Zoltán Somogyvári

Prediction of emerging technologies based on analysis of the US patent citation network

The network of patents connected by citations is an evolving graph, which provides a representation of the innovation process. A patent citing another implies that the cited patent reflects a piece of previously existing knowledge that the citing patent builds upon. A methodology presented here (1) identifies actual clusters of patents: i.e., technological branches, and (2) gives predictions about the temporal changes of the structure of the clusters. A predictor, called the citation vector, is defined for characterizing technological development to show how a patent cited by other patents belongs to various industrial fields. The clustering technique adopted is able to detect the new emerging recombinations, and predicts emerging new technology clusters. The predictive ability of our new method is illustrated on the example of USPTO subcategory 11, Agriculture, Food, Textiles. A cluster of patents is determined based on citation data up to 1991, which shows significant overlap of the class 442 formed at the beginning of 1997. These new tools of predictive analytics could support policy decision making processes in science and technology, and help formulate recommendations for action.

Model-based source localization of extracellular action potentials.

A new model-based analysis method was set up for revealing information encrypted in extracellular spatial potential patterns of neocortical action potentials. Spikes were measured by extracellular linear multiple microelectrode in vivo cats primary auditory cortex and were analyzed based on current source density (CSD) distribution models. Validity of the monopole and other point source approximations were tested on the measured potential patterns by numerical fitting. We have found, that point source models could not provide accurate description of the measured patterns. We introduced a new model of the CSD distribution on a spiking cell, called counter-current model (CCM). This new model was shown to provide better description of the spatial current distribution of the cell during the initial negative deflection of the extracellular action potential, from the onset of the spike to the negative peak. The new model was tested on simulated extracellular potentials. We proved numerically, that all the parameters of the model could be determined accurately based on measurements. Thus, fitting of the CCM allowed extraction of these parameters from the measurements. Due to model fitting, CSD could be calculated with much higher accuracy as done with the traditional method because distance dependence of the spatial potential patterns was explicitly taken into consideration in our method. Average CSD distribution of the neocortical action potentials was calculated and spatial decay constant of the dendritic trees was determined by applying our new method.

Laminar analysis of initiation and spread of epileptiform discharges in three in vitro models

Overexcitation of neuronal networks in some forebrain structures and pathological synchronization of neuronal activity play crucial role in epileptic seizures. Seizure activity can be elicited experimentally by different convulsants. Because of various distribution of excitatory and inhibitory connections in the neocortex there might be laminar differences in seizure sensitivity. Current source density (CSD) analysis or immunocytochemical c-Fos localization offer suitable tools to localize increased activation of neurons during seizure. In the present experiments, interictal epileptiform activity elicited by 4-aminopiridine, bicuculline or Mg(2+)-free solution was recorded with a 16-channel multielectrode assembly in different layers of the somatosensory cortex, and CSDs were calculated. Parallel c-Fos immunocytochemistry was applied. Each convulsant elicited characteristic activation pattern. 4-aminopiridine induced relatively short discharges, which were associated with a huge sink in layer V, the sink and source pattern was relatively simple. Mg(2+)-free solution elicited the longest discharges, sinks appeared typically in the supragranular layers II and III than quickly distributed toward layers V and VI. Bicuculline induced rather similar seizure pattern as Mg(2+)-free solution, but the amplitudes of field potentials were larger, while the durations shorter. The peak of c-Fos activation, however, was not parallel with the largest electrical activation. Larger amount of stained cells appeared in layers II and III in 4-aminopiridine and bicuculline, respectively. In Mg(2+)-free solution the highest c-Fos activity was detected in upper layer VI. Long-lasting cellular effects do not always correspond to the largest electrical responses, which are primarily determined by the activation of asymmetrical pyramidal neurons. Interneurons, which possess more symmetric process arborisation, play less important role in the generation of field potentials, although they may be intensively activated during seizure.

Localization of single‐cell current sources based on extracellular potential patterns: the spike CSD method

Traditional current source density (tCSD) calculation method calculates neural current source distribution of extracellular (EC) potential patterns, thus providing important neurophysiological information. While the tCSD method is based on physical principles, it adopts some assumptions, which can not hold for single‐cell activity. Consequently, tCSD method gives false results for single‐cell activity. A new, spike CSD (sCSD) method has been developed, specifically designed to reveal CSD distribution of single cells during action potential generation. This method is based on the inverse solution of the Poisson‐equation. The efficiency of the method is tested and demonstrated with simulations, and showed, that the sCSD method reconstructed the original CSD more precisely than the tCSD. The sCSD method is applied to EC spatial potential patterns of spikes, measured in cat primary auditory cortex with a 16‐channel chronically implanted linear probe in vivo. Using our method, the cell–electrode distances were estimated and the spatio‐temporal CSD distributions were reconstructed. The results suggested, that the new method is potentially useful in determining fine details of the spatio‐temporal dynamics of spikes.

Technical integration of hippocampus, basal ganglia and physical models for spatial navigation

Computational neuroscience is increasingly moving beyond modeling individual neurons or neural systems to consider the integration of multiple models, often constructed by different research groups. We report on our preliminary technical integration of recent hippocampal formation, basal ganglia and physical environment models, together with visualisation tools, as a case study in the use of Python across the modelling tool-chain. We do not present new modeling results here. The architecture incorporates leaky-integrator and rate-coded neurons, a 3D environment with collision detection and tactile sensors, 3D graphics and 2D plots. We found Python to be a flexible platform, offering a significant reduction in development time, without a corresponding significant increase in execution time. We illustrate this by implementing a part of the model in various alternative languages and coding styles, and comparing their execution times. For very large-scale system integration, communication with other languages and parallel execution may be required, which we demonstrate using the BRAHMS frameworks Python bindings.

Nanosize effects on the microstructure of BaFe10.3Co0.85Ti0.85O19 hexaferrite

Nanosized and microcrystalline BaFe10.3Co0.85Ti0.85O19 samples were investigated by neutron diffraction. A strong anisotropy of the thermal expansion coefficient and its dependence on the grain size was established. The cation distributions and the magnetic moments were determined. The refined magnetic moments proved to be much lower than the theoretical spin only moments, especially for the 4e and 12k sites, indicating a local noncollinearity with short-range ordering.

Calcium buffer proteins are specific markers of human retinal neurons

Ca2+-buffer proteins (CaBPs) modulate the temporal and spatial characteristics of transient intracellular Ca2+-concentration changes in neurons in order to fine-tune the strength and duration of the output signal. CaBPs have been used as neurochemical markers to identify and trace neurons of several brain loci including the mammalian retina. The CaBP content of retinal neurons, however, varies between species and, thus, the results inferred from animal models cannot be utilised directly by clinical ophthalmologists. Moreover, the shortage of well-preserved human samples greatly impedes human retina studies at the cellular and network level. Our purpose has therefore been to examine the distribution of major CaBPs, including calretinin, calbindin-D28, parvalbumin and the recently discovered secretagogin in exceptionally well-preserved human retinal samples. Based on a combination of immunohistochemistry, Neurolucida tracing and Lucifer yellow injections, we have established a database in which the CaBP marker composition can be defined for morphologically identified cell types of the human retina. Hence, we describe the full CaBP make-up for a number of human retinal neurons, including HII horizontal cells, AII amacrine cells, type-1 tyrosine-hydroxylase-expressing amacrine cells and other lesser known neurons. We have also found a number of unidentified cells whose morphology remains to be characterised. We present several examples of the colocalisation of two or three CaBPs with slightly different subcellular distributions in the same cell strongly suggesting a compartment-specific division of labour of Ca2+-buffering by CaBPs. Our work thus provides a neurochemical framework for future ophthalmological studies and renders new information concerning the cellular and subcellular distribution of CaBPs for experimental neuroscience.

Episodes in Space: A Modeling Study of Hippocampal Place Representation

A computer model of learning and representing spatial locations is studied. The model builds on biological constraints and assumptions drawn from the anatomy and physiology of the hippocampal formation of the rat. The emphasis of the presented research is on the usability of a computer model originally proposed to describe episodic memory capabilities of the hippocampus in a spatial task. In the present model two modalities --- vision and path integration --- are contributing to the recognition of a given place. We study how place cell activity emerges due to Hebbian learning in the model hippocampus as a result of random exploration of the environment. The model is implemented in the Webots mobile robotics simulation software. Our results show that the location of the robot is well predictable from the activity of a population of model place cells, thus the model is suitable to be used as a basic building block of location-based navigation strategies. However, some properties of the stored memories strongly resembles that of episodic memories, which do not match special spatial requirements.

Slow dynamics of epileptic seizure: Analysis and model ☆

Abstract The slow dynamics of epileptic seizures are studied by combining in vivo electrophysiology, data analysis by using wavelet transformation and neural network modeling. Even a skeleton model with simple neurodynamics was able to reproduce many characteristics of the epileptic seizure.

Extraction of distance information from the activity of entorhinal grid cells: a model study

Most vertebrates are able to make detours and find shortcuts to achieve economical navigation. This ability requires the animal to keep track its direction and distance from specific locations. In rodents, direction of the animal is coded by the activity of head direction cells present in several regions of the brain, but distance information is only indirectly available, through the entorhinal cortical grid cell system. A neural system downstream from the entorhinal cortex seems to be necessary to extract the distance information from the periodic activity of grid cells. We propose that a system of such cells store the distance of the animal from important locations in the dentate gyrus region of the hippocampus and these “distance cells” might be identified with the dentate granule cells. A computational model is set up to study the neural mechanism of distance information decoding from the ensemble of grids cells. The proposed distance cells receive innervation from entorhinal grid cells, the connection strength between grid cells and distance cells is set by a one-shot-learning rule and the distance cell activity is affected by a winner-take-all mechanism. Simulation results of this model verifies that the activity of the distance cell population is able to unambiguously code the distance of the animal from important places. The proposed distance cells have a multi-peaked, patchy spatial activity pattern similar to the firing pattern of granule cells in dentate gyrus.