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At least 91 records · Page 5Linked to original sources
PubMed1987

Influence of noise on the function of a "physiological" neural network.

A model neural network with stochastic elements in its millisecond dynamics is investigated. The network consists of neuronal units which are modelled in close analogy to physiological neurons. Dynamical variables of the network are the cellular potentials, axonic currents and synaptic efficacies. The dynamics of the synapses obeys a modified Hebbian rule and, as proposed by v. d. Malsburg (1981, 1985), develop on a time scale of a tenth of a second. In a previous publication (Buhmann and Schulten 1986) we have confirmed that the resulting noiseless auto-associative network is capable of the well-known computational tasks of formal associative networks (Cooper 1973; Kohonen et al. 1984, 1981; Hopfield 1982). In the present paper we demonstrate that random fluctuations of the membrane potential improve the performance of the network. In comparison to a deterministic network a noisy neural network can learn at lower input frequencies and with lower average neural firing rates. The electrical activity of a noisy network is very reminiscent of that observed by physiological recordings. We demonstrate furthermore that associative storage reduces the effective dimension of the phase space in which the electrical activity of the network develops.

Animals↗
PubMed1991

A neural network model of the cerebellar cortex performing dynamic associations.

The present paper proposes a model which applies formal neural network modeling techniques to construct a theoretical representation of the cerebellar cortex and its performances in motor control. A schema that makes explicit use of propagation delays of neural signals, is introduced to describe the ability to store temporal sequences of patterns in the Golgi-granule cell system. A perceptron association is then performed on these sequences of patterns by the Purkinje cell layer. The model conforms with important biological constraints, such as the known excitatory or inhibitory nature of the various synapses. Also, as suggested by experimental evidence, the synaptic plasticity underlying the learning ability of the model, is confined to the parallel fiber--Purkinje cell synapses, and takes place under the control of the climbing fibers. The result is a neural network model, constructed according to the anatomy of the cerebellar cortex, and capable of learning and retrieval of temporal sequences of patterns. It provides a framework to represent and interpret properties of learning and control of movements by the cerebellum, and to assess the capacity of formal neural network techniques for modeling of real neural systems.

Animals↗
PubMed1991

Morphogenesis in shape-space. Elementary meta-dynamics in a model of the immune network.

A unique feature of the immune system is that it possesses meta-dynamics: the process governing the removal of certain clones from the active population and the recruitment of new clones from the pool of lymphocytes freshly produced by the bone marrow. In this paper, we present a computer model which focuses on those aspects of the system that characteristically derive from the meta-dynamics as such. We observe that when a region of shape-space is densely populated, there is an emergence of dynamically quasi-stable configurations. Moreover, when the system develops in the presence of permanent self-antigens, the latter are systematically incorporated into such coherent configurations. We conclude that the meta-dynamics of the biological immune system may be such that it gives rise to the emergence of a connected, self-sustaining network that we call the Central Immune System: a coherent self-identity which incorporates the molecules of the somatic self and, more generally, reflects the history of its own development.

Autoantigens↗
PubMed1992

Nuclear-cytoskeletal interactions: evidence for physical connections between the nucleus and cell periphery and their alteration by transformation.

The overall coordination of cell structure and function that results in gene expression requires a spatial and temporal precision that would be unobtainable in the absence of structural order within the cell. Cells contain extensive and elaborate three-dimensional skeletal networks that form integral structural components of the plasma membrane, cytoplasm, and nucleus. These skeletal networks form a dynamic tissue matrix are composed of the nuclear matrix, cytoskeleton, and extracellular matrix. The tissue matrix is an interactive network which undergoes dynamic changes as cells move and change shape. Pathologists have long recognized cancer in pathologic specimens based on the altered morphology of tumor cells compared to their normal counterparts. The structural order of cells appears to be altered in transformed cells. This structural order is reflected in the altered morphology and motility observed in transformed cells compared to their normal counterparts, however, it is unclear whether the structural changes observed in cancer cells have any functional significance. We report here on the nature of the physical connections between the nucleus and cell periphery in nontransformed cells and demonstrate that the nucleus is dynamically coupled to the cell periphery via actin microfilaments. We also demonstrate that the dynamic coupling of the nucleus to the cell periphery via actin microfilaments is altered in Kirsten-ras transformed rat kidney epithelial cells. This loss of structure-function relationship may be an important factor in the process of cell transformation.

Acrylamide↗
PubMed1991

Modifications of the intermediate filament and nuclear matrix networks by the extracellular matrix.

The tissue matrix system is a dynamic, interacting structural network directly linking the nuclear matrix, cytoskeleton and the extracellular matrix. We report here that interaction of normal rat kidney epithelial cells (NRK) and Kirsten-ras transformed rat kidney cells, with an extracellular matrix secreted by tumor cells, causes modifications to the protein composition of the intermediate filament and nuclear matrix networks. The matrix networks are different between normal and transformed cells; however, these alterations by the tumor extracellular matrix are similar in both cell types. These data represent the first report that modification of the extracellular matrix environment can have an effect on the protein composition of the nuclear matrix.

Animals↗
PubMedDate not supplied

The messages in optic nerve fibers and their interpretation.

Spike discharges are the principal carriers of information in the nervous system. Although both the ionic and the molecular mechanisms of spike generation have been studied extensively, the methods for analyzing a spike train that are currently employed have not changed much from those in use 20 years ago. There is an apparent need for a refinement of the methods used to analyze spike trains. We present here a summary of our recent results of an analysis of spike trains from retinal ganglion cells that is based on Wiener's theory of non-linear analysis or white-noise analysis. We found that spike trains carry, at least to a second-order approximation, as much information as is carried by the ganglion cell's postsynaptic potential (PSP). There is no loss of information when an analog signal, PSP, is converted into a point process, namely, spike discharges. It is indeed possible to predict the cell's PSP from a spike train. This finding has two important implications. First, the neuron network in the retina produces a PSP, the dynamics of which are optimal for triggering a spike discharge, or conversely, the spike-generation mechanism is optimized to match the dynamics of the network. The external stimulus that is optimal for production of a ganglion-cell discharge is represented as the cell's PSP. Second, there is structure encoded within the spike train; information on a second-order non-linearity is encoded by the relative timing of two consecutive spike discharges. Coding of non-linearity into a spike train is an efficient means of signal compression and is an important aspect of neurophysiology.

Animals↗
PubMed1988

The microtubule-dependent formation of a tubulovesicular network with characteristics of the ER from cultured cell extracts.

The formation of a dynamic tubulovesicular membrane network that resembles the endoplasmic reticulum (ER) has been observed in extracts of cultured chick embryo fibroblasts (CEF cells) using video-enhanced differential interference contrast microscopy. Initially, membranes in the CEF extracts appeared amorphous and aggregated, but with time, membrane tubules moved out along stationary microtubules. The membrane tubules formed new branches on intersecting microtubules and fused with other branches to form a network of interconnected polygons. The tubulovesicular network was solubilized by detergent and took on a beaded morphology in a hypotonic buffer. Formation of the tubulovesicular network required ATP and microtubules. The network did not contain elements of the plasma membrane, Golgi apparatus, or mitochondria but could be labeled with ER markers. We suggest that the tubulovesicular network contains components from the ER and is formed by membrane associated motors moving upon microtubules in a process we call microtubule-dependent tethering.

Adenosine Triphosphate↗
PubMed1989

Modulations of EEG activity in the entorhinal cortex and forebrain olfactory areas during odour sampling.

With the aim of determining a possible role of the entorhinal cortex (EC) in the processing of olfactory information, its electrical activity was recorded during different types of odour sampling behaviour. The EC spontaneous field potentials (EEGs) were related to those recorded simultaneously from the more rostrally lying olfactory bulb (OB) and prepiriform cortex (PPC), using coherence functions. The coherence values are measures of coupling between two neuronal populations. Auto- and coherence spectra were characterized by a peak in the beta-band (35-36 Hz) and another in the 16-20 Hz band. For the beta-peak of the PPC-EC coherence it was shown that both the maximal values and the median frequency decrease during the first 0.5-1.0 s of a trial in a two-choice odour discrimination task. These transients differed significantly for CS+ and CS- trials. However, no such difference was observed during exploratory sniffing at the same pair of odorants. It was concluded that during sniffing the degree of coupling of the EC with the PPC depends on the behavioural context but not on the quality of the odorants. As regards the 16-20 Hz components it was shown, using bispectral analysis, that these components represent the 1/2 subharmonic of the beta-components. The subharmonic components were enhanced during the initiation of sniffing. This means that the neural networks of OB, PPC and EC have non-linear dynamic properties. These networks show different modes of oscillatory behaviour, characteristic of the restful state and of the active sniffing state. Theoretical implications of these experimental results are discussed. In conclusion, it was demonstrated that modulations with sniffing behaviour are present in the beta-EEG components and their subharmonics in the olfactory brain areas including the EC. This indicates that the latter is involved in processes of the evaluation of olfactory cues in relation to the animal's behaviour.

Animals↗
PubMed2026

Elucidating the Mechanism of Xiaoqinglong Decoction in Chronic Urticaria Treatment: An Integrated Approach of Network Pharmacology, Bioinformatics Analysis, Molecular Docking, and Molecular Dynamics Simulations.

INTRODUCTION: Xiaoqinglong Decoction (XQLD) is a traditional Chinese medicinal formula commonly used to treat chronic urticaria (CU). However, its underlying therapeutic mechanisms remain incompletely characterized. This study employed an integrated approach combining network pharmacology, bioinformatics, molecular docking, and molecular dynamics simulations to identify the active components, potential targets, and related signaling pathways involved in XQLD's therapeutic action against CU, thereby providing a mechanistic foundation for its clinical application. METHODS: The active components of XQLD and their corresponding targets were identified using the Traditional Chinese Medicine Systems Pharmacology (TCMSP) database. CU-related targets were retrieved from the OMIM and GeneCards databases. Subsequently, core components and targets were determined via protein-protein interaction (PPI) network analysis and component-target-pathway network construction. Topological analyses were performed using Cytoscape software to prioritize core nodes within these networks. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were conducted via the DAVID database to identify enriched biological processes and signaling pathways. Molecular docking was performed to evaluate binding interactions between key components and core targets, while molecular dynamics (MD) simulations were employed to assess the stability of the component-target complexes with the lowest binding energy. Finally, CU-related targets of XQLD were validated using datasets from the Gene Expression Omnibus (GEO) database. RESULTS: A total of 135 active components and 249 potential targets of XQLD were identified, alongside 1,711 CU-related targets. Core components, such as quercetin, kaempferol, beta-sitosterol, naringenin, stigmasterol, and luteolin, exhibited high degree values in the constructed networks. The core targets identified included AKT1, TNF, IL6, TP53, PTGS2, CASP3, BCL2, ESR1, PPARG, and MAPK3. GO and KEGG pathway enrichment analyses revealed the PI3K-Akt signaling pathway as a central regulatory mechanism. Molecular docking studies demonstrated strong binding affinities between active components and core targets, with the stigmasterol-AKT1 complex exhibiting the lowest binding energy (-11.4 kcal/mol) and high stability in MD simulations. Validation using GEO datasets identified 12 core genes shared between CU-related targets and XQLD-associated targets, including PTGS2 and IL6, which were also prioritized as core targets in the network pharmacology analyses. DISCUSSION: This study comprehensively integrates multidisciplinary approaches to clarify the potential molecular mechanisms of XQLD in treating CU, highlighting its multitarget and multipathway synergistic effects. Molecular docking and dynamics simulations confirm the stable interaction between stigmasterol and the core target AKT1. Additionally, GEO dataset analysis verifies the pathogenic relevance of targets such as PTGS2 and IL6, significantly enhancing the credibility of our findings. These results provide a modern scientific basis for the traditional therapeutic effects of XQLD on CU and have important implications for developing multitarget treatments for this condition. However, this study mainly relies on database mining and computational simulations. Further in vitro and in vivo experimental validations are needed to confirm the predicted component-target-pathway interactions. CONCLUSION: This study identifies the active components, potential targets, and pathways through which XQLD exerts therapeutic effects on CU. These findings provide a theoretical foundation for further mechanistic studies and support their clinical application in the treatment of CU.

Molecular Docking Simulation↗
PubMed2023

Profiling of drug resistance in Src kinase at scale uncovers a regulatory network coupling autoinhibition and catalytic domain dynamics.

Kinase inhibitors are effective cancer therapies, but resistance often limits clinical efficacy. Despite the cataloging of numerous resistance mutations, our understanding of kinase inhibitor resistance is still incomplete. Here, we comprehensively profiled the resistance of ∼3,500 Src tyrosine kinase mutants to four different ATP-competitive inhibitors. We found that ATP-competitive inhibitor resistance mutations are distributed throughout Src's catalytic domain. In addition to inhibitor contact residues, residues that participate in regulating Src's phosphotransferase activity were prone to the development of resistance. Unexpectedly, we found that a resistance-prone cluster of residues located on the top face of the N-terminal lobe of Src's catalytic domain contributes to autoinhibition by reducing catalytic domain dynamics, and mutations in this cluster led to resistance by lowering inhibitor affinity and promoting kinase hyperactivation. Together, our studies demonstrate how drug resistance profiling can be used to define potential resistance pathways and uncover new mechanisms of kinase regulation.

src-Family Kinases↗
PubMed1990

Dynamics of a class of immune networks. I. Global stability of idiotype interactions.

This paper establishes the conditions under which a class of differential equations which appear in the study of immune systems (Varela et al., 1988a, In: Theoretical Immunology Part II. New Jersey: Addison Wesley), are globally stable. This is proved by adapting a Liapunov functional originally proposed by Cohen & Grossberg (1983, IEEE Transac SMC 13, 815-826) for competitive systems. The global stability thus obtained is valid on the fast time scale where only idiotypic interactions are relevant, thus excluding both lymphocyte proliferation processes and repertoire change via recruitment from immature bone marrow B cells.

Animals↗
PubMed1990

Dynamics of a class of immune networks. II. Oscillatory activity of cellular and humoral components.

Simulations show that for a certain range of its free parameters, a model of the immune network including both cellular and humoral components is capable of self-sustained oscillations. The model also possesses a number of fixed points which appear to be unstable. These results, taken together, suggest the hypothesis that the immune network may be able to sustain a non-degenerate diversity of active clones which are actively connected to each other only on condition that the activities of these clones are oscillatory.

Animals↗
PubMed1990

Complex dynamics and noise in simple neural networks with delayed mixed feedback.

This paper briefly reviews the role of mixed feedback, neural delays, and neural noise in the genesis of complex oscillations in neurological feedback systems. The results are concretely discussed within the context of recurrent inhibition in the mammalian hippocampus, and a hybrid version of the pupil light reflex with externally imposed electronic feedback.

Animals↗
PubMed2024

Regulating IL-2 Immune Signaling Function Via A Core Allosteric Structural Network.

Human interleukin-2 (IL-2) is a crucial cytokine for T cell regulation, with therapeutic potential in cancer and autoimmune diseases. However, IL-2's pleiotropic effects across different immune cell types often lead to toxicity and limited efficacy. Previous efforts to enhance IL-2's therapeutic profile have focused on modifying its receptor binding sites. Yet, the underlying dynamics and intramolecular networks contributing to IL-2 receptor recognition remain unexplored. This study presents a detailed characterization of IL-2 dynamics compared to two engineered IL-2 mutants, "superkines" S15 and S1, which exhibit biased signaling towards effector T cells. Using NMR spectroscopy and molecular dynamics simulations, we demonstrate significant variations in core dynamic pathways and conformational exchange rates across these three IL-2 variants. We identify distinct allosteric networks and minor state conformations in the superkines, despite their structural similarity to wild-type IL-2. Furthermore, we rationally design a mutation (L56A) in the S1 superkine's core network, which partially reverts its dynamics, receptor binding affinity, and T cell signaling behavior towards that of wild-type IL-2. Our results reveal that IL-2 superkine core dynamics play a critical role in their enhanced receptor binding and function, suggesting that modulating IL-2 dynamics and core allostery represents an untapped approach for designing immunotherapies with improved immune cell selectivity profiles.

Interleukin-2↗
PubMed1978

Self-stimulation in the rat: quantitative characteristics of the reward pathway.

Quantitative characteristics of the neural pathway that carries the reinforcing signal in electrical self-stimulation of the brain were established by finding which combinations of stimulation parameters give the same performance in a runway. The reward for each run was a train of evenly spaced monophasic cathodal pulses from a monopolar electrode. With train duration and pulse frequency held constant, the required current was a hyperbolic function of pulse duration, with chronaxie c approximately 1.5 msec. With pulse duration held constant, the required strength of the train (the charge delivered per second) was a hyperbolic function of train duration, with chronaxie C approximately 500 msec. To a first approximation, the values of c and C were independent of the choice either of train duration and pulse frequency or of pulse duration, respectively. Hence, the current intensity required by any choice of train duration, pulse frequency, and pulse duration dependent on only two basic parameters, c and C, and one quantity, Qi, the required impulse charge. These may reflect, respectively, current integration by directly excited neurons; temporal integration of neural activity by synaptic processes in a neural network; and the peak of the impulse response of the network, assuming that the network has linear dynamics and that the reward depends on the peak of the output of the network.

Animals↗
PubMed1986

Associative recognition and storage in a model network of physiological neurons.

We consider a neural network model in which the single neurons are chosen to closely resemble known physiological properties. The neurons are assumed to be linked by synapses which change their strength according to Hebbian rules on a short time scale (100 ms). The dynamics of the network--the time evolution of the cell potentials and the synapses--is investigated by computer simulation. As in more abstract network models (Cooper 1973; Hopfield 1982; Kohonen 1984) it is found that the local dynamics of the cell potentials and the synaptic strengths result in global cooperative properties of the network and enable the network to process an incoming flux of information and to learn and store patterns associatively. A trained net can associate missing details of a pattern, can correct wrong details and can suppress noise in a pattern. The network can further abstract the prototype from a series of patterns with variations. A suitable coupling constant connecting the dynamics of the cell potentials with the synaptic strengths is derived by a mean field approximation. This coupling constant controls the neural sensitivity and thereby avoids both extremes of the network state, the state of permanent inactivity and the state of epileptic hyperactivity.

Animals↗
PubMed2024

Seasonal hydrological dynamics affected the diversity and assembly process of the antibiotic resistome in a canal network.

The significant threat of antibiotic resistance genes (ARGs) to aquatic environments health has been widely acknowledged. To date, several studies have focused on the distribution and diversity of ARGs in a single river while their profiles in complex river networks are largely known. Here, the spatiotemporal dynamics of ARG profiles in a canal network were examined using high-throughput quantitative PCR, and the underlying assembly processes and its main environmental influencing factors were elucidated using multiple statistical analyses. The results demonstrated significant seasonal dynamics with greater richness and relative abundance of ARGs observed during the dry season compared to the wet season. ARG profiles exhibited a pronounced distance-decay pattern in the dry season, whereas no such pattern was evident in the wet season. Null model analysis indicated that deterministic processes, in contrast to stochastic processes, had a significant impact on shaping the ARG profiles. Furthermore, it was found that Firmicutes and pH emerged as the foremost factors influencing these profiles. This study enhanced our comprehension of the variations in ARG profiles within canal networks, which may contribute to the design of efficient management approaches aimed at restraining the propagation of ARGs.

Seasons↗