Large time step discrete-time modeling of sharp wave activity in hippocampal area CA3.

Reduced models of neuronal spiking activity simulated with a fixed integration time are frequently used in studies of spatio-temporal dynamics of neurobiological networks. The choice of fixed time step integration provides computational simplicity and efficiency, especially in cases dealing with large number of neurons and synapses operating at a different level of activity across the population at any given time. A network model tuned to generate a particular type of oscillations or wave patterns is sensitive to the intrinsic properties of neurons and synapses and, therefore, commonly susceptible to changes the time step of integration. In this study, we analyzed a model of sharp-wave activity in the network of hippocampal area CA3, to examine how an increase of the integration time step affects network behavior and to propose adjustments of intrinsic properties neurons and synapses that help minimize or remove the damage caused by the time step increase.

Emergence of antiphase bursting in two populations of randomly spiking elements.

Animal locomotion activity relies on the generation and control of coordinated periodic actions in a central pattern generator (CPG). A core element of many CPGs responsible for the rhythm generation is a pair of reciprocally coupled neuron populations. Recent interest in the development of highly reduced models of CPG networks is motivated by utilization of CPG models in applications for biomimetic robotics. This paper considers the use of a reduced model in the form of a discrete time system to study the emergence of antiphase bursting activity in two reciprocally coupled populations evoked by the postinhibitory rebound effect.

Dynamics of epileptiform activity in mouse hippocampal slices.

Increase of the extracellular K( + ) concentration mediates seizure-like synchronized activities in vitro and was proposed to be one of the main factors underlying epileptogenesis in some types of seizures in vivo. While underlying biophysical mechanisms clearly involve cell depolarization and overall increase in excitability, it remains unknown what qualitative changes of the spatio-temporal network dynamics occur after extracellular K( + ) increase. In this study, we used multi-electrode recordings from mouse hippocampal slices to explore changes of the network activity during progressive increase of the extracellular K( + ) concentration. Our analysis revealed complex spatio-temporal evolution of epileptiform activity and demonstrated a sequence of state transitions from relatively simple network bursts into complex bursting, with multiple synchronized events within each burst. We describe these transitions as qualitative changes of the state attractors, constructed from experimental data, mediated by elevation of extracellular K( + ) concentration.

Oscillations and synchrony in large-scale cortical network models.

Intrinsic neuronal and circuit properties control the responses of large ensembles of neurons by creating spatiotemporal patterns of activity that are used for sensory processing, memory formation, and other cognitive tasks. The modeling of such systems requires computationally efficient single-neuron models capable of displaying realistic response properties. We developed a set of reduced models based on difference equations (map-based models) to simulate the intrinsic dynamics of biological neurons. These phenomenological models were designed to capture the main response properties of specific types of neurons while ensuring realistic model behavior across a sufficient dynamic range of inputs. This approach allows for fast simulations and efficient parameter space analysis of networks containing hundreds of thousands of neurons of different types using a conventional workstation. Drawing on results obtained using large-scale networks of map-based neurons, we discuss spatiotemporal cortical network dynamics as a function of parameters that affect synaptic interactions and intrinsic states of the neurons.

Role of network dynamics in shaping spike timing reliability.

We study the reliability of cortical neuron responses to periodically modulated synaptic stimuli. Simple map-based models of two different types of cortical neurons are constructed to replicate the intrinsic resonances of reliability found in experimental data and to explore the effects of those resonance properties on collective behavior in a cortical network model containing excitatory and inhibitory cells. We show that network interactions can enhance the frequency range of reliable responses and that the latter can be controlled by the strength of synaptic connections. The underlying dynamical mechanisms of reliability enhancement are discussed.

Detectability of nondifferentiable generalized synchrony.

Generalized synchronization of chaos is a type of cooperative behavior in directionally coupled oscillators that is characterized by existence of stable and persistent functional dependence of response trajectories from the chaotic trajectory of driving oscillator. In many practical cases this function is nondifferentiable and has a very complex shape. The generalized synchrony in such cases seems to be undetectable, and only the cases in which a differentiable synchronization function exists are considered to make sense in practice. We show that this viewpoint is not always correct and the nondifferentiable generalized synchrony can be revealed in many practical cases. Conditions for detection of generalized synchrony are derived analytically, and illustrated numerically with a simple example of nondifferentiable generalized synchronization.

Modeling of spiking-bursting neural behavior using two-dimensional map.

A simple model that replicates the dynamics of spiking and spiking-bursting activity of real biological neurons is proposed. The model is a two-dimensional map that contains one fast and one slow variable. The mechanisms behind generation of spikes, bursts of spikes, and restructuring of the map behavior are explained using phase portrait analysis. The dynamics of two coupled maps that model the behavior of two electrically coupled neurons is discussed. Synchronization regimes for spiking and bursting activities of these maps are studied as a function of coupling strength. It is demonstrated that the results of this model are in agreement with the synchronization of chaotic spiking-bursting behavior experimentally found in real biological neurons.

Synchronization of chaotic systems: Transverse stability of trajectories in invariant manifolds.

We examine synchronization of identical chaotic systems coupled in a drive/response manner. A rigorous criterion is presented which, if satisfied, guarantees that synchronization to the driving trajectory is linearly stable to perturbations. An easy to use approximate criterion for estimating linear stability is also presented. One major advantage of these criteria is that, for simple systems, many of the calculations needed to implement them can be performed analytically. Geometrical interpretations of the criterion are discussed, as well as how they may be used to investigate synchronization between mutual coupled systems and the stability of invariant manifolds within a dynamical system. Finally, the relationship between our criterion and results from control theory are discussed. Analytical and numerical results from tests of these criteria on four different dynamical systems are presented. (c) 1997 American Institute of Physics.

Images of synchronized chaos: Experiments with circuits.

Synchronization of oscillations underlies organized dynamical behavior of many physical, biological and other systems. Recent studies of the dynamics of coupled systems with complex behavior indicate that synchronization can occur not only in case of periodic oscillations, but also in regimes of chaotic oscillations. Using experimental observations of chaotic oscillations in coupled nonlinear circuits we discuss a few forms of cooperative behavior that are related to the regimes of synchronized chaos. This paper is prepared under the request of the editors of the special focus issue of Chaos and contains the materials for the lecture at the International School in Nonlinear Science, "Nonlinear Waves: Synchronization and Patterns," Nizhniy Novgorod, Russia, 1995. The main goal of the paper is to outline the collection of examples that illustrate the state of the art of chaos synchronization. (c) 1996 American Institute of Physics.