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At least 163 records · Page 9Linked to original sources
PubMed1999

Structure of EVH1, a novel proline-rich ligand-binding module involved in cytoskeletal dynamics and neural function.

The Ena-VASP homology (EVH1) domain is a protein interaction module found in several proteins that are involved in transducing migratory and morphological signals into cytoskeletal reorganization. EVH1 specifically recognizes proline-rich sequences in its binding partners and directs the localization and formation of multicomponent assemblies involved in actin-based motile processes and neural development. The structure of the complex between an EVH1 domain and the target peptide sequence EFPPPPT identifies the interactions responsible for recognition and distinguishes it from other proline-rich binding modules, including SH3 and WW domains. Surprisingly, the EVH1 domain has structural similarity to pleckstrin homology (PH), phosphotyrosine-binding (PTB) and ran-binding (RanBD) domains.

Amino Acid Sequence↗
PubMed2001

Dynamics of neural networks: a proposed mechanism to account for changes in clinical symptomatology through time in patients with psychotic diseases.

The classical Kraepelinean dichotomy between manic depressive insanity and the schizophrenias has been recently challenged from clinical and neurobiological quarters. It is not so infrequent to see patients shift from a manic to a schizophrenic symptomatology and vice versa. This paper proposes neurobiological mechanisms as to how these changes may occur, based on recent data on the functioning of neural networks at different modes.

Humans↗
PubMed2003

Understanding neural system dynamics through task modulation and measurement of functional MRI amplitude, latency, and width.

Estimates of hemodynamic amplitude, delay, and width were combined to investigate system dynamics involved in lexical decision making. Subjects performed a lexical decision task using word and nonword stimuli rotated 0 degrees, 60 degrees, or 120 degrees. Averaged hemodynamic responses to repeated stimulation were fit to a Gamma-variate function convolved with a heavyside function of varying onset and duration to estimate each voxel's activation delay and width. Consistent with prolonged reaction times for the rotated stimuli and nonwords, the motor cortex showed delayed hemodynamic onset for both conditions. Language areas such as the lingual gyrus, middle temporal gyrus, fusiform gyrus, and precuneus all showed delayed hemodynamic onsets to rotated stimuli but not to nonword stimuli. The inferior frontal gyrus showed both increased onset latency for rotated stimuli and a wider hemodynamic response to nonwords, consistent with prolonged processing in this area during the lexical decision task. Phonological processing areas such as superior temporal and angular gyrus showed no delay or width difference for rotated stimuli. These results suggest that phonological routes but not semantic routes to the lexicon can proceed regardless of stimulus orientation. This study demonstrates the utility of estimating hemodynamic delay and width in addition to amplitude allowing for more quantitative measures of brain function such as mental chronometry.

Adult↗
PubMed2001

Modeling genetic regulatory dynamics in neural development.

We model genetic regulatory networks in the framework of continuous-time recurrent networks. The network parameters are determined from gene expression level time series data using genetic algorithms. We have applied the method to expression data from the development of rat central nervous system, where the active genes cluster into four groups, within which the temporal expression patterns are similar. The data permit us to identify approximately the interactions between these groups of genes. We find that generally a single time series is of limited value in determining the interactions in the network, but multiple time series collected in related tissues or under treatment with different drugs can fix their values much more precisely.

Algorithms↗
PubMed2002

Information space dynamics for neural networks.

We propose a coupled map lattice defined on a hypercube in M dimensions, the information space, to model memory retrieval by a neural network. We consider that both neuronal activity and the spiking phase may carry information. In this model the state of the network at a given time t is completely determined by a function y(sigma-->,t) of the bit strings sigma-->=(sigma1,sigma2,...,sigmaM), where sigma(i)=+/-1 with i=1,2, ...,M, that gives the intensity with which the information sigma--> is being expressed by the network. As an example, we consider logistic maps, coupled in the information space, to describe the evolution of the intensity function y(sigma-->,t). We propose an interpretation of the maps in terms of the physiological state of the neurons and the coupling between them, obtain Hebb-like learning rules, show that the model works as an associative memory, numerically investigate the capacity of the network and the size of the basins of attraction, and estimate finite size effects. We finally show that the model, when exposed to sequences of uncorrelated stimuli, shows recency and latency effects that depend on the noise level, delay time of measurement, and stimulus intensity.

Journal Article↗
PubMed1987

Frequency modulation dynamics in neural networks.

The VCON model described here shares certain qualitative features of stimulus-response characteristics with the data in FIGURE 1 and TABLE 1. At the same time, it provides an uncomplicated methodology for modelling neural networks that emphasizes their frequency aspects. In particular, models based on VCONs are amenable to the rotation vector method, which can be used to uncover stable synchronization of firing within a network. This is described in the appendix, where it is shown that the stable firing patterns within the network correspond to minima of an associated (local) energy function. We have seen here how a model of a simple CPG for breathing can be constructed and analyzed. Similar models for rhythm splitting of small mammal activity cycles, sound location networks, and motility in the gastrointestinal tract have been constructed, and large networks of VCONs have been shown to have stable spatial patterns of synchronization.

Action Potentials↗
PubMed2004

On the phase reduction and response dynamics of neural oscillator populations.

We undertake a probabilistic analysis of the response of repetitively firing neural populations to simple pulselike stimuli. Recalling and extending results from the literature, we compute phase response curves (PRCs) valid near bifurcations to periodic firing for Hindmarsh-Rose, Hodgkin-Huxley, FitzHugh-Nagumo, and Morris-Lecar models, encompassing the four generic (codimension one) bifurcations. Phase density equations are then used to analyze the role of the bifurcation, and the resulting PRC, in responses to stimuli. In particular, we explore the interplay among stimulus duration, baseline firing frequency, and population-level response patterns. We interpret the results in terms of the signal processing measure of gain and discuss further applications and experimentally testable predictions.

Action Potentials↗
PubMed1985

Electrochemical stimulation of plasma LH and hypothalamic norepinephrine concentrations at short- and long-term intervals after hypothalamic knife cuts.

Norepinephrine (NE) concentrations in several diencephalic locations were studied in female rats in conjunction with luteinizing hormone (LH) release after medial preoptic area (MPOA) stimulation at short (7 days) and longer time intervals after surgical interruption of anterior or anterolateral neural connections of mediobasal hypothalamus (MBH). Concentrations of diencephalic NE were altered in two general ways after brain surgery: (1) transient early postoperative increases in some regions which appeared unrelated to the type of surgery performed; and (2) other specific decreases in NE concentration which were related to the types of surgery performed and whether a particular ascending noradrenergic pathway was interrupted. At 180 days after surgery, these two types of change in NE concentrations were no longer present. Maximum increases in plasma LH concentrations observed after electrochemical stimulation of the MPOA at either 7 or 180 days after MBH deafferentation also varied according to: (1) the postoperative interval studied; and (2) the location of pathway interruption. Interruption of anterior MBH pathways showed only a transient (7 day interval) reduction in LH release after MPOA stimulation, whereas when both lateral and anterior pathways were severed, there was a more nearly permanent (180 day interval) disruption of LH release after stimulation. The results of these studies support the contention that anterolateral MBH neural connections may constitute a dynamic neural substrate contributing to a gradual improvement in neuroendocrine function observed after early surgical disconnections.

Animals↗
PubMed2005

Some generalized sufficient convergence criteria for nonlinear continuous neural networks.

A reason for applying the direct method of Lyapunov to artificial neural networks (ANNs) is to design dynamical neural networks so that they exhibit global asymptotic stability. Lyapunov functions that frequently appear in the ANN literature include the quadratic function, the Persidskii function, and the Luré-Postnikov function. This contribution revisits the quadratic function and shows that via Krasovskii-like stability criteria, it is possible to have a very simple and systematic procedure to obtain not only new and generalized results but also well-known sufficient conditions for convergence established recently by non-Lyapunov methods, such as the matrix measure and nonlinear measure.

Algorithms↗
PubMed2001

Dynamics of some neural network models with delay.

The dynamics of the neuronic model described by the one-dimensional delay functional differential equation are studied in this paper. We give a strict and detailed analysis of dynamical characteristic of this model by the Lyapunov functional approach and Hopf bifurcation proposition. Furthermore, numerical simulations, as well as Lyapunov exponents, are presented to support our conjectures about the appearance of complex dynamics such as chaos. We also investigate the dynamics of the neural network model described by the n-dimensional delay functional differential equation with a symmetrical weight matrix, and corresponding simulation results are included as concrete examples.

Artificial Intelligence↗
PubMed2004

Dynamic analysis of neural encoding by point process adaptive filtering.

Neural receptive fields are dynamic in that with experience, neurons change their spiking responses to relevant stimuli. To understand how neural systems adapt their representations of biological information, analyses of receptive field plasticity from experimental measurements are crucial. Adaptive signal processing, the well-established engineering discipline for characterizing the temporal evolution of system parameters, suggests a framework for studying the plasticity of receptive fields. We use the Bayes' rule Chapman-Kolmogorov paradigm with a linear state equation and point process observation models to derive adaptive filters appropriate for estimation from neural spike trains. We derive point process filter analogues of the Kalman filter, recursive least squares, and steepest-descent algorithms and describe the properties of these new filters. We illustrate our algorithms in two simulated data examples. The first is a study of slow and rapid evolution of spatial receptive fields in hippocampal neurons. The second is an adaptive decoding study in which a signal is decoded from ensemble neural spiking activity as the receptive fields of the neurons in the ensemble evolve. Our results provide a paradigm for adaptive estimation for point process observations and suggest a practical approach for constructing filtering algorithms to track neural receptive field dynamics on a millisecond timescale.

Action Potentials↗
PubMed2005

Beyond bistability: biophysics and temporal dynamics of working memory.

Working memory has often been modeled and conceptualized as a kind of binary (bistable) memory switch, where stimuli turn on plateau-like persistent activity in subsets of cells, in line with many in vivo electrophysiological reports. A potentially related form of bistability, termed up- and down-states, has been studied with regard to its synaptic and ionic basis in vivo and in reduced cortical preparations. Also single cell mechanisms for producing bistability have been proposed and investigated in brain slices and computationally. Recently, however, it has been emphasized that clear plateau-like bistable activity is rather rare during working memory tasks, and that neurons exhibit a multitude of different temporally unfolding activity profiles and temporal structure within their spiking dynamics. Hence, working memory seems to be a highly dynamical neural process with yet unknown mappings from dynamical to computational properties. Empirical findings on ramping activity profiles and temporal structure will be reviewed, as well as neural models that attempt to account for it and its computational significance. Furthermore, recent in vivo, neural culture, and in vitro preparations will be discussed that offer new possibilities for studying the biophysical mechanisms underlying computational processes during working memory. These preparations have revealed additional evidence for temporal structure and spatio-temporally organized attractor states in cortical networks, as well as for specific computational properties that may characterize synaptic processing during high-activity states as during working memory. Together such findings may lay the foundations for highly dynamical theories of working memory based on biophysical principles.

Action Potentials↗