Faculty Bibliography

Studies of hand manipulation neurons in posterior parietal cortex of monkeys suggest that their spike trains represent objects by the hand postures needed for grasping or by the underlying patterns of muscle activation. To analyze the role of hand kinematics and object properties in a trained prehension task, we correlated the firing rates of neurons in anterior area 5 with hand behaviors as monkeys grasped and lifted knobs of different shapes and locations in the workspace. Trials were divided into four classes depending on the approach trajectory: forward, lateral, and local approaches, and regrasps. The task factors controlled by the animal-how and when he used the hand-appeared to play the principal roles in modulating firing rates of area 5 neurons. In all, 77% of neurons studied (58/75) showed significant effects of approach style on firing rates; 80% of the population responded at higher rates and for longer durations on forward or lateral approaches that included reaching, wrist rotation, and hand preshaping prior to contact, but only 13% distinguished the direction of reach. The higher firing rates in reach trials reflected not only the arm movements needed to direct the hand to the target before contact, but persisted through the contact, grasp, and lift stages. Moreover, the approach style exerted a stronger effect on firing rates than object features, such as shape and location, which were distinguished by half of the population. Forty-three percent of the neurons signaled both the object properties and the hand actions used to acquire them. However, the spread in firing rates evoked by each knob on reach and no-reach trials was greater than distinctions between different objects grasped with the same approach style. Our data provide clear evidence for synergies between reaching and grasping that may facilitate smooth, coordinated actions of the arm and hand

Conventional methods widely available for the analysis of spike trains and related neural data include various time- and frequency-domain analyses, such as peri-event and interspike interval histograms, spectral measures, and probability distributions. Information theoretic methods are increasingly recognized as significant tools for the analysis of spike train data. However, developing robust implementations of these methods can be time-consuming, and determining applicability to neural recordings can require expertise. In order to facilitate more widespread adoption of these informative methods by the neuroscience community, we have developed the Spike Train Analysis Toolkit. STAToolkit is a software package which implements, documents, and guides application of several information-theoretic spike train analysis techniques, thus minimizing the effort needed to adopt and use them. This implementation behaves like a typical Matlab toolbox, but the underlying computations are coded in C for portability, optimized for efficiency, and interfaced with Matlab via the MEX framework. STAToolkit runs on any of three major platforms: Windows, Mac OS, and Linux. The toolkit reads input from files with an easy-to-generate text-based, platform-independent format. STAToolkit, including full documentation and test cases, is freely available open source via http://neuroanalysis.org , maintained as a resource for the computational neuroscience and neuroinformatics communities. Use cases drawn from somatosensory and gustatory neurophysiology, and community use of STAToolkit, demonstrate its utility and scope

The Neuroscience Information Framework (NIF), developed for the NIH Blueprint for Neuroscience Research and available at http://nif.nih.gov and http://neurogateway.org , is built upon a set of coordinated terminology components enabling data and web-resource description and selection. Core NIF terminologies use a straightforward syntax designed for ease of use and for navigation by familiar web interfaces, and readily exportable to aid development of relational-model databases for neuroscience data sharing. Datasets, data analysis tools, web resources, and other entities are characterized by multiple descriptors, each addressing core concepts, including data type, acquisition technique, neuroanatomy, and cell class. Terms for each concept are organized in a tree structure, providing is-a and has-a relations. Broad general terms near each root span the category or concept and spawn more detailed entries for specificity. Related but distinct concepts (e.g., brain area and depth) are specified by separate trees, for easier navigation than would be required by graph representation. Semantics enabling NIF data discovery were selected at one or more workshops by investigators expert in particular systems (vision, olfaction, behavioral neuroscience, neurodevelopment), brain areas (cerebellum, thalamus, hippocampus), preparations (molluscs, fly), diseases (neurodegenerative disease), or techniques (microscopy, computation and modeling, neurogenetics). Workshop-derived integrated term lists are available Open Source at http://brainml.org ; a complete list of participants is at http://brainml.org/workshops

Neuroscience produces a vast amount of data from an enormous diversity of neurons. A neuronal classification system is essential to organize such data and the knowledge that is derived from them. Classification depends on the unequivocal identification of the features that distinguish one type of neuron from another. The problems inherent in this are particularly acute when studying cortical interneurons. To tackle this, we convened a representative group of researchers to agree on a set of terms to describe the anatomical, physiological and molecular features of GABAergic interneurons of the cerebral cortex. The resulting terminology might provide a stepping stone towards a future classification of these complex and heterogeneous cells. Consistent adoption will be important for the success of such an initiative, and we also encourage the active involvement of the broader scientific community in the dynamic evolution of this project

Neurons in posterior parietal cortex (PPC) may serve both proprioceptive and exteroceptive functions during prehension, signaling hand actions and object properties. To assess these roles, we used digital video recordings to analyze responses of 83 hand-manipulation neurons in area 5 as monkeys grasped and lifted objects that differed in shape (round and rectangular), size (large and small spheres), and location (identical rectangular blocks placed lateral and medial to the shoulder). The task contained seven stages -- approach, contact, grasp, lift, hold, lower, relax -- plus a pretrial interval. The four test objects evoked similar spike trains and mean rate profiles that rose significantly above baseline from approach through lift, with peak activity at contact. Although representation by the spike train of specific hand actions was stronger than distinctions between grasped objects, 34% of these neurons showed statistically significant effects of object properties or hand postures on firing rates. Somatosensory input from the hand played an important role as firing rates diverged most prominently on contact as grasp was secured. The small sphere -- grasped with the most flexed hand posture -- evoked the highest firing rates in 43% of the population. Twenty-one percent distinguished spheres that differed in size and weight, and 14% discriminated spheres from rectangular blocks. Location in the workspace modulated response amplitude as objects placed across the midline evoked higher firing rates than positions lateral to the shoulder. We conclude that area 5 neurons, like those in area AIP, integrate object features, hand actions, and grasp postures during prehension

Prehension responses of 76 neurons in primary somatosensory (S-I) and motor (M-I) cortices were analyzed in three macaques during performance of a grasp and lift task. Digital video recordings of hand kinematics synchronized to neuronal spike trains were compared with responses in posterior parietal areas 5 and AIP/7b (PPC) of the same monkeys during seven task stages: 1) approach, 2) contact, 3) grasp, 4) lift, 5) hold, 6) lower, and 7) relax. S-I and M-I firing patterns signaled particular hand actions, rather than overall task goals. S-I responses were more diverse than those in PPC, occurred later in time, and focused primarily on grasping. Sixty-three percent of S-I neurons fired at peak rates during contact and/or grasping. Lift, hold, and lowering excited fewer S-I cells. Only 8% of S-I cells fired at peak rates before contact, compared with 27% in PPC. M-I responses were also diverse, forming functional groups for hand preshaping, object acquisition, and grip force application. M-I activity began < or =500 ms before contact, coinciding with the earliest activity in PPC. Activation of specific muscle groups in the hand was paralleled by matching patterns of somatosensory feedback from S-I needed for efficient performance. These findings support hypotheses that predictive and planning components of prehension are represented in PPC and premotor cortex, whereas performance and feedback circuits dominate activity in M-I and S-I. Somatosensory feedback from the hand to S-I enables real-time adjustments of grasping by connections to M-I and updates future prehension plans through projections to PPC