation improved BMI performance compared with current intention estimation techniques. OFC assisted training allowed the subject to consistently achieve proficient control. Spike-event-based adaptation resulted in faster and more consistent performance convergence compared with batch-based methods, and was robust to parameter initialization. Finally, the architecture extended control to tasks beyond those used for CLDA training. These results have significant implications towards the development of clinically-viable neuroprosthetics.

Closed-loop decoder adaptation (CLDA) is an emerging paradigm for both improving and maintaining online performance in brain-machine interfaces (BMIs). The time required for initial decoder training and any subsequent decoder recalibrations could be potentially reduced by performing continuous adaptation, in which decoder parameters are updated at every time step during these procedures, rather than waiting to update the decoder at periodic intervals in a more batch-based process. Here, we present recursive maximum likelihood (RML), a CLDA algorithm that performs continuous adaptation of a Kalman filter decoder's parameters. We demonstrate that RML possesses a variety of useful properties and practical algorithmic advantages. First, we show how RML leverages the accuracy of updates based on a batch of data while still adapting parameters on every time step. Second, we illustrate how the RML algorithm is parameterized by a single, intuitive half-life parameter that can be used to adjust the rate of adaptation in real time. Third, we show how even when the number of neural features is very large, RML's memory-efficient recursive update rules can be reformulated to also be computationally fast so that continuous adaptation is still feasible. To test the algorithm in closed-loop experiments, we trained three macaque monkeys to perform a center-out reaching task by using either spiking activity or local field potentials to control a 2D computer cursor. RML achieved higher levels of performance more rapidly in comparison to a previous CLDA algorithm that adapts parameters on a more intermediate timescale. Overall, our results indicate that RML is an effective CLDA algorithm for achieving rapid performance acquisition using continuous adaptation.

Closed-loop decoder adaptation (CLDA) is an emerging paradigm for achieving rapid performance improvements in online brain-machine interface (BMI) operation. Designing an effective CLDA algorithm requires making multiple important decisions, including choosing the timescale of adaptation, selecting which decoder parameters to adapt, crafting the corresponding update rules, and designing CLDA parameters. These design choices, combined with the specific settings of CLDA parameters, will directly affect the algorithm's ability to make decoder parameters converge to values that optimize performance. In this article, we present a general framework for the design and analysis of CLDA algorithms and support our results with experimental data of two monkeys performing a BMI task. First, we analyze and compare existing CLDA algorithms to highlight the importance of four critical design elements: the adaptation timescale, selective parameter adaptation, smooth decoder updates, and intuitive CLDA parameters. Second, we introduce mathematical convergence analysis using measures such as mean-squared error and KL divergence as a useful paradigm for evaluating the convergence properties of a prototype CLDA algorithm before experimental testing. By applying these measures to an existing CLDA algorithm, we demonstrate that our convergence analysis is an effective analytical tool that can ultimately inform and improve the design of CLDA algorithms.

Understanding the principles governing the dynamic coordination of functional brain networks remains an important unmet goal within neuroscience. How do distributed ensembles of neurons transiently coordinate their activity across a variety of spatial and temporal scales? While a complete mechanistic account of this process remains elusive, evidence suggests that neuronal oscillations may play a key role in this process, with different rhythms influencing both local computation and long-range communication. To investigate this question, we recorded multiple single unit and local field potential (LFP) activity from microelectrode arrays implanted bilaterally in macaque motor areas. Monkeys performed a delayed center-out reach task either manually using their natural arm (Manual Control, MC) or under direct neural control through a brain-machine interface (Brain Control, BC). In accord with prior work, we found that the spiking activity of individual neurons is coupled to multiple aspects of the ongoing motor beta rhythm (10-45 Hz) during both MC and BC, with neurons exhibiting a diversity of coupling preferences. However, here we show that for identified single neurons, this beta-to-rate mapping can change in a reversible and task-dependent way. For example, as beta power increases, a given neuron may increase spiking during MC but decrease spiking during BC, or exhibit a reversible shift in the preferred phase of firing. The within-task stability of coupling, combined with the reversible cross-task changes in coupling, suggest that task-dependent changes in the beta-to-rate mapping play a role in the transient functional reorganization of neural ensembles. We characterize the range of task-dependent changes in the mapping from beta amplitude, phase, and inter-hemispheric phase differences to the spike rates of an ensemble of simultaneously-recorded neurons, and discuss the potential implications that dynamic remapping from oscillatory activity to spike rate and timing may hold for models of computation and communication in distributed functional brain networks.

Closed-loop operation of a brain-machine interface (BMI) relies on the subject's ability to learn an inverse transformation of the plant to be controlled. In this paper, we propose a model of the learning process that undergoes closed-loop BMI operation. We first explore the properties of the model and show that it is able to learn an inverse model of the controlled plant. Then, we compare the model predictions to actual experimental neural and behavioral data from nonhuman primates operating a BMI, which demonstrate high accordance of the model with the experimental data. Applying tools from control theory to this learning model will help in the design of a new generation of neural information decoders which will maximize learning speed for BMI users.

Brain-machine interfaces (BMIs) provide a versatile tool for rehabilitation of severely disabled people. Current BMI systems focus on the control of kinematic variables. However, this approach limits the application space of BMI technology to simulated environments. Real-world rehabilitation robots, on the other hand, must operate in a variety of complex physical situations. BMI systems that are aimed toward prostheses must, then, control interaction forces with their environments. In this paper, we design a BMI-driven architecture that provides a critical link between neuronal ensemble activity and real-world dynamics. In particular, our system allows simultaneous estimation of kinematic and stiffness variables of a prosthetic device. This approach is achieved by recording instantaneous activities from cortical neural ensembles that input to a musculoskeletal model of the arm, from which limb kinematics and dynamics are estimated and converted into control signals of a prosthetic device. Using real neural and behavioral data from nonhuman primates, we show that our architecture can accurately predict kinematic and stiffness variables in different dynamic situations. This architecture has strong implications in the development of the next generation of neural prosthetics that will restore motor function in neurologically impaired patients. Moreover, it demonstrates a novel framework for studying how the brain learns and adapts to new environments.

This paper presents a model of the learning process occurring during operation of a closed-loop brain-machine interface (BMI). The learning model updates neuron firing properties based on a feedback-error learning scheme, featuring feedforward and feedback controllers. Our goal is to replicate in simulation experimental results showing functional reorganization of neuronal ensembles during BMI experiments. We show that the proposed model can simulate motor learning, and that the predicted changes in neuronal tuning are consistent with experimental observations. We believe that being able to simulate motor learning in a BMI context will allow designing decoders that would facilitate the learning process in real world experiments.

Brain machine interfaces (BMI) utilize linear or non-linear models to map the neural activity to the associated behavior which is typically the 2D or 3D hand position of a primate. Linear models are plagued by the massive disparity of the input and output dimensions thereby leading to poor generalization. A solution would be to use non-linear models like the recurrent multi-layer perceptron (RMLP) that provide parsimonious mapping functions with better generalization. However, this results in a drastic increase in the training complexity, which can be critical for practical use of a BMI. This paper bridges the gap between superior performance per trained weight and model learning complexity. Towards this end, we propose to use echo state networks (ESN) to transform the neuronal firing activity into a higher dimensional space and then derive an optimal sparse linear mapping in the transformed space to match the hand position. The sparse mapping is obtained using a weight constrained cost function whose optimal solution is determined using a stochastic gradient algorithm.

This work presents a 3D tracking method that exploits both the physics of narrowband echolocation and the dynamics of the sensor, taking inspiration from narrowband (CF-FM) bats. The platform used in this work is RoBat, a biomimetic sonarhead mounted on a mobile platform. Two different techniques are integrated in the tracker. First, the Barshan and Kuc method (1992) for estimating target range and azimuth angle is adapted to the geometry of the sonarhead. Next, Walker's arc scanning hypothesis for target elevation estimation in CF-FM bats is implemented, applying curve fitting for an accurate estimation of the peak-delay. The integration of both techniques in a 3D tracking system is performed, applying both methods independently. A more sophisticated method which requires the geometric model to be revisited and a better time-of-flight estimation method, is also proposed.

This work presents an evolutionary approach to pinna design. Narrowband echolocating bats move the pinna to alter the directional sensitivity of their perceptual systems. Adding pinnae to RoBat--a biomimetic sonarhead mounted on a mobile robot--is the goal of this work. After a description of the earlier work on artificial pinnae consisting of multiple reflectors around the transducer, an acoustic model, inspired by a physical model of sound diffraction and reflections in the human concha, is described and revisited as the model to use for evolving complex shapes. A genetic algorithm evolved the shape of the pinnae with respect to desired features of the directivity pattern of the receiver transducers. Some interesting paraboloid shapes for specific echolocating behaviors were evolved, improving performance with respect to the bare transducer's performance.

In this work, RoBat, a biomimetic sonarhead mounted on a mobile robot is presented as a platform in which to study perception in bats. After addressing the relation between bats and robots, a biologically plausible model of the bat's cochlea is described. The model consists of a filterbank with an artificial neural network (ANN) connected at the end of it. The ANN is used for extracting the frequency of the received echoes. Thus, Doppler shifts, which play a fundamental role in bat navigation, can be successfully estimated. Experiments with simulated and real data show the efficiency of the system when used on a LEGO mobile robot.