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ABSTRACT: 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.
Neural Computation 04/2013; · 1.88 Impact Factor
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ABSTRACT: The rodent somatosensory barrel cortex (S1bf) has proved a valuable model for studying neural plasticity in vivo. It has been observed that sensory deprivation or conditioning reorganizes sensory-driven activity within S1bf. These observations suggest a role for S1bf in somatosensory learning. This study evaluated the hypothesis that the response properties of extracellularly recorded neurons in S1bf would change as subjects learned to respond to stimulation of S1bf. Intra-cortical microstimulation (ICMS) of S1bf was used as a means for bypassing feedforward drive from the sensory periphery, midbrain and thalamus, while exciting local cortical networks. To separate the learning of this conditioned stimulus-conditioned response (CS-CR) from other elements of the task, we employed a cross-modal transfer schedule. Long Evans rats were initially trained to respond to an auditory stimulus. All subjects were then implanted in both S1bfs with chronic microwire arrays for recording neural activity and delivering ICMS. Next, this association was transferred to ICMS of one hemisphere's S1bf. S1bf responded to ICMS with a brief increase in firing rate followed by a longer reduction in activity. We observed the duration of reduced activity elicited by ICMS increased as the subjects began to respond correctly more often than expected by chance, and the magnitude of the initial positive response increased as they consolidated this CS-CR. Subsequent ICMS of the opposite S1bf revealed this CS-CR did not generalize across hemispheres. These results suggest a mechanism involving a single hemisphere's S1bf tunes cortical responses in concert with changes in rodent behavior during somatosensory learning.
Journal of Neurophysiology 03/2013; · 3.32 Impact Factor
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ABSTRACT: 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.
PLoS Computational Biology 12/2012; 8(12):e1002809. · 5.22 Impact Factor
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ABSTRACT: Brain-machine interfaces (BMIs) must be carefully designed for closed-loop control to ensure the best possible performance. The Kalman filter (KF) is a recursive linear BMI algorithm which has been shown to smooth cursor kinematics and improve control over non-recursive linear methods. However, recursive estimators are not without their drawbacks. Here we show that recursive decoders can decrease BMI controllability by coupling kinematic variables that the subject might expect to be unrelated. For instance, a 2D neural cursor where velocity is controlled using a KF can increase the difficulty of straight reaches by linking horizontal and vertical velocity estimates. These effects resemble force fields in arm control. Analysis of experimental data from one non-human primate controlling a position/velocity KF cursor in closed-loop shows that the presence of these force-field effects correlated with decreased performance. We designed a modified KF parameter estimation algorithm to eliminate these effects. Cursor controllability improved significantly when our modifications were used in a closed-loop BMI simulator. Thus, designing highly controllable BMIs requires parameter estimation techniques that carefully craft relationships between decoded variables.
Conference proceedings: ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Conference 08/2012; 2012:1314-7.
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ABSTRACT: Closed-loop decoder adaptation (CLDA) shows great promise to improve closed-loop brain-machine interface (BMI) performance. Developing adaptation algorithms capable of rapidly improving performance, independent of initial performance, may be crucial for clinical applications where patients have limited movement and sensory abilities due to motor deficits. Given the subject-decoder interactions inherent in closed-loop BMIs, the decoder adaptation time-scale may be of particular importance when initial performance is limited. Here, we present SmoothBatch, a CLDA algorithm which updates decoder parameters on a 1-2 min time-scale using an exponentially weighted sliding average. The algorithm was experimentally tested with one nonhuman primate performing a center-out reaching BMI task. SmoothBatch was seeded four ways with varying offline decoding power: 1) visual observation of a cursor ( n = 20), 2) ipsilateral arm movements ( n = 8), 3) baseline neural activity ( n = 17), and 4) arbitrary weights ( n = 11). SmoothBatch rapidly improved performance regardless of seeding, with performance improvements from 0.018 ±0.133 successes/min to > 8 successes/min within 13.1 ±5.5 min ( n = 56). After decoder adaptation ceased, the subject maintained high performance. Moreover, performance improvements were paralleled by SmoothBatch convergence, suggesting that CLDA involves a co-adaptation process between the subject and the decoder.
IEEE transactions on neural systems and rehabilitation engineering: a publication of the IEEE Engineering in Medicine and Biology Society 07/2012; 20(4):468-77. · 2.42 Impact Factor
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ABSTRACT: The ability to learn new skills and perfect them with practice applies not only to physical skills but also to abstract skills, like motor planning or neuroprosthetic actions. Although plasticity in corticostriatal circuits has been implicated in learning physical skills, it remains unclear if similar circuits or processes are required for abstract skill learning. Here we use a novel behavioural task in rodents to investigate the role of corticostriatal plasticity in abstract skill learning. Rodents learned to control the pitch of an auditory cursor to reach one of two targets by modulating activity in primary motor cortex irrespective of physical movement. Degradation of the relation between action and outcome, as well as sensory-specific devaluation and omission tests, demonstrate that these learned neuroprosthetic actions are intentional and goal-directed, rather than habitual. Striatal neurons change their activity with learning, with more neurons modulating their activity in relation to target-reaching as learning progresses. Concomitantly, strong relations between the activity of neurons in motor cortex and the striatum emerge. Specific deletion of striatal NMDA receptors impairs the development of this corticostriatal plasticity, and disrupts the ability to learn neuroprosthetic skills. These results suggest that corticostriatal plasticity is necessary for abstract skill learning, and that neuroprosthetic movements capitalize on the neural circuitry involved in natural motor learning.
Nature 03/2012; 483(7389):331-5. · 36.28 Impact Factor
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ABSTRACT: Neurons in the brain form highly complex networks through synaptic connections. Traditionally, functional connectivity between neurons has been explored using methods such as correlations, which do not contain any notion of directionality. Recently, an information-theoretic approach based on directed information theory has been proposed as a way to infer the direction of influence. However, it is still unclear whether this new approach provides any additional insight beyond conventional correlation analyses. In this paper, we present a modified procedure for estimating directed information and provide a comparison of results obtained using correlation analyses on both simulated and experimental data. Using physiologically realistic simulations, we demonstrate that directed information can outperform correlation in determining connections between neural spike trains while also providing directionality of the relationship, which cannot be assessed using correlation. Secondly, applying our method to rodent and primate data sets, we demonstrate that directed information can accurately estimate the conduction delay in connections between different brain structures. Moreover, directed information reveals connectivity structures that are not captured by correlations. Hence, directed information provides accurate and novel insights into the functional connectivity of neural ensembles that are applicable to data from neurophysiological studies in awake behaving animals.
Journal of Neural Engineering 02/2012; 9(2):026004. · 3.84 Impact Factor
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ABSTRACT: Oscillatory phase coupling within large-scale brain networks is a topic of increasing interest within systems, cognitive, and theoretical neuroscience. Evidence shows that brain rhythms play a role in controlling neuronal excitability and response modulation (Haider B, McCormick D. Neuron 62: 171-189, 2009) and regulate the efficacy of communication between cortical regions (Fries P. Trends Cogn Sci 9: 474-480, 2005) and distinct spatiotemporal scales (Canolty RT, Knight RT. Trends Cogn Sci 14: 506-515, 2010). In this view, anatomically connected brain areas form the scaffolding upon which neuronal oscillations rapidly create and dissolve transient functional networks (Lakatos P, Karmos G, Mehta A, Ulbert I, Schroeder C. Science 320: 110-113, 2008). Importantly, testing these hypotheses requires methods designed to accurately reflect dynamic changes in multivariate phase coupling within brain networks. Unfortunately, phase coupling between neurophysiological signals is commonly investigated using suboptimal techniques. Here we describe how a recently developed probabilistic model, phase coupling estimation (PCE; Cadieu C, Koepsell K Neural Comput 44: 3107-3126, 2010), can be used to investigate changes in multivariate phase coupling, and we detail the advantages of this model over the commonly employed phase-locking value (PLV; Lachaux JP, Rodriguez E, Martinerie J, Varela F. Human Brain Map 8: 194-208, 1999). We show that the N-dimensional PCE is a natural generalization of the inherently bivariate PLV. Using simulations, we show that PCE accurately captures both direct and indirect (network mediated) coupling between network elements in situations where PLV produces erroneous results. We present empirical results on recordings from humans and nonhuman primates and show that the PCE-estimated coupling values are different from those using the bivariate PLV. Critically on these empirical recordings, PCE output tends to be sparser than the PLVs, indicating fewer significant interactions and perhaps a more parsimonious description of the data. Finally, the physical interpretation of PCE parameters is straightforward: the PCE parameters correspond to interaction terms in a network of coupled oscillators. Forward modeling of a network of coupled oscillators with parameters estimated by PCE generates synthetic data with statistical characteristics identical to empirical signals. Given these advantages over the PLV, PCE is a useful tool for investigating multivariate phase coupling in distributed brain networks.
Journal of Neurophysiology 01/2012; 107(7):2020-31. · 3.32 Impact Factor
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IEEE Trans. Biomed. Engineering. 01/2012; 59:8-11.
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ABSTRACT: Phase-amplitude cross-frequency coupling (CFC)-where the phase of a low-frequency signal modulates the amplitude or power of a high-frequency signal-is a topic of increasing interest in neuroscience. However, existing methods of assessing CFC are inherently bivariate and cannot estimate CFC between more than two signals at a time. Given the increase in multielectrode recordings, this is a strong limitation. Furthermore, the phase coupling between multiple low-frequency signals is likely to produce a high rate of false positives when CFC is evaluated using bivariate methods. Here, we present a novel method for estimating the statistical dependence between one high-frequency signal and N low-frequency signals, termed multivariate phase-coupling estimation (PCE). Compared to bivariate methods, the PCE produces sparser estimates of CFC and can distinguish between direct and indirect coupling between neurophysiological signals-critical for accurately estimating coupling within multiscale brain networks.
IEEE transactions on bio-medical engineering 01/2012; 59(1):8-11. · 2.15 Impact Factor
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ABSTRACT: Redundant encoding of information facilitates reliable distributed information processing. To explore this hypothesis in the motor system, we applied concepts from information theory to quantify the redundancy of movement-related information encoded in the macaque primary motor cortex (M1) during natural and neuroprosthetic control. Two macaque monkeys were trained to perform a delay center-out reaching task controlling a computer cursor under natural arm movement (manual control, 'MC'), and using a brain-machine interface (BMI) via volitional control of neural ensemble activity (brain control, 'BC'). During MC, we found neurons in contralateral M1 to contain higher and more redundant information about target direction than ipsilateral M1 neurons, consistent with the laterality of movement control. During BC, we found that the M1 neurons directly incorporated into the BMI ('direct' neurons) contained the highest and most redundant target information compared to neurons that were not incorporated into the BMI ('indirect' neurons). This effect was even more significant when comparing to M1 neurons of the opposite hemisphere. Interestingly, when we retrained the BMI to use ipsilateral M1 activity, we found that these neurons were more redundant and contained higher information than contralateral M1 neurons, even though ensembles from this hemisphere were previously less redundant during natural arm movement. These results indicate that ensembles most associated to movement contain highest redundancy and information encoding, which suggests a role for redundancy in proficient natural and prosthetic motor control.
Journal of Computational Neuroscience 11/2011; 32(3):555-61. · 2.51 Impact Factor
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ABSTRACT: Performing closed-loop modifications of a brain-machine interface (BMI) decoder is a technique that shows great promise for improving performance. We compare two algorithms for implementing adaptations that update decoder parameters on different time-scales (discrete batches vs. online), and present experimental results of a non-human primate performing a standard center-out BMI task. To ensure that our experimental training models are representative of a broad range of paralyzed patients, our decoders were initially trained using neural activity recorded during subject observation of cursor movement. We find that both closed-loop adaptation algorithms can be used to boost BMI performance from 20-30% to 80%, yielding movement kinematics similar to natural arm movements. Based on insights derived from the performance of each algorithm, we propose that a hybrid of batch and online decoder adaptation may be the best approach.
Conference proceedings: ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Conference 08/2011; 2011:5436-9.
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ABSTRACT: Neurons in the brain form complicated networks through synaptic connections. Traditionally, functional connectivity between neurons has been analyzed using simple metrics such as correlation, which do not provide direction of influence. Recently, an information theoretic measure known as directed information has been proposed as a way to capture directionality in the relationship, thereby moving towards a model of effective connectivity. This measure is grounded upon the concept of Granger causality and can be estimated by modeling neural spike trains as point process generalized linear models. However, the added benefit of using directed information to infer connectivity over conventional methods such as correlation is still unclear. Here, we propose a novel estimation procedure for the directed information. Using physiologically realistic simulations, we demonstrate that directed information can outperform correlation in determining connections between neural spike trains while also providing directionality of the relationship, which cannot be assessed using correlation.
Conference proceedings: ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Conference 08/2011; 2011:7324-7.
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ABSTRACT: Brain-machine interfaces (BMIs) provide a framework for studying cortical dynamics and the neural correlates of learning. Neuroprosthetic control has been associated with tuning changes in specific neurons directly projecting to the BMI (hereafter referred to as direct neurons). However, little is known about the larger network dynamics. By monitoring ensembles of neurons that were either causally linked to BMI control or indirectly involved, we found that proficient neuroprosthetic control is associated with large-scale modifications to the cortical network in macaque monkeys. Specifically, there were changes in the preferred direction of both direct and indirect neurons. Notably, with learning, there was a relative decrease in the net modulation of indirect neural activity in comparison with direct activity. These widespread differential changes in the direct and indirect population activity were markedly stable from one day to the next and readily coexisted with the long-standing cortical network for upper limb control. Thus, the process of learning BMI control is associated with differential modification of neural populations based on their specific relation to movement control.
Nature Neuroscience 05/2011; 14(5):662-7. · 15.53 Impact Factor
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ABSTRACT: The growing use of multi-channel neural recording techniques in behaving animals has produced rich datasets that hold immense potential for advancing our understanding of how the brain mediates behavior. One limitation of these techniques is they do not provide important information about the underlying anatomical connections among the recorded neurons within an ensemble. Inferring these connections is often intractable because the set of possible interactions grows exponentially with ensemble size. This is a fundamental challenge one confronts when interpreting these data. Unfortunately, the combination of expert knowledge and ensemble data is often insufficient for selecting a unique model of these interactions. Our approach shifts away from modeling the network diagram of the ensemble toward analyzing changes in the dynamics of the ensemble as they relate to behavior. Our contribution consists of adapting techniques from signal processing and Bayesian statistics to track the dynamics of ensemble data on time-scales comparable with behavior. We employ a Bayesian estimator to weigh prior information against the available ensemble data, and use an adaptive quantization technique to aggregate poorly estimated regions of the ensemble data space. Importantly, our method is capable of detecting changes in both the magnitude and structure of correlations among neurons missed by firing rate metrics. We show that this method is scalable across a wide range of time-scales and ensemble sizes. Lastly, the performance of this method on both simulated and real ensemble data is used to demonstrate its utility.
Frontiers in Computational Neuroscience 01/2011; 5:52. · 2.15 Impact Factor
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ABSTRACT: Research into the development of brain-machine interfaces (BMIs) has led to demonstrations of rodents, nonhuman primates, and humans controlling prosthetic devices in real time through modulation of neural signals. In particular, cortical BMI studies have shown that improvements in performance require learning and are associated with changes in neuronal tuning properties. These studies have further shown evidence of long-term improvements in performance with practice. The authors conducted experiments to understand long-term skill acquisition with BMIs and to characterize the neural correlates of improvements in task performance. They specifically assessed long-term acquisition of neuroprosthetic skill (i.e., accurate task performance readily recalled across days). In 2 monkeys performing a center-out task using a brain-controlled (BC) computer cursor, they closely monitored daily performance trends and the neural correlates under different conditions. Importantly, they assessed BC performance using a continuous-control multistep task. The authors first conducted experiments that mimicked experimental conditions commonly used. Specifically, a large set of neurons was incorporated with daily recalibration of the transform of neural activity to BC. Under such conditions, they found evidence of variable daily performance. In contrast, when a fixed transform was applied to stable recordings from an ensemble of neurons across days, there was consistent evidence of long-term skill acquisition. Such skill acquisition was associated with the crystallization of a cortical map for prosthetic control. Taken together, the results suggest that the primate motor cortex can achieve skilled control of a neuroprosthetic device through consolidation of a cortical representation.
Journal of Motor Behavior 11/2010; 42(6):355-60. · 1.64 Impact Factor
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ABSTRACT: Hebb proposed that neuronal cell assemblies are critical for effective perception, cognition, and action. However, evidence for brain mechanisms that coordinate multiple coactive assemblies remains lacking. Neuronal oscillations have been suggested as one possible mechanism for cell assembly coordination. Prior studies have shown that spike timing depends upon local field potential (LFP) phase proximal to the cell body, but few studies have examined the dependence of spiking on distal LFP phases in other brain areas far from the neuron or the influence of LFP-LFP phase coupling between distal areas on spiking. We investigated these interactions by recording LFPs and single-unit activity using multiple microelectrode arrays in several brain areas and then used a unique probabilistic multivariate phase distribution to model the dependence of spike timing on the full pattern of proximal LFP phases, distal LFP phases, and LFP-LFP phase coupling between electrodes. Here we show that spiking activity in single neurons and neuronal ensembles depends on dynamic patterns of oscillatory phase coupling between multiple brain areas, in addition to the effects of proximal LFP phase. Neurons that prefer similar patterns of phase coupling exhibit similar changes in spike rates, whereas neurons with different preferences show divergent responses, providing a basic mechanism to bind different neurons together into coordinated cell assemblies. Surprisingly, phase-coupling-based rate correlations are independent of interneuron distance. Phase-coupling preferences correlate with behavior and neural function and remain stable over multiple days. These findings suggest that neuronal oscillations enable selective and dynamic control of distributed functional cell assemblies.
Proceedings of the National Academy of Sciences 10/2010; 107(40):17356-61. · 9.68 Impact Factor
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ABSTRACT: Simultaneous behavior and multielectrode neural recordings in freely behaving rodents holds great promise to study the neural bases of behavior and disease models in combination with genetic manipulations. Here, we introduce the use of three-axis accelerometers to characterize the behavior of rats and mice during chronic neural recordings. These sensors were small and light enough to be worn by rodents and were used to record three-axis acceleration during freely moving behavior. A two-layer neural network-based pattern recognition algorithm was developed to extract the natural behavior of mice from the acceleration data. Successful recognition of resting, eating, grooming, and rearing are shown using this approach. The inertial sensors were combined with continuous 24-h recordings of neural data from the striatum of mice to characterize variations in neural activity with circadian cycles and to study the neural correlates of spontaneous action initiation. Finally, accelerometers were used to study the performance of rodents in traditional operant conditioning, where they were used to extract the reaction time of rodents. Thus the addition of accelerometer recordings of rodents to chronic multielectrode neural recordings provides great value for a number of neuroscience applications.
Journal of Neurophysiology 07/2010; 104(1):569-75. · 3.32 Impact Factor
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IEEE Transactions on Systems, Man, and Cybernetics, Part A. 01/2010; 40:732-742.
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ABSTRACT: Corticostriatal dynamics exhibit gross alterations over the course of natural motor learning, yet little is known about the role they play in neuroprosthetic tasks. We therefore investigated interactions between the striatum and primary motor cortex while rats learned to control a brain-machine interface. Striatal firing rates increased greatly from early to late in learning, suggesting that the striatum underlies similar functions in both natural and neuroprosthetic motor learning. In addition, spike-field coherence between neurons in primary motor cortex and local field potentials in the striatum increased greatly in the alpha band in late learning relative to early learning, suggesting the development of functional interactions in corticostriatal networks over the course of learning.
Conference proceedings: ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Conference 01/2010; 2010:2682-5.
