Daniel Gardner

Petilla terminology: nomenclature of features of GABAergic interneurons of the cerebral cortex.

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.

Bilateral Symmetry and Interneuronal Organization in the Buccal Ganglia of Aplysia

Principles of functional organization of the bilaterally symmetric buccal ganglia of Aplysia were studied in 20 identified cells used as a reference population. Four of the identified cells (two in each ganglion) are multiaction interneurons, each of which innervates six identified ipsilateral follower cells, mediating cholinergic excitation to one cell and cholinergic inhibition to five others. Bilateral coordination is effected by common inputs to all four interneurons. Ipsilateral pairs of interneurons are electrotonically coupled and produce identical synaptic actions on their common follower population. This apparent redundancy of interneuronal action leads to feed-forward summation, eliciting amplified synaptic output from each interneuron pair.

The Neuroscience Information Framework: A Data and Knowledge Environment for Neuroscience

With support from the Institutes and Centers forming the NIH Blueprint for Neuroscience Research, we have designed and implemented a new initiative for integrating access to and use of Web-based neuroscience resources: the Neuroscience Information Framework. The Framework arises from the expressed need of the neuroscience community for neuroinformatic tools and resources to aid scientific inquiry, builds upon prior development of neuroinformatics by the Human Brain Project and others, and directly derives from the Society for Neuroscience’s Neuroscience Database Gateway. Partnered with the Society, its Neuroinformatics Committee, and volunteer consultant-collaborators, our multi-site consortium has developed: (1) a comprehensive, dynamic, inventory of Web-accessible neuroscience resources, (2) an extended and integrated terminology describing resources and contents, and (3) a framework accepting and aiding concept-based queries. Evolving instantiations of the Framework may be viewed at http://nif.nih.gov, http://neurogateway.org, and other sites as they come on line.

Towards effective and rewarding data sharing.

Recently issued NIH policy statement and implementation guidelines (National Institutes of Health, 2003) promote the sharing of research data. While urging that “all data should be considered for data sharing” and “data should be made as widely and freely available as possible” the current policy requires only high-direct-cost (>US

Diphasic postsynaptic potential: a chemical synapse capable of mediating conjoint excitation and inhibition.

500,000/yr) grantees to share research data, starting 1 October 2003. Data sharing is central to science, and we agree that data should be made available.

Common Data Model for Neuroscience Data and Data Model Exchange

Two identified interneurons in each buccal ganglion of Aplysia can mediate conjoined excitation and inhibition to a single follower cell. A single presynaptic action potential in one of these interneurons produces a diphasic, depolarizing-hyperpolarizing synaptic potential apparently as a result of a single transmitter acting on two types of postsynaptic receptors in the follower cell. These receptors produce synaptic potentials with differing reversal potentials, ionic conductances, time courses, rates of decrement with repetition, pharmacological properties, and functional consequences. The excitatory receptor controls a sodium conductance, the inhibitory receptor controls a chloride conductance. Both components of the synaptic potentials can be produced by iontophoretic application of acetylcholine on the cell body of the follower cell, and each component is differentially sensitive to different cholinergic blocking agents.

Rate-limiting step of inhibitory post-synaptic current decay in Aplysia buccal ganglia.

OBJECTIVE Generalizing the data models underlying two prototype neurophysiology databases, the authors describe and propose the Common Data Model (CDM) as a framework for federating a broad spectrum of disparate neuroscience information resources. DESIGN Each component of the CDM derives from one of five superclasses-data, site, method, model, and reference-or from relations defined between them. A hierarchic attribute-value scheme for metadata enables interoperability with variable tree depth to serve specific intra- or broad inter-domain queries. To mediate data exchange between disparate systems, the authors propose a set of XML-derived schema for describing not only data sets but data models. These include biophysical description markup language (BDML), which mediates interoperability between data resources by providing a meta-description for the CDM. RESULTS The set of superclasses potentially spans data needs of contemporary neuroscience. Data elements abstracted from neurophysiology time series and histogram data represent data sets that differ in dimension and concordance. Site elements transcend neurons to describe subcellular compartments, circuits, regions, or slices; non-neuroanatomic sites include sequences to patients. Methods and models are highly domain-dependent. CONCLUSIONS True federation of data resources requires explicit public description, in a metalanguage, of the contents, query methods, data formats, and data models of each data resource. Any data model that can be derived from the defined superclasses is potentially conformant and interoperability can be enabled by recognition of BDML-described compatibilities. Such metadescriptions can buffer technologic changes.

Social Work in Health Care with Older Adults: Future Challenges:

1. In neurones BL and BR 3, 6, 8, 9, 10 and 11 of Aplysia buccal ganglia, cholinergic inhibitory post‐synaptic potentials are produced by activity in either of two presynaptic cells. In order to analyse the synaptic conductance change, neurones were voltage‐clamped inhibitory post‐synaptic currents (i.p.s.c.) recorded. 2. The synaptic conductance change rises to an average peak value of 0.65 micromho and decays exponentially with single time constant tau of 19 msec. 3. We have attempted to identify the rate‐limiting step responsible for i.p.s.c. decay from among the following possibilities: (1) acetylcholine (ACh) supply, (2) ACh removal by diffusion, (3) ACh removal by hydrolysis or (4) a slow unbinding or conformational change closing open synaptic current channels. 4. Cooling prolongs tau, with Q10 of 5.2. Cooling and eserine treatment together produce greatly prolonged, exponentially decaying i.p.s.c.s with tau > 150 msec. These results suggest that ACh removal, either by diffusion or hydrolysis, is not the rate‐limiting step. 5. Prolonging synaptic action potential time course with intracellular injection of tetraethylammonium broadens the i.p.s.c. peak but does not affect the decay tail, suggesting that the rate‐limiting step is not ACh release. 6. The spectrum of ACh‐induced current fluctuations is fitted by a double Lorentzian with cut‐off frequencies of 7.8 and 47 Hz. The frequency of the slower component corresponds to the macroscopic i.p.s.c. decay tau. 7. We conclude that a slow conformational change closing open synaptic current channels is likely to determine i.p.s.c. decay. We cannot, however, exclude either delayed diffusion or a late tail of slow ACh release as possibilities.

Spike Train Analysis Toolkit: Enabling Wider Application of Information-Theoretic Techniques to Neurophysiology

Dramatic changes in demography, epidemiology, and the financing and delivery of health care have significantly affected the lives of older adults and their families. The authors review current and future trends in aging and health care in the United States and their implications for social work practice in enhancing the health and well-being of older adults and their caregivers. Health care social work in the 21st century requires the development and application of evidence-based knowledge that reflects the interrelatedness of aging, physical and mental health, and intergenerational family processes. The authors demonstrate the value of social work research to issues that are in the forefront of gerontological health care practice, policy, and education.

Neurodatabase.org: networking the microelectrode

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.