László Zalányi

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.

Impaired associative learning in schizophrenia: behavioral and computational studies

Associative learning is a central building block of human cognition and in large part depends on mechanisms of synaptic plasticity, memory capacity and fronto–hippocampal interactions. A disorder like schizophrenia is thought to be characterized by altered plasticity, and impaired frontal and hippocampal function. Understanding the expression of this dysfunction through appropriate experimental studies, and understanding the processes that may give rise to impaired behavior through biologically plausible computational models will help clarify the nature of these deficits. We present a preliminary computational model designed to capture learning dynamics in healthy control and schizophrenia subjects. Experimental data was collected on a spatial-object paired-associate learning task. The task evinces classic patterns of negatively accelerated learning in both healthy control subjects and patients, with patients demonstrating lower rates of learning than controls. Our rudimentary computational model of the task was based on biologically plausible assumptions, including the separation of dorsal/spatial and ventral/object visual streams, implementation of rules of learning, the explicit parameterization of learning rates (a plausible surrogate for synaptic plasticity), and learning capacity (a plausible surrogate for memory capacity). Reductions in learning dynamics in schizophrenia were well-modeled by reductions in learning rate and learning capacity. The synergy between experimental research and a detailed computational model of performance provides a framework within which to infer plausible biological bases of impaired learning dynamics in schizophrenia.

Convergence and divergence are mostly reciprocated properties of the connections in the network of cortical areas

Cognition is based on the integrated functioning of hierarchically organized cortical processing streams in a manner yet to be clarified. Because integration fundamentally depends on convergence and the complementary notion of divergence of the neuronal connections, we analysed integration by measuring the degree of convergence/divergence through the connections in the network of cortical areas. By introducing a new index, we explored the complementary convergent and divergent nature of connectional reciprocity and delineated the backward and forward cortical sub-networks for the first time. Integrative properties of the areas defined by the degree of convergence/divergence through their afferents and efferents exhibited distinctive characteristics at different levels of the cortical hierarchy. Areas previously identified as hubs exhibit information bottleneck properties. Cortical networks largely deviate from random graphs where convergence and divergence are balanced at low reciprocity level. In the cortex, which is dominated by reciprocal connections, balance appears only by further increasing the number of reciprocal connections. The results point to the decisive role of the optimal number and placement of reciprocal connections in large-scale cortical integration. Our findings also facilitate understanding of the functional interactions between the cortical areas and the information flow or its equivalents in highly recurrent natural and artificial networks.

Channel noise in Hodgkin–Huxley model neurons

Abstract We study the effect of channel noise on the firing properties of the Hodgkin–Huxley equations. The spectral properties of the currents and corresponding Hurst indices are determined, and their relevance for information processing is discussed. The interspike interval histograms and their properties are also discussed.

The effect of synaptic depression on stochastic resonance

Abstract Stochastic resonance is a mechanism, where noise plays a beneficial role in amplifying weak signals arriving to some nonlinear system. A possible way to improve the signal-to-noise ratio (SNR) of the output of the system is to use several realizations, and sum and average the output of them in a proper way. In neural systems the different synaptic strengths play important role in the averaging. Earlier studies neglected the effect of the possible rapid synaptic plasticity on stochastic resonance, and tacitly assumed constant synaptic strengths. Here, we systematically examined in a small integrate-and-fire network the interaction between stochasticity and synaptic modifiability, namely synaptic depression. This kind of plasticity mechanism increased the SNR in some parameter regions.

Computational Approach to Schizophrenia: Disconnection Syndrome and Dynamical Pharmacology

Schizophrenia may be best understood in terms of abnormal interactions between different brain regions. Tasks such as associative learning that engage different brain regions may be ideal for studying altered brain function in the illness. Preliminary data suggest that the hippocampus is involved in the encoding (learning) and the prefrontal cortex in the retrieval of associative memories. Specific changes in the fMRI activities have also been observed based on comparative studies between stable schizophrenia patients and healthy control subjects. Disconnectivity, observed between brain regions in schizophrenic patients could result from abnormal modulation of N‐methyl‐D‐aspartate (NMDA)‐dependent plasticity implicated in schizophrenia.

Role of hyperpolarization-activated conductances in the auditory brainstem

Abstract This study examines the possible functional role of two hyperpolarization-activated conductances in the interaural intensity difference detector lateral superior olive (LSO). Inputs of these neurons are transformed into an output, which provides a firing-rate code for a certain interaural intensity difference range. The I h conductances effect is partly masked by the inwardly rectifying outward K + currents effect. Since resting potential, input resistance, membrane time constant, as well as synaptic release probability are all affected by the pharmacological agents used in vitro experiments, it is not easy to dissect out the role of these conductances. We therefore used computer simulations to investigate this issue. The interplay between the two hyperpolarization-activated conductances, first-spike latency, f-I function, input resistance and the width of the dynamic firing regime were examined.

Single Cell and Population Activities in Cortical-like Systems

Dynamics of single cells and large cell populations are the subject of investigation by using differently detailed models. Multicompartmental modeling techniques are used to systematically investigate the location-dependent effects of GABA-ergic inhibition on the firing patterns of hippocampal pyramidal cells. Appearance of stochastic resonance in a model of mitral and granule cells of the olfactory bulb is demonstrated by using a single-compartmental model approach. Spatial propagation of synchronized activities in hippocampal slices are studied by a model of large neural populations.

Estimating the Dynamics of Kernel-Based Evolving Networks

In this paper we present the application of a novel methodology to scientific citation and collaboration networks. This methodology is designed for understanding the governing dynamics of evolving networks and relies on an attachment kernel, a scalar function of node properties, that stochastically drives the addition and deletion of vertices and edges. We illustrate how the kernel function of a given network can be extracted from the history of the network and discuss other possible applications.