Bringing Anatomical Information into Neuronal Network Models.

For constructing neuronal network models computational neuroscientists have access to wide-ranging anatomical data that nevertheless tend to cover only a fraction of the parameters to be determined. Finding and interpreting the most relevant data, estimating missing values, and combining the data and estimates from various sources into a coherent whole is a daunting task. With this chapter we aim to provide guidance to modelers by describing the main types of anatomical data that may be useful for informing neuronal network models. We further discuss aspects of the underlying experimental techniques relevant to the interpretation of the data, list particularly comprehensive data sets, and describe methods for filling in the gaps in the experimental data. Such methods of "predictive connectomics" estimate connectivity where the data are lacking based on statistical relationships with known quantities. Exploiting organizational principles that link the plethora of data in a unifying framework can be useful for informing computational models. Besides overarching principles, we touch upon the most prominent features of brain organization that are likely to influence predicted neuronal network dynamics, with a focus on the mammalian cerebral cortex. Given the still existing need for modelers to navigate a complex data landscape full of holes and stumbling blocks, it is vital that the field of neuroanatomy is moving toward increasingly systematic data collection, representation, and publication.

A new class of multi-stable neural networks: stability analysis and learning process.

Recently, multi-stable Neural Networks (NN) with exponential number of attractors have been presented and analyzed theoretically; however, the learning process of the parameters of these systems while considering stability conditions and specifications of real world problems has not been studied. In this paper, a new class of multi-stable NNs using sinusoidal dynamics with exponential number of attractors is introduced. The sufficient conditions for multi-stability of the proposed system are posed using Lyapunov theorem. In comparison to the other methods in this class of multi-stable NNs, the proposed method is used as a classifier by applying a learning process with respect to the topological information of data and conditions of Lyapunov multi-stability. The proposed NN is applied on both synthetic and real world datasets with an accuracy comparable to classical classifiers.

Neural associative memories for the integration of language, vision and action in an autonomous agent.

Language understanding is a long-standing problem in computer science. However, the human brain is capable of processing complex languages with seemingly no difficulties. This paper shows a model for language understanding using biologically plausible neural networks composed of associative memories. The model is able to deal with ambiguities on the single word and grammatical level. The language system is embedded into a robot in order to demonstrate the correct semantical understanding of the input sentences by letting the robot perform corresponding actions. For that purpose, a simple neural action planning system has been combined with neural networks for visual object recognition and visual attention control mechanisms.

Pelvicaliceal biomodeling as an aid to achieving optimal access in percutaneous nephrolithotripsy.

PURPOSE: To investigate prospectively the benefits of three-dimensional stereolithographic biomodeling produced from CT data as an aid to achieving optimal access for percutaneous nephrolithotripsy (PCNL). PATIENTS AND METHODS: Eight patients with complex urinary calculi were selected. Multislice CT scans of the kidney in native and excretory phases were acquired with the patient in the prone position to simulate the position during surgery. Contiguous reconstructed slices were produced from the data volume. The data of interest were processed to transform them into a format acceptable for production of a biomodel. Exact plastic replicas of the pelvicaliceal system and the calculi were created and used for morphologic assessment, preoperative planning, patient education, and surgical navigation. RESULTS: The survey results were based on subjective opinions rather than objective data. The biomodels enhanced the ability to visualize a patient's unique anatomy before surgery. This aided the planning and rehearsal of endourologic procedures. CONCLUSION: Although this study is only a preliminary investigation, we postulate that biomodeling has the advantage of allowing imaging data to be displayed in a physical form. In difficult cases, this technique may improve treatment, operative planning, and communication with colleagues and patients. The limitations of the technology include the manufacturing time and cost, but more accurate puncture-site selection may reduce costs by saving operating time.

Pattern separation and synchronization in spiking associative memories and visual areas.

Scene analysis in the mammalian visual system, conceived as a distributed and parallel process, faces the so-called binding problem. As a possible solution, the temporal correlation hypothesis has been suggested and implemented in phase-coding models. We propose an alternative model that reproduces experimental findings of synchronized and desynchronized fast oscillations more closely. This model is based on technical considerations concerning improved pattern separation in associative memories on the one hand, and on known properties of the visual cortex on the other. It consists of two reciprocally connected areas, one corresponding to a peripheral visual area (P), the other a central association area (C). P implements the orientation-selective subsystem of the primary visual cortex, while C was modeled as an associative memory with connections formed by Hebbian learning of all assemblies corresponding to stimulus objects. Spiking neurons including habituation and correlated noise were incorporated as well as realistic synaptic delays. Three learned stimuli were presented simultaneously and correlation analysis was performed on spike recordings. Generally, we found two states of activity: (i) relatively slow and unordered oscillations at about 20-25 Hz, synchronized only within small regions; and (ii) faster and more precise oscillations around 50-60 Hz, synchronized over the whole simulated area. The neuron groups representing one stimulus tended to be simultaneously in either the slow or the fast state. At each particular time, only one assembly was found to be in the fast state. Activation of the three assemblies switched on a time scale of 100 ms. This can be interpreted as self-generated attention switching. On the time scale corresponding to gamma oscillations, cross correlations between local neuron groups were either modulated or flat. Modulated correlograms resulted if the groups coded features corresponding to a common object. Otherwise, the correlograms remained flat. This behavior is in agreement with experimental results, while phase-code models would generally predict modulated correlations also in the case of different objects. Furthermore, we derive a technical version from our biological associative memory model that accomplishes fast pattern separation parallel in O(log2 n) steps for n neurons and sparse coding.

Three learning phases for radial-basis-function networks.

In this paper, learning algorithms for radial basis function (RBF) networks are discussed. Whereas multilayer perceptrons (MLP) are typically trained with backpropagation algorithms, starting the training procedure with a random initialization of the MLP's parameters, an RBF network may be trained in many different ways. We categorize these RBF training methods into one-, two-, and three-phase learning schemes. Two-phase RBF learning is a very common learning scheme. The two layers of an RBF network are learnt separately; first the RBF layer is trained, including the adaptation of centers and scaling parameters, and then the weights of the output layer are adapted. RBF centers may be trained by clustering, vector quantization and classification tree algorithms, and the output layer by supervised learning (through gradient descent or pseudo inverse solution). Results from numerical experiments of RBF classifiers trained by two-phase learning are presented in three completely different pattern recognition applications: (a) the classification of 3D visual objects; (b) the recognition hand-written digits (2D objects); and (c) the categorization of high-resolution electrocardiograms given as a time series (ID objects) and as a set of features extracted from these time series. In these applications, it can be observed that the performance of RBF classifiers trained with two-phase learning can be improved through a third backpropagation-like training phase of the RBF network, adapting the whole set of parameters (RBF centers, scaling parameters, and output layer weights) simultaneously. This, we call three-phase learning in RBF networks. A practical advantage of two- and three-phase learning in RBF networks is the possibility to use unlabeled training data for the first training phase. Support vector (SV) learning in RBF networks is a different learning approach. SV learning can be considered, in this context of learning, as a special type of one-phase learning, where only the output layer weights of the RBF network are calculated, and the RBF centers are restricted to be a subset of the training data. Numerical experiments with several classifier schemes including k-nearest-neighbor, learning vector quantization and RBF classifiers trained through two-phase, three-phase and support vector learning are given. The performance of the RBF classifiers trained through SV learning and three-phase learning are superior to the results of two-phase learning, but SV learning often leads to complex network structures, since the number of support vectors is not a small fraction of the total number of data points.

Conformational change of elongation factor Tu (EF-Tu) induced by antibiotic binding. Crystal structure of the complex between EF-Tu.GDP and aurodox.

Aurodox is a member of the family of kirromycin antibiotics, which inhibit protein biosynthesis by binding to elongation factor Tu (EF-Tu). We have determined the crystal structure of the 1:1:1 complex of Thermus thermophilus EF-Tu with GDP and aurodox to 2.0-A resolution. During its catalytic cycle, EF-Tu adopts two strikingly different conformations depending on the nucleotide bound: the GDP form and the GTP form. In the present structure, a GTP complex-like conformation of EF-Tu is observed, although GDP is bound to the nucleotide-binding site. This is consistent with previous proposals that aurodox fixes EF-Tu on the ribosome by locking it in its GTP form. Binding of EF-Tu.GDP to aminoacyl-tRNA and mutually exclusive binding of kirromycin and elongation factor Ts to EF-Tu can be explained on the basis of the structure. For many previously observed mutations that provide resistance to kirromycin, it can now be understood how they prevent interaction with the antibiotic. An unexpected feature of the structure is the reorientation of the His-85 side chain toward the nucleotide-binding site. We propose that this residue stabilizes the transition state of GTP hydrolysis, explaining the acceleration of the reaction by kirromycin-type antibiotics.

Crystallization, structure solution and refinement of hen egg-white lysozyme at pH 8.0 in the presence of MPD.

Hen egg-white lysozyme has been crystallized at slightly alkaline pH using 2-methyl-2,4-pentanediol (MPD) as the precipitant. The crystals are nearly isomorphous to crystals grown at acidic pH using sodium chloride as the precipitant. However, the growth kinetics differ markedly between the two conditions. The major reason for this is a molecule of MPD that binds tightly in between two lysozyme molecules and favors the growth of the crystals along the crystallographic c direction over growth perpendicular to it.

Crystal structure of RNA 3'-terminal phosphate cyclase, a ubiquitous enzyme with unusual topology.

BACKGROUND: RNA cyclases are a family of RNA-modifying enzymes that are conserved in eucarya, bacteria and archaea. They catalyze the ATP-dependent conversion of the 3'-phosphate to the 2',3'-cyclic phosphodiester at the end of RNA, in a reaction involving formation of the covalent AMP-cyclase intermediate. These enzymes might be responsible for production of the cyclic phosphate RNA ends that are known to be required by many RNA ligases in both prokaryotes and eukaryotes. RESULTS: The high-resolution structure of the Escherichia coli RNA 3'-terminal phosphate cyclase was determined using multiwavelength anomalous diffraction. Two orthorhombic crystal forms of E. coli cyclase (space group P2(1)2(1)2(1) and P2(1)2(1)2) were used to solve and refine the structure to 2.1 A resolution (R factor 20.4%; R(free) 27.6%). Each molecule of RNA cyclase consists of two domains. The larger domain contains three repeats of a folding unit comprising two parallel alpha helices and a four-stranded beta sheet; this fold was previously identified in translation initiation factor 3 (IF3). The large domain is similar to one of the two domains of 5-enolpyruvylshikimate-3-phosphate synthase and UDP-N-acetylglucosamine enolpyruvyl transferase. The smaller domain uses a similar secondary structure element with different topology, observed in many other proteins such as thioredoxin. CONCLUSIONS: The fold of RNA cyclase consists of known elements connected in a new and unique manner. Although the active site of this enzyme could not be unambiguously assigned, it can be mapped to a region surrounding His309, an adenylate acceptor, in which a number of amino acids are highly conserved in the enzyme from different sources. The structure of E. coli cyclase will be useful for interpretation of structural and mechanistic features of this and other related enzymes.

Dynamical cluster analysis of cortical fMRI activation.

Localized changes in cortical blood oxygenation during voluntary movements were examined with functional magnetic resonance imaging (fMRI) and evaluated with a new dynamical cluster analysis (DCA) method. fMRI was performed during finger movements with eight subjects on a 1.5-T scanner using single-slice echo planar imaging with a 107-ms repetition time. Clustering based on similarity of the detailed signal time courses requires besides the used distance measure no assumptions about spatial location and extension of activation sites or the shape of the expected activation time course. We discuss the basic requirements on a clustering algorithm for fMRI data. It is shown that with respect to easy adjustment of the quantization error and reproducibility of the results DCA outperforms the standard k-means algorithm. In contrast to currently used clustering methods for fMRI, like k-means or fuzzy k-means, DCA extracts the appropriate number and initial shapes of representative signal time courses from data properties during run time. With DCA we simultaneously calculate a two-dimensional projection of cluster centers (MDS) and data points for online visualization of the results. We describe the new DCA method and show for the well-studied motor task that it detects cortical activation loci and provides additional information by discriminating different shapes and phases of hemodynamic responses. Robustness of activity detection is demonstrated with respect to repeated DCA runs and effects of different data preprocessing are shown. As an example of how DCA enables further analysis we examined activation onset times. In areas SMA, M1, and S1 simultaneous and sequential activation (in the given order) was found.

Direct visualization of HIV-1 entry: mechanisms and role of cell surface receptors.

Highly fluorescent virions of T- and M-tropic HIV-1 strains were obtained by incorporation of the viral accessory protein Vpr, fused to the green fluorescent protein, in trans. The fluorescent virions displayed normal morphology, were infectious, and could be used for direct visualization of HIV-1 attachment and trafficking in various cell lines. More than 90% of the viral particles were found to enter the cells by direct membrane fusion in T-cells, CD4+ HeLa cells, and macrophages. Visualizing HIV-1 attachment and entry in the absence or presence of CD4 and/or the appropriate coreceptors indicated that CD4 is the major receptor for virus attachment in the case of JR-CSF and NL-4-3 HIV-1 isolates; however, the coreceptors are required for membrane fusion. Internalization of the coreceptor CXCR4 inhibited entry, but did not prevent virus binding suggesting that transient downregulation of the coreceptor(s) may not be the most efficient way of blocking HIV infection in vivo.

Synaptic delay learning in pulse-coupled neurons.

We present rules for the unsupervised learning of coincidence between excitatory postsynaptic potentials (EPSPs) by the adjustment of postsynaptic delays between the transmitter binding and the opening of ion channels. Starting from a gradient descent scheme, we develop a robust and more biological threshold rule by which EPSPs from different synapses can be gradually pulled into coincidence. The synaptic delay changes are determined from the summed potential--at the site where the coincidence is to be established--and from postulated synaptic learning functions that accompany the individual EPSPs. According to our scheme, templates for the detection of spatiotemporal patterns of synaptic activation can be learned, which is demonstrated by computer simulation. Finally, we discuss possible relations to biological mechanisms.

Crystal structure and amino acid sequence of Wolinella succinogenes L-asparaginase.

The amino acid sequence and tertiary structure of Wolinella succinogenes L-asparaginase were determined, and were compared with the structures of other type-II bacterial L-asparaginases. Each chain of this homotetrameric enzyme consists of 330 residues. The amino acid sequence is 40-50% identical to the sequences of related proteins from other bacterial sources, and all residues previously shown to be crucial for the catalytic action of these enzymes are identical. Differences between the amino acid sequence of W. succinogenes L-asparaginase and that of related enzymes are discussed in terms of the possible influence on the substrate specificity. The overall fold of the protein subunit is almost identical to that observed for other L-asparaginases. Two fragments in each subunit, a very highly flexible loop (approximately 20 amino acids) that forms part of the active site, and the N-terminus (two amino acids), are not defined in the structure. The orientation of Thr14, a residue probably involved in the catalytic activity, indicates the absence of ligand in the active-site pocket. The rigid part of the active site, which includes the asparaginase triad Thr93-Lys 166-Asp94, is structurally very highly conserved with equivalent regions found in other type-II bacterial L-asparaginases.

A covalently bound catalytic intermediate in Escherichia coli asparaginase: crystal structure of a Thr-89-Val mutant.

Escherichia coli asparaginase II catalyzes the hydrolysis of L-asparagine to L-aspartate via a threonine-bound acyl-enzyme intermediate. A nearly inactive mutant in which one of the active site threonines, Thr-89, was replaced by valine was constructed, expressed, and crystallized. Its structure, solved at 2.2 A resolution, shows high overall similarity to the wild-type enzyme, but an aspartyl moiety is covalently bound to Thr-12, resembling a reaction intermediate. Kinetic analysis confirms the deacylation deficiency, which is also explained on a structural basis. The previously identified oxyanion hole is described in more detail.

Thermostable aminoacylase from Bacillus stearothermophilus: significance of the metal center for catalysis and protein stability.

A thermostable aminoacylase (N-acylamino acid amidohydrolase, EC 3.5.1.14) from Bacillus stearothermophilus was overexpressed in E. coli and characterized with respect to metal content, metal dependence, heat stability, and quaternary structure. Like other enzymes of the aminoacylase family, native aminoacylase contains one Zn2+ ion per subunit. Several other transition metal ions (Co2+, Mn2+ and Cd2+) also sustain aminoacylase activity toward N-acetyl L-alanine with Cd2+ giving the highest turnover number. The stability constants of the respective metal complexes were estimated by activity measurements in metal buffer systems. Co2+ also acts as an activator mainly by lowering the Km for the substrate. These data and CD spectra obtained with the native and the metal-free enzyme suggest a predominantly structural role for the intrinsic metal ion of thermostable aminoacylase. In contrast to previous reports the enzyme behaved as a dimer in analytical gel filtration.

Detecting higher-order interactions among the spiking events in a group of neurons.

We propose a formal framework for the description of interactions among groups of neurons. This framework is not restricted to the common case of pair interactions, but also incorporates higher-order interactions, which cannot be reduced to lower-order ones. We derive quantitative measures to detect the presence of such interactions in experimental data, by statistical analysis of the frequency distribution of higher-order correlations in multiple neuron spike train data. Our first step is to represent a frequency distribution as a Markov field on the minimal graph it induces. We then show the invariance of this graph with regard to changes of state. Clearly, only linear Markov fields can be adequately represented by graphs. Higher-order interdependencies, which are reflected by the energy expansion of the distribution, require more complex graphical schemes, like constellations or assembly diagrams, which we introduce and discuss. The coefficients of the energy expansion not only point to the interactions among neurons but are also a measure of their strength. We investigate the statistical meaning of detected interactions in an information theoretic sense and propose minimum relative entropy approximations as null hypotheses for significance tests. We demonstrate the various steps of our method in the situation of an empirical frequency distribution on six neurons, extracted from data on simultaneous multineuron recordings from the frontal cortex of a behaving monkey and close with a brief outlook on future work.

Aminoacylase I from porcine kidney: identification and characterization of two major protein domains.

The domain structure of hog-kidney aminoacylase I was studied by limited proteolytic digestion with trypsin and characterization of the resulting fragments. In the native enzyme, the sequences from residue 6 to 196 and 307 to 406 are resistant to trypsin and remain tightly bound in nondenaturing solvents, while the intervening sequence (197-306) is efficiently degraded by trypsin. We conclude that the N-terminal half of the molecule and its C-terminal fourth form two independently folded domains. Both contain a peculiar PWW(A,L) sequence motif preceded by several strongly polar residues. We propose that these sequences form surface loops that mediate the membrane association of aminoacyclase I. We further show that the three free cysteine residues and the essential Zn2+ ion reside in the trypsin-resistant domains, while the intervening sequence contains the only disulfide H bond of the protein.