Cortical preparatory activity indexes learned motor memories.

The brain's remarkable ability to learn and execute various motor behaviours harnesses the capacity of neural populations to generate a variety of activity patterns. Here we explore systematic changes in preparatory activity in motor cortex that accompany motor learning. We trained rhesus monkeys to learn an arm-reaching task1 in a curl force field that elicited new muscle forces for some, but not all, movement directions2,3. We found that in a neural subspace predictive of hand forces, changes in preparatory activity tracked the learned behavioural modifications and reassociated4 existing activity patterns with updated movements. Along a neural population dimension orthogonal to the force-predictive subspace, we discovered that preparatory activity shifted uniformly for all movement directions, including those unaltered by learning. During a washout period when the curl field was removed, preparatory activity gradually reverted in the force-predictive subspace, but the uniform shift persisted. These persistent preparatory activity patterns may retain a motor memory of the learned field5,6 and support accelerated relearning of the same curl field. When a set of distinct curl fields was learned in sequence, we observed a corresponding set of field-specific uniform shifts which separated the associated motor memories in the neural state space7-9. The precise geometry of these uniform shifts in preparatory activity could serve to index motor memories, facilitating the acquisition, retention and retrieval of a broad motor repertoire.

Decoding and perturbing decision states in real time.

In dynamic environments, subjects often integrate multiple samples of a signal and combine them to reach a categorical judgment1. The process of deliberation can be described by a time-varying decision variable (DV), decoded from neural population activity, that predicts a subject's upcoming decision2. Within single trials, however, there are large moment-to-moment fluctuations in the DV, the behavioural significance of which is unclear. Here, using real-time, neural feedback control of stimulus duration, we show that within-trial DV fluctuations, decoded from motor cortex, are tightly linked to decision state in macaques, predicting behavioural choices substantially better than the condition-averaged DV or the visual stimulus alone. Furthermore, robust changes in DV sign have the statistical regularities expected from behavioural studies of changes of mind3. Probing the decision process on single trials with weak stimulus pulses, we find evidence for time-varying absorbing decision bounds, enabling us to distinguish between specific models of decision making.

Inferring single-trial neural population dynamics using sequential auto-encoders.

Neuroscience is experiencing a revolution in which simultaneous recording of thousands of neurons is revealing population dynamics that are not apparent from single-neuron responses. This structure is typically extracted from data averaged across many trials, but deeper understanding requires studying phenomena detected in single trials, which is challenging due to incomplete sampling of the neural population, trial-to-trial variability, and fluctuations in action potential timing. We introduce latent factor analysis via dynamical systems, a deep learning method to infer latent dynamics from single-trial neural spiking data. When applied to a variety of macaque and human motor cortical datasets, latent factor analysis via dynamical systems accurately predicts observed behavioral variables, extracts precise firing rate estimates of neural dynamics on single trials, infers perturbations to those dynamics that correlate with behavioral choices, and combines data from non-overlapping recording sessions spanning months to improve inference of underlying dynamics.

Structure and variability of delay activity in premotor cortex.

Voluntary movements are widely considered to be planned before they are executed. Recent studies have hypothesized that neural activity in motor cortex during preparation acts as an 'initial condition' which seeds the proceeding neural dynamics. Here, we studied these initial conditions in detail by investigating 1) the organization of neural states for different reaches and 2) the variance of these neural states from trial to trial. We examined population-level responses in macaque premotor cortex (PMd) during the preparatory stage of an instructed-delay center-out reaching task with dense target configurations. We found that after target onset the neural activity on single trials converges to neural states that have a clear low-dimensional structure which is organized by both the reach endpoint and maximum speed of the following reach. Further, we found that variability of the neural states during preparation resembles the spatial variability of reaches made in the absence of visual feedback: there is less variability in direction than distance in neural state space. We also used offline decoding to understand the implications of this neural population structure for brain-machine interfaces (BMIs). We found that decoding of angle between reaches is dependent on reach distance, while decoding of arc-length is independent. Thus, it might be more appropriate to quantify decoding performance for discrete BMIs by using arc-length between reach end-points rather than the angle between them. Lastly, we show that in contrast to the common notion that direction can better be decoded than distance, their decoding capabilities are comparable. These results provide new insights into the dynamical neural processes that underline motor control and can inform the design of BMIs.

Neural constraints on learning.

Learning, whether motor, sensory or cognitive, requires networks of neurons to generate new activity patterns. As some behaviours are easier to learn than others, we asked if some neural activity patterns are easier to generate than others. Here we investigate whether an existing network constrains the patterns that a subset of its neurons is capable of exhibiting, and if so, what principles define this constraint. We employed a closed-loop intracortical brain-computer interface learning paradigm in which Rhesus macaques (Macaca mulatta) controlled a computer cursor by modulating neural activity patterns in the primary motor cortex. Using the brain-computer interface paradigm, we could specify and alter how neural activity mapped to cursor velocity. At the start of each session, we observed the characteristic activity patterns of the recorded neural population. The activity of a neural population can be represented in a high-dimensional space (termed the neural space), wherein each dimension corresponds to the activity of one neuron. These characteristic activity patterns comprise a low-dimensional subspace (termed the intrinsic manifold) within the neural space. The intrinsic manifold presumably reflects constraints imposed by the underlying neural circuitry. Here we show that the animals could readily learn to proficiently control the cursor using neural activity patterns that were within the intrinsic manifold. However, animals were less able to learn to proficiently control the cursor using activity patterns that were outside of the intrinsic manifold. These results suggest that the existing structure of a network can shape learning. On a timescale of hours, it seems to be difficult to learn to generate neural activity patterns that are not consistent with the existing network structure. These findings offer a network-level explanation for the observation that we are more readily able to learn new skills when they are related to the skills that we already possess.

Lipid Composition of Latex and Rubber Particles in Hevea brasiliensis and Taraxacum kok-saghyz.

Natural rubber is usually synthesized in the rubber particles present in the latex of rubber-producing plants such as the Pará rubber tree (Hevea brasiliensis) and rubber dandelion (Taraxacum kok-saghyz). Since the detailed lipid compositions of fresh latex and rubber particles of the plants are poorly known, the present study reports detailed compound lipid composition, focusing on phospholipids and galactolipids in the latex and rubber particles of the plants. In the fresh latex and rubber particles of both plants, phospholipids were much more dominant (85-99%) compared to galactolipids. Among the nine classes of phospholipids, phosphatidylcholines (PCs) were most abundant, at ~80%, in both plants. Among PCs, PC (36:4) and PC (34:2) were most abundant in the rubber tree and rubber dandelion, respectively. Two classes of galactolipids, monogalactosyl diacylglycerol and digalactosyl diacylglycerol, were detected as 12% and 1%, respectively, of total compound lipids in rubber tree, whereas their percentages in the rubber dandelion were negligible (< 1%). Overall, the compound lipid composition differed only slightly between the fresh latex and the rubber particles of both rubber plants. These results provide fundamental data on the lipid composition of rubber particles in two rubber-producing plants, which can serve as a basis for artificial rubber particle production in the future.

Natural rubber biosynthesis in plants, the rubber transferase complex, and metabolic engineering progress and prospects.

Natural rubber (NR) is a nonfungible and valuable biopolymer, used to manufacture ~50 000 rubber products, including tires and medical gloves. Current production of NR is derived entirely from the para rubber tree (Hevea brasiliensis). The increasing demand for NR, coupled with limitations and vulnerability of H. brasiliensis production systems, has induced increasing interest among scientists and companies in potential alternative NR crops. Genetic/metabolic pathway engineering approaches, to generate NR-enriched genotypes of alternative NR plants, are of great importance. However, although our knowledge of rubber biochemistry has significantly advanced, our current understanding of NR biosynthesis, the biosynthetic machinery and the molecular mechanisms involved remains incomplete. Two spatially separated metabolic pathways provide precursors for NR biosynthesis in plants and their genes and enzymes/complexes are quite well understood. In contrast, understanding of the proteins and genes involved in the final step(s)-the synthesis of the high molecular weight rubber polymer itself-is only now beginning to emerge. In this review, we provide a critical evaluation of recent research developments in NR biosynthesis, in vitro reconstitution, and the genetic and metabolic pathway engineering advances intended to improve NR content in plants, including H. brasiliensis, two other prospective alternative rubber crops, namely the rubber dandelion and guayule, and model species, such as lettuce. We describe a new model of the rubber transferase complex, which integrates these developments. In addition, we highlight the current challenges in NR biosynthesis research and future perspectives on metabolic pathway engineering of NR to speed alternative rubber crop commercial development.

Trial-by-Trial Motor Cortical Correlates of a Rapidly Adapting Visuomotor Internal Model.

Accurate motor control is mediated by internal models of how neural activity generates movement. We examined neural correlates of an adapting internal model of visuomotor gain in motor cortex while two macaques performed a reaching task in which the gain scaling between the hand and a presented cursor was varied. Previous studies of cortical changes during visuomotor adaptation focused on preparatory and perimovement epochs and analyzed trial-averaged neural data. Here, we recorded simultaneous neural population activity using multielectrode arrays and focused our analysis on neural differences in the period before the target appeared. We found that we could estimate the monkey's internal model of the gain using the neural population state during this pretarget epoch. This neural correlate depended on the gain experienced during recent trials and it predicted the speed of the subsequent reach. To explore the utility of this internal model estimate for brain-machine interfaces, we performed an offline analysis showing that it can be used to compensate for upcoming reach extent errors. Together, these results demonstrate that pretarget neural activity in motor cortex reflects the monkey's internal model of visuomotor gain on single trials and can potentially be used to improve neural prostheses.SIGNIFICANCE STATEMENT When generating movement commands, the brain is believed to use internal models of the relationship between neural activity and the body's movement. Visuomotor adaptation tasks have revealed neural correlates of these computations in multiple brain areas during movement preparation and execution. Here, we describe motor cortical changes in a visuomotor gain change task even before a specific movement is cued. We were able to estimate the gain internal model from these pretarget neural correlates and relate it to single-trial behavior. This is an important step toward understanding the sensorimotor system's algorithms for updating its internal models after specific movements and errors. Furthermore, the ability to estimate the internal model before movement could improve motor neural prostheses being developed for people with paralysis.

SCYL2 Genes Are Involved in Clathrin-Mediated Vesicle Trafficking and Essential for Plant Growth.

Protein transport between organelles is an essential process in all eukaryotic cells and is mediated by the regulation of processes such as vesicle formation, transport, docking, and fusion. In animals, SCY1-LIKE2 (SCYL2) binds to clathrin and has been shown to play roles in trans-Golgi network-mediated clathrin-coated vesicle trafficking. Here, we demonstrate that SCYL2A and SCYL2B, which are Arabidopsis (Arabidopsis thaliana) homologs of animal SCYL2, are vital for plant cell growth and root hair development. Studies of the SCYL2 isoforms using multiple single or double loss-of-function alleles show that SCYL2B is involved in root hair development and that SCYL2A and SCYL2B are essential for plant growth and development and act redundantly in those processes. Quantitative reverse transcription-polymerase chain reaction and a ß-glucuronidase-aided promoter assay show that SCYL2A and SCYL2B are differentially expressed in various tissues. We also show that SCYL2 proteins localize to the Golgi, trans-Golgi network, and prevacuolar compartment and colocalize with Clathrin Heavy Chain1 (CHC1). Furthermore, bimolecular fluorescence complementation and coimmunoprecipitation data show that SCYL2B interacts with CHC1 and two Soluble NSF Attachment Protein Receptors (SNAREs): Vesicle Transport through t-SNARE Interaction11 (VTI11) and VTI12. Finally, we present evidence that the root hair tip localization of Cellulose Synthase-Like D3 is dependent on SCYL2B. These findings suggest the role of SCYL2 genes in plant cell developmental processes via clathrin-mediated vesicle membrane trafficking.

Neural population dynamics during reaching.

Most theories of motor cortex have assumed that neural activity represents movement parameters. This view derives from what is known about primary visual cortex, where neural activity represents patterns of light. Yet it is unclear how well the analogy between motor and visual cortex holds. Single-neuron responses in motor cortex are complex, and there is marked disagreement regarding which movement parameters are represented. A better analogy might be with other motor systems, where a common principle is rhythmic neural activity. Here we find that motor cortex responses during reaching contain a brief but strong oscillatory component, something quite unexpected for a non-periodic behaviour. Oscillation amplitude and phase followed naturally from the preparatory state, suggesting a mechanistic role for preparatory neural activity. These results demonstrate an unexpected yet surprisingly simple structure in the population response. This underlying structure explains many of the confusing features of individual neural responses.

Tensor Analysis Reveals Distinct Population Structure that Parallels the Different Computational Roles of Areas M1 and V1.

Cortical firing rates frequently display elaborate and heterogeneous temporal structure. One often wishes to compute quantitative summaries of such structure-a basic example is the frequency spectrum-and compare with model-based predictions. The advent of large-scale population recordings affords the opportunity to do so in new ways, with the hope of distinguishing between potential explanations for why responses vary with time. We introduce a method that assesses a basic but previously unexplored form of population-level structure: when data contain responses across multiple neurons, conditions, and times, they are naturally expressed as a third-order tensor. We examined tensor structure for multiple datasets from primary visual cortex (V1) and primary motor cortex (M1). All V1 datasets were 'simplest' (there were relatively few degrees of freedom) along the neuron mode, while all M1 datasets were simplest along the condition mode. These differences could not be inferred from surface-level response features. Formal considerations suggest why tensor structure might differ across modes. For idealized linear models, structure is simplest across the neuron mode when responses reflect external variables, and simplest across the condition mode when responses reflect population dynamics. This same pattern was present for existing models that seek to explain motor cortex responses. Critically, only dynamical models displayed tensor structure that agreed with the empirical M1 data. These results illustrate that tensor structure is a basic feature of the data. For M1 the tensor structure was compatible with only a subset of existing models.

A Non-Human Primate Brain-Computer Typing Interface.

Brain-computer interfaces (BCIs) record brain activity and translate the information into useful control signals. They can be used to restore function to people with paralysis by controlling end effectors such as computer cursors and robotic limbs. Communication neural prostheses are BCIs that control user interfaces on computers or mobile devices. Here we demonstrate a communication prosthesis by simulating a typing task with two rhesus macaques implanted with electrode arrays. The monkeys used two of the highest known performing BCI decoders to type out words and sentences when prompted one symbol/letter at a time. On average, Monkeys J and L achieved typing rates of 10.0 and 7.2 words per minute (wpm), respectively, copying text from a newspaper article using a velocity-only two dimensional BCI decoder with dwell-based symbol selection. With a BCI decoder that also featured a discrete click for key selection, typing rates increased to 12.0 and 7.8 wpm. These represent the highest known achieved communication rates using a BCI. We then quantified the relationship between bitrate and typing rate and found it approximately linear: typing rate in wpm is nearly three times bitrate in bits per second. We also compared the metrics of achieved bitrate and information transfer rate and discuss their applicability to real-world typing scenarios. Although this study cannot model the impact of cognitive load of word and sentence planning, the findings here demonstrate the feasibility of BCIs to serve as communication interfaces and represent an upper bound on the expected achieved typing rate for a given BCI throughput.

Heterologous expression of chloroplast-localized geranylgeranyl pyrophosphate synthase confers fast plant growth, early flowering and increased seed yield.

Geranylgeranyl pyrophosphate synthase (GGPS) is a key enzyme for a structurally diverse class of isoprenoid biosynthetic metabolites including gibberellins, carotenoids, chlorophylls and rubber. We expressed a chloroplast-targeted GGPS isolated from sunflower (Helianthus annuus) under control of the cauliflower mosaic virus 35S promoter in tobacco (Nicotiana tabacum). The resulting transgenic tobacco plants expressing heterologous GGPS showed remarkably enhanced growth (an increase in shoot and root biomass and height), early flowering, increased number of seed pods and greater seed yield compared with that of GUS-transgenic lines (control) or wild-type plants. The gibberellin levels in HaGGPS-transgenic plants were higher than those in control plants, indicating that the observed phenotype may result from increased gibberellin content. However, in HaGGPS-transformant tobacco plants, we did not observe the phenotypic defects such as reduced chlorophyll content and greater petiole and stalk length, which were previously reported for transgenic plants expressing gibberellin biosynthetic genes. Fast plant growth was also observed in HaGGPS-expressing Arabidopsis and dandelion plants. The results of this study suggest that GGPS expression in crop plants may yield desirable agronomic traits, including enhanced growth of shoots and roots, early flowering, greater numbers of seed pods and/or higher seed yield. This research has potential applications for fast production of plant biomass that provides commercially valuable biomaterials or bioenergy.

Single-unit activity, threshold crossings, and local field potentials in motor cortex differentially encode reach kinematics.

A diversity of signals can be recorded with extracellular electrodes. It remains unclear whether different signal types convey similar or different information and whether they capture the same or different underlying neural phenomena. Some researchers focus on spiking activity, while others examine local field potentials, and still others posit that these are fundamentally the same signals. We examined the similarities and differences in the information contained in four signal types recorded simultaneously from multielectrode arrays implanted in primary motor cortex: well-isolated action potentials from putative single units, multiunit threshold crossings, and local field potentials (LFPs) at two distinct frequency bands. We quantified the tuning of these signal types to kinematic parameters of reaching movements. We found 1) threshold crossing activity is not a proxy for single-unit activity; 2) when examined on individual electrodes, threshold crossing activity more closely resembles LFP activity at frequencies between 100 and 300 Hz than it does single-unit activity; 3) when examined across multiple electrodes, threshold crossing activity and LFP integrate neural activity at different spatial scales; and 4) LFP power in the "beta band" (between 10 and 40 Hz) is a reliable indicator of movement onset but does not encode kinematic features on an instant-by-instant basis. These results show that the diverse signals recorded from extracellular electrodes provide somewhat distinct and complementary information. It may be that these signal types arise from biological phenomena that are partially distinct. These results also have practical implications for harnessing richer signals to improve brain-machine interface control.

Endoplasmic reticulum- and Golgi-localized phospholipase A2 plays critical roles in Arabidopsis pollen development and germination.

The phospholipase A(2) (PLA(2)) superfamily of lipolytic enzymes is involved in a number of essential biological processes, such as inflammation, development, host defense, and signal transduction. Despite the proven involvement of plant PLA(2)s in many biological functions, including senescence, wounding, elicitor and stress responses, and pathogen defense, relatively little is known about plant PLA(2)s, and their genes essentially remain uncharacterized. We characterized three of four Arabidopsis thaliana PLA(2) paralogs (PLA(2)-ß, -γ, and -δ) and found that they (1) are expressed during pollen development, (2) localize to the endoplasmic reticulum and/or Golgi, and (3) play critical roles in pollen development and germination and tube growth. The suppression of PLA(2) using the RNA interference approach resulted in pollen lethality. The inhibition of pollen germination by pharmacological PLA(2) inhibitors was rescued by a lipid signal molecule, lysophosphatidyl ethanolamine. Based on these results, we propose that plant reproduction, in particular, male gametophyte development, requires the activities of the lipid-modifying PLA(2)s that are conserved in other organisms.

An Integrated 3-Dimentional Navigation System Increases the Accuracy, Efficiency, and Safety of Percutaneous Thoracolumbar Pedicle Screw Placement in Minimally Invasive Approaches: A Randomized Cadaveric Study.

STUDY DESIGN: Cadaveric study. OBJECTIVES: The purpose of this study was to compare a novel, integrated 3D navigational system (NAV) and conventional fluoroscopy in the accuracy, efficiency, and radiation exposure of thoracolumbar percutaneous pedicle screw (PPS) placement. METHODS: Twelve skeletally mature cadaveric specimens were obtained for twelve individual surgeons. Each participant placed bilateral PS at 11 segments, from T8 to S1. Prior to insertion, surgeons were randomized to the sequence of techniques and the side (left or right). Following placement, a CT scan of the spine was obtained for each cadaver, and an independent reviewer assessed the accuracy of screw placement using the Gertzbein grading system. Outcome metrics of interest included a comparison of breach incidence/severity, screw placement time, total procedure time, and radiation exposure between the techniques. Bivariate statistics were employed to compare outcomes at each level. RESULTS: A total of 262 screws (131 using each technique) were placed. The incidence of cortical breaches was significantly lower with NAV compared to FG (9% vs 18%; P = .048). Of breaches with NAV, 25% were graded as moderate or severe compared to 39% in the FG subgroup (P = .034). Median time for screw placement was significantly lower with NAV (2.7 vs 4.1 min/screw; P = .012), exclusive of registration time. Cumulative radiation exposure to the surgeon was significantly lower for NAV-guided placement (9.4 vs 134 µGy, P = .02). CONCLUSIONS: The use of NAV significantly decreased the incidence of cortical breaches, the severity of screw breeches, screw placement time, and radiation exposure to the surgeon when compared to traditional FG.

Learning leaves a memory trace in motor cortex.

How are we able to learn new behaviors without disrupting previously learned ones? To understand how the brain achieves this, we used a brain-computer interface (BCI) learning paradigm, which enables us to detect the presence of a memory of one behavior while performing another. We found that learning to use a new BCI map altered the neural activity that monkeys produced when they returned to using a familiar BCI map in a way that was specific to the learning experience. That is, learning left a "memory trace" in the primary motor cortex. This memory trace coexisted with proficient performance under the familiar map, primarily by altering neural activity in dimensions that did not impact behavior. Forming memory traces might be how the brain is able to provide for the joint learning of multiple behaviors without interference.

Cooperation and functional diversification of two closely related galactolipase genes for jasmonate biosynthesis.

Jasmonic acid (JA) plays pivotal roles in diverse plant biological processes, including wound response. Chloroplast lipid hydrolysis is a critical step for JA biosynthesis, but the mechanism of this process remains elusive. We report here that DONGLE (DGL), a homolog of DEFECTIVE IN ANTHER DEHISCENCE1 (DAD1), encodes a chloroplast-targeted lipase with strong galactolipase and weak phospholipase A(1) activity. DGL is expressed in the leaves and has a specific role in maintaining basal JA content under normal conditions, and this expression regulates vegetative growth and is required for a rapid JA burst after wounding. During wounding, DGL and DAD1 have partially redundant functions for JA production, but they show different induction kinetics, indicating temporally separated roles: DGL plays a role in the early phase of JA production, and DAD1 plays a role in the late phase of JA production. Whereas DGL and DAD1 are necessary and sufficient for JA production, phospholipase D appears to modulate wound response by stimulating DGL and DAD1 expression.

Phospholipase A(2) is required for PIN-FORMED protein trafficking to the plasma membrane in the Arabidopsis root.

Phospholipase A(2) (PLA(2)), which hydrolyzes a fatty acyl chain of membrane phospholipids, has been implicated in several biological processes in plants. However, its role in intracellular trafficking in plants has yet to be studied. Here, using pharmacological and genetic approaches, the root hair bioassay system, and PIN-FORMED (PIN) auxin efflux transporters as molecular markers, we demonstrate that plant PLA(2)s are required for PIN protein trafficking to the plasma membrane (PM) in the Arabidopsis thaliana root. PLA(2)alpha, a PLA(2) isoform, colocalized with the Golgi marker. Impairments of PLA(2) function by PLA(2)alpha mutation, PLA(2)-RNA interference (RNAi), or PLA(2) inhibitor treatments significantly disrupted the PM localization of PINs, causing internal PIN compartments to form. Conversely, supplementation with lysophosphatidylethanolamine (the PLA(2) hydrolytic product) restored the PM localization of PINs in the pla(2)alpha mutant and the ONO-RS-082-treated seedling. Suppression of PLA(2) activity by the inhibitor promoted accumulation of trans-Golgi network vesicles. Root hair-specific PIN overexpression (PINox) lines grew very short root hairs, most likely due to reduced auxin levels in root hair cells, but PLA(2) inhibitor treatments, PLA(2)alpha mutation, or PLA(2)-RNAi restored the root hair growth of PINox lines by disrupting the PM localization of PINs, thus reducing auxin efflux. These results suggest that PLA(2), likely acting in Golgi-related compartments, modulates the trafficking of PIN proteins.

An L1-regularized logistic model for detecting short-term neuronal interactions.

Interactions among neurons are a key component of neural signal processing. Rich neural data sets potentially containing evidence of interactions can now be collected readily in the laboratory, but existing analysis methods are often not sufficiently sensitive and specific to reveal these interactions. Generalized linear models offer a platform for analyzing multi-electrode recordings of neuronal spike train data. Here we suggest an L(1)-regularized logistic regression model (L(1)L method) to detect short-term (order of 3 ms) neuronal interactions. We estimate the parameters in this model using a coordinate descent algorithm, and determine the optimal tuning parameter using a Bayesian Information Criterion. Simulation studies show that in general the L(1)L method has better sensitivities and specificities than those of the traditional shuffle-corrected cross-correlogram (covariogram) method. The L(1)L method is able to detect excitatory interactions with both high sensitivity and specificity with reasonably large recordings, even when the magnitude of the interactions is small; similar results hold for inhibition given sufficiently high baseline firing rates. Our study also suggests that the false positives can be further removed by thresholding, because their magnitudes are typically smaller than true interactions. Simulations also show that the L(1)L method is somewhat robust to partially observed networks. We apply the method to multi-electrode recordings collected in the monkey dorsal premotor cortex (PMd) while the animal prepares to make reaching arm movements. The results show that some neurons interact differently depending on task conditions. The stronger interactions detected with our L(1)L method were also visible using the covariogram method.