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PubMedDate not supplied

Dynamics of sparsely connected networks of excitatory and inhibitory spiking neurons.

The dynamics of networks of sparsely connected excitatory and inhibitory integrate-and-fire neurons are studied analytically. The analysis reveals a rich repertoire of states, including synchronous states in which neurons fire regularly; asynchronous states with stationary global activity and very irregular individual cell activity; and states in which the global activity oscillates but individual cells fire irregularly, typically at rates lower than the global oscillation frequency. The network can switch between these states, provided the external frequency, or the balance between excitation and inhibition, is varied. Two types of network oscillations are observed. In the fast oscillatory state, the network frequency is almost fully controlled by the synaptic time scale. In the slow oscillatory state, the network frequency depends mostly on the membrane time constant. Finite size effects in the asynchronous state are also discussed.

Action Potentials↗
PubMed2000

Cholinergic modulation of cortical function: a hypothetical role in shifting the dynamics in cortical network.

Wide innervation of cholinergic projections throughout the cortex implies that acetylcholine (ACh) plays an essential role in information processing, but how it works is still enigmatic. Experimental as well as theoretical work in the olfactory cortex and hippocampus suggests that ACh, via the muscarinic receptors, serves to shift the dynamics of the cortical networks into a state where afferent influence predominates over intracortical influence. Recent experiments in the visual and somatosensory cortex suggested that this hypothesis could be extended to neocortex. In addition, participation of the nicotinic receptors in regulating the synaptic response in the somatosensory cortex further substantiates this hypothesis. This hypothesis, derived mainly from in vitro work, also seemed to account for results from in vivo experiments without any obvious inconsistencies.

Acetylcholine↗
PubMed2004

Reconstruction of shared nonlinear dynamics in a network.

We study a general physical network consisting of a collection of response systems with complex nonlinear dynamics, influenced by a common driver. The goal is to reconstruct dynamics, regular or chaotic, that are common to all of the response systems, working from simultaneous time series measured at the responses systems only. A fundamental theorem is stated concerning the reconstruction of the common driver. An algorithm is developed, based on the theorem, to carry out the reconstruction, and is demonstrated with several examples.

Journal Article↗
PubMed2001

Neural dynamics in cortical networks--precision of joint-spiking events.

Electrophysiological studies of cortical function on the basis of multiple single-neuron recordings reveal neuronal interactions which depend on stimulus context and behavioural events. These interactions exhibit dynamics on different time scales, with time constants down to the millisecond range. Mechanisms underlying such dynamic organization of the cortical network were investigated by experimental and theoretical approaches. We review some recent results from these studies, concentrating on the occurrence of precise joint-spiking events in cortical activity, both in physiological and in model neural networks. These findings suggest that a combinatorial neural code, based on rapid associations of groups of neurons co-ordinating their activity at the single spike level, is biologically feasible.

Action Potentials↗
PubMed2003

Two dynamically distinct inhibitory networks in layer 4 of the neocortex.

Normal operations of the neocortex depend critically on several types of inhibitory interneurons, but the specific function of each type is unknown. One possibility is that interneurons are differentially engaged by patterns of activity that vary in frequency and timing. To explore this, we studied the strength and short-term dynamics of chemical synapses interconnecting local excitatory neurons (regular-spiking, or RS, cells) with two types of inhibitory interneurons: fast-spiking (FS) cells, and low-threshold spiking (LTS) cells of layer 4 in the rat barrel cortex. We also tested two other pathways onto the interneurons: thalamocortical connections and recurrent collaterals from corticothalamic projection neurons of layer 6. The excitatory and inhibitory synapses interconnecting RS cells and FS cells were highly reliable in response to single stimuli and displayed strong short-term depression. In contrast, excitatory and inhibitory synapses interconnecting the RS and LTS cells were less reliable when initially activated. Excitatory synapses from RS cells onto LTS cells showed dramatic short-term facilitation, whereas inhibitory synapses made by LTS cells onto RS cells facilitated modestly or slightly depressed. Thalamocortical inputs strongly excited both RS and FS cells but rarely and only weakly contacted LTS cells. Both types of interneurons were strongly excited by facilitating synapses from axon collaterals of corticothalamic neurons. We conclude that there are two parallel but dynamically distinct systems of synaptic inhibition in layer 4 of neocortex, each defined by its intrinsic spiking properties, the short-term plasticity of its chemical synapses, and (as shown previously) an exclusive set of electrical synapses. Because of their unique dynamic properties, each inhibitory network will be recruited by different temporal patterns of cortical activity.

Action Potentials↗
PubMed2005

Cooperative dynamics in a network of stochastic elements with delayed feedback.

Networks of globally coupled, noise-activated, bistable elements with connection time delays are considered. The dynamics of these systems is studied numerically using a Langevin description and analytically using (1) a Gaussian approximation as well as (2) a dichotomous model. The system demonstrates ordering phase transitions and multistability. That is, for a strong enough feedback it exhibits nontrivial stationary states and oscillatory states whose frequencies depend only on the mean of the time delay distribution function. Other observed dynamical phenomena include coherence resonance and, in the case of nonuniform coupling strengths, amplitude death and chaos. Furthermore, an increase of the stability of the trivial equilibrium with increasing nonuniformity of the time delays is observed.

Journal Article↗
PubMed2005

Neural network-based adaptive dynamic surface control for a class of uncertain nonlinear systems in strict-feedback form.

The dynamic surface control (DSC) technique was developed recently by Swaroop et al. This technique simplified the backstepping design for the control of nonlinear systems in strict-feedback form by overcoming the problem of "explosion of complexity." It was later extended to adaptive backstepping design for nonlinear systems with linearly parameterized uncertainty. In this paper, by incorporating this design technique into a neural network based adaptive control design framework, we have developed a backstepping based control design for a class of nonlinear systems in strict-feedback form with arbitrary uncertainty. Our development is able to eliminate the problem of "explosion of complexity" inherent in the existing method. In addition, a stability analysis is given which shows that our control law can guarantee the uniformly ultimate boundedness of the solution of the closed-loop system, and make the tracking error arbitrarily small.

Algorithms↗
PubMed2003

Dynamic modeling of genetic networks using genetic algorithm and S-system.

MOTIVATION: The modeling of system dynamics of genetic networks, metabolic networks or signal transduction cascades from time-course data is formulated as a reverse-problem. Previous studies focused on the estimation of only network structures, and they were ineffective in inferring a network structure with feedback loops. We previously proposed a method to predict not only the network structure but also its dynamics using a Genetic Algorithm (GA) and an S-system formalism. However, it could predict only a small number of parameters and could rarely obtain essential structures. In this work, we propose a unified extension of the basic method. Notable improvements are as follows: (1) an additional term in its evaluation function that aims at eliminating futile parameters; (2) a crossover method called Simplex Crossover (SPX) to improve its optimization ability; and (3) a gradual optimization strategy to increase the number of predictable parameters. RESULTS: The proposed method is implemented as a C program called PEACE1 (Predictor by Evolutionary Algorithms and Canonical Equations 1). Its performance was compared with the basic method. The comparison showed that: (1) the convergence rate increased about 5-fold; (2) the optimization speed was raised about 1.5-fold; and (3) the number of predictable parameters was increased about 5-fold. Moreover, we successfully inferred the dynamics of a small genetic network constructed with 60 parameters for 5 network variables and feedback loops using only time-course data of gene expression.

Algorithms↗
PubMed2002

Spurious dynamics in somatosensory cortex.

Cortical networks are dynamical systems whose task is to process information. However, in addition to 'intended' dynamical behaviors, the sheer complexity of a cortical network's structure-regardless of its precise details-should generate additional 'unintended' dynamical behaviors. Dynamics observed in cortical network models and in the somatosensory cortex suggest that such spurious dynamical behaviors are likely to be pervasive but relatively simple, contributing to-rather than dominating-a network's response to stimuli. Spurious dynamics may be responsible for a variety of experimentally observed intriguing features of cortical dynamics. Because of their distributed origins and emergent nature, such dynamical features, while clearly identifiable, will resist attempts at identifying specific mechanisms to explain them. We describe some of the spurious dynamical phenomena associated with somatosensory cortical response to brushing stimulation, to illustrate how spurious dynamics can affect neurons' functional properties, cortical stimulus representation and, ultimately, perception.

Algorithms↗
PubMed2002

Dynamic cortical and subcortical networks in learning and delayed recall of timed motor sequences.

We used positron emission tomography to examine learning and retention of timed motor sequences. Subjects were scanned during learning (LRN) and baseline (ISO) on 3 d: day 1, after 5 d of practice (day 5) and after a 4 week delay (recall). Blood flow was compared across days of learning and between the LRN and ISO conditions. Overall, significant changes in activity were seen across days for the LRN condition, but not the ISO baseline. Day 1 results revealed extensive activation in the cerebellar cortex, particularly lobules III/IV and VI. Day 5 results showed increased activity in the basal ganglia (BG) and frontal lobe, with no significant cerebellar activity. At recall, significantly greater activity was seen in M1, premotor, and parietal cortex. Blood flow in the cerebellum decreased significantly between day 1 and recall. These results reveal a dynamic network of motor structures that are differentially active during different phases of learning and delayed recall. For the first time our findings show that recall of motor sequences in humans is mediated by a predominantly cortical network. Based on these results, we suggest that during early learning cerebellar mechanisms are involved in adjusting movement kinematics according to sensory input to produce accurate motor output. Thereafter, the cerebellar mechanisms required for early learning are no longer called into play. During late learning, the BG may be involved in automatization. At delayed recall, movement parameters appear to be encoded in a distributed representation mediated by M1, premotor, and parietal cortex.

Adult↗
PubMed2005

Cortactin phosphorylation as a switch for actin cytoskeletal network and cell dynamics control.

Cortactin is an important molecular scaffold for actin assembly and organization. Novel mechanistic functions of cortactin have emerged with more interacting partners identified, revealing its multifaceted roles in regulating actin cytoskeletal networks that are necessary for endocytosis, cell migration and invasion, adhesion, synaptic organization and cell morphogenesis. These processes are mediated by its multi-domains binding to F-actin and Arp2/3 complex and various SH3 targets. Furthermore, its role in actin remodeling is subjected to regulation by tyrosine and serine/threonine kinases. Elucidating the mechanisms underlying cortactin phosphorylation and its functional consequences would provide new insights to various aspects of cell dynamics control.

Actins↗
PubMed2004

Investigating the dynamic behavior of biochemical networks using model families.

MOTIVATION: Supporting the evolutionary modeling process of dynamic biochemical networks based on sampled in vivo data requires more than just simulation. In the course of the modeling process, the modeler is typically concerned not only with a single model but also with sequences, alternatives and structural variants of models. Powerful automatic methods are then required to assist the modeler in the organization and the evaluation of alternative models. Moreover, the structure and peculiarities of the data require dedicated tool support. SUMMARY: To support all stages of an evolutionary modeling process, a new general formalism for the combinatorial specification of large model families is introduced. It allows for automatic navigation in the space of models and excludes biologically meaningless models on the basis of elementary flux mode analysis. An incremental usage of the measured data is supported by using splined data instead of state variables. With MMT2, a versatile tool has been developed as a computational engine intended to be built into a tool chain. Using automatic code generation, automatic differentiation for sensitivity analysis and grid computing technology, a high performance computing environment is achieved. MMT2 supplies XML model specification and several software interfaces. The performance of MMT2 is illustrated by several examples from ongoing research projects. AVAILABILITY: http://www.simtec.mb.uni-siegen.de/ CONTACT: wiechert@simtec.mb.uni-siegen.de.

Algorithms↗
PubMed1993

A Cayley tree immune network model with antibody dynamics.

A Cayley tree model of idiotypic networks that includes both B cell and antibody dynamics is formulated and analysed. As in models with B cells only, localized states exist in the network with limited numbers of activated clones surrounded by virgin or near-virgin clones. The existence and stability of these localized network states are explored as a function of model parameters. As in previous models that have included antibody, the stability of immune and tolerant localized states are shown to depend on the ratio of antibody to B cell lifetimes as well as the rate of antibody complex removal. As model parameters are varied, localized steady-states can break down via two routes: dynamically, into chaotic attractors, or structurally into percolation attractors. For a given set of parameters percolation and chaotic attractors can coexist with localized attractors, and thus there do not exist clear cut boundaries in parameter space that separate regions of localized attractors from regions of percolation and chaotic attractors. Stable limit cycles, which are frequent in the two-clone antibody B cell (AB) model, are only observed in highly connected networks. Also found in highly connected networks are localized chaotic attractors. As in experiments by Lundkvist et al. (1989. Proc. natn. Acad. Sci. U.S.A. 86, 5074-5078), injection of Ab1 antibodies into a system operating in the chaotic regime can cause a cessation of fluctuations of Ab1 and Ab2 antibodies, a phenomenon already observed in the two-clone AB model. Interestingly, chaotic fluctuations continue at higher levels of the tree, a phenomenon observed by Lundkvist et al. but not accounted for previously.

Animals↗
PubMed2002

Fast convergence of spike sequences to periodic patterns in recurrent networks.

The dynamical attractors are thought to underlie many biological functions of recurrent neural networks. Here we show that stable periodic spike sequences with precise timings are the attractors of the spiking dynamics of recurrent neural networks with global inhibition. Almost all spike sequences converge within a finite number of transient spikes to these attractors. The convergence is fast, especially when the global inhibition is strong. These results support the possibility that precise spatiotemporal sequences of spikes are useful for information encoding and processing in biological neural networks.

Action Potentials↗
PubMed2026

Sanguinarine as a multi-target therapeutic candidate for laryngeal cancer: insights from network pharmacology, molecular dynamics and in vitro validation.

OBJECTIVE: To identify the core targets and elucidate the potential molecular mechanisms of sanguinarine (SA) against laryngeal squamous cell carcinoma (LSCC), and to validate its antitumor effects in vitro. METHODS: Potential targets of SA were predicted using SwissTargetPrediction, TargetNet, and SuperPred and intersected with LSCC-related targets obtained from the GeneCards, OMIM, and DISEASES databases. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were performed. A protein-protein interaction (PPI) network was constructed using the STRING database (combined score > 0.900), and topological parameters including degree centrality (DC), betweenness centrality (BC), closeness centrality (CC), eigenvector centrality (EC), and local average connectivity (LAC) were calculated in Cytoscape to identify core genes based on median thresholds. Molecular docking and 100-ns molecular dynamics (MD) simulations were conducted for epidermal growth factor receptor (EGFR), Phosphatidylinositide-3-kinase catalytic subunit alpha (PIK3CA), phosphatidylinositol-4,5-biphosphate 3-kinase catalytic subunit β (PIK3CB), phosphatidylinositol-4,5-bisphosphate 3-kinase catalytic subunit delta (PIK3CD), and Non-Receptor Tyrosine Kinase (SRC). The effects of SA on LSCC were evaluated using CCK-8, colony formation, Transwell migration, and wound-healing assays in TU177 cells and TU212. RESULTS: A total of 213 common targets were identified, which were significantly enriched in PI3K-Akt signaling, EGFR tyrosine kinase inhibitor resistance, and adhesion- and migration-related pathways. The PPI network comprised 259 nodes and 259 edges, from which five core genes-PIK3CA, PIK3CB, PIK3CD, EGFR, and SRC-were identified. Molecular docking revealed strong binding affinities between SA and the PI3K family proteins (- 9.79 to - 10.96 kcal/mol), as well as EGFR (- 8.58 kcal/mol) and SRC (- 6.77 kcal/mol). MD simulations indicated greater stability of SA complexes with EGFR and PI3K family members compared with SRC. In vitro assays demonstrated that SA significantly inhibited TU177 cell and TU212 cell proliferation, colony formation, and migration. CONCLUSION: SA may exert anti-laryngeal cancer effects through synergistic multi-target inhibition centered on the EGFR/SRC/PI3K signaling axis, highlighting its potential as a promising therapeutic candidate for LSCC.

Humans↗
PubMed1990

Reticular activation and the dynamics of neuronal networks.

It is postulated that during arousal the cortical system is driven by a spatially and temporally noisy signal arising from non-specific reticulo-cortical pathways. An elementary unit of cortical neuroanatomy is assumed, which permits non-linear dynamics to be represented by stochastic linear equations. Under these assumptions the resonant modes of the system of cortical dendrites approach thermodynamic equilibrium. Specific sensory signals perturb the dendritic system about equilibrium, generate low frequency, linear, non-dispersive waves corresponding to the EEG, which in turn regulate action potential sequences, and instantiate internal inputs to the dendritic field. A large and distributed memory capacity in axo-synaptic couplings, resistance to interference between functionally separate logical operations, and a very large next-state function set emerge as properties of the network. The model is able to explain the close association of the EEG with cognition, the channel of low capacity corresponding to the field of immediate attention, the low overall correlation of action potentials with EEG, and specificity of action potentials in some neurons during particular cognitive activity. Predictions made from hypothesis include features of thermal equilibrium in EEG (determinable by autoregression) and expectation that the cortical evoked response can be accounted for as the response to a sensory impulse of specific time characteristics.

Brain↗
PubMed1999

Dynamics of neural networks with a central element.

A neural network is considered which is designed as a system of phase oscillators and contains a central oscillator that interacts with a number of peripheral oscillators. Analytical and simulation methods are used to study the dynamics of the system that is conditioned by the interaction parameters and natural frequencies of the oscillators. The boundaries of parameter regions are found that correspond to the synchronization of the whole network or to partial synchronization between the central oscillator and a group of peripheral oscillators. For a system with two peripheral oscillators the bifurcation analysis is applied to describe the changes of synchronization modes. The implications of the results for attention modeling are discussed.

Journal Article↗
PubMed2002

Complex dynamics and the structure of small neural networks.

The discrete-time dynamics of small neural networks is studied empirically, with emphasis laid on non-trivial bifurcation scenarios. For particular two- and three-neuron networks interesting dynamical properties like periodic, quasi-periodic and chaotic attractors are observed, many of them co-existing for one and the same set of parameters. An appropriate equivalence class of networks is defined, describing them as parametrized dynamical systems with identical dynamical capacities. Combined symmetries in phase space and parameter space are shown to generate different representatives of such a class. Moreover, conditions on the connectivity structure are suggested, which guarantee the existence of complex dynamics for a considered equivalence class of network configurations.

Algorithms↗