RESUMEN

We present a mean-field formalism able to predict the collective dynamics of large networks of conductance-based interacting spiking neurons. We apply this formalism to several neuronal models, from the simplest Adaptive Exponential Integrate-and-Fire model to the more complex Hodgkin-Huxley and Morris-Lecar models. We show that the resulting mean-field models are capable of predicting the correct spontaneous activity of both excitatory and inhibitory neurons in asynchronous irregular regimes, typical of cortical dynamics. Moreover, it is possible to quantitatively predict the population response to external stimuli in the form of external spike trains. This mean-field formalism therefore provides a paradigm to bridge the scale between population dynamics and the microscopic complexity of the individual cells physiology.NEW & NOTEWORTHY Population models are a powerful mathematical tool to study the dynamics of neuronal networks and to simulate the brain at macroscopic scales. We present a mean-field model capable of quantitatively predicting the temporal dynamics of a network of complex spiking neuronal models, from Integrate-and-Fire to Hodgkin-Huxley, thus linking population models to neurons electrophysiology. This opens a perspective on generating biologically realistic mean-field models from electrophysiological recordings.

Asunto(s)

Fenómenos Electrofisiológicos/fisiología , Modelos Neurológicos , Red Nerviosa/fisiología , Redes Neurales de la Computación , Neuronas/fisiología , Animales , Humanos

RESUMEN

KEY POINTS: We recreated in vitro the fluctuation-driven regime observed at the soma during asynchronous network activity in vivo and we studied the firing rate response as a function of the properties of the membrane potential fluctuations. We provide a simple analytical template that captures the firing response of both pyramidal neurons and various theoretical models. We found a strong heterogeneity in the firing rate response of layer V pyramidal neurons: in particular, individual neurons differ not only in their mean excitability level, but also in their sensitivity to fluctuations. Theoretical modelling suggest that this observed heterogeneity might arise from various expression levels of the following biophysical properties: sodium inactivation, density of sodium channels and spike frequency adaptation. ABSTRACT: Characterizing the input-output properties of neocortical neurons is of crucial importance for understanding the properties emerging at the network level. In the regime of low-rate irregular firing (such as in the awake state), determining those properties for neocortical cells remains, however, both experimentally and theoretically challenging. Here, we studied this problem using a combination of theoretical modelling and in vitro experiments. We first identified, theoretically, three somatic variables that describe the dynamical state at the soma in this fluctuation-driven regime: the mean, standard deviation and time constant of the membrane potential fluctuations. Next, we characterized the firing rate response of individual layer V pyramidal cells in this three-dimensional space by means of perforated-patch recordings and dynamic clamp in the visual cortex of juvenile mice in vitro. We found that individual neurons strongly differ not only in terms of their excitability, but also, and unexpectedly, in their sensitivities to fluctuations. Finally, using theoretical modelling, we attempted to reproduce these results. The model predicts that heterogeneous levels of biophysical properties such as sodium inactivation, sharpness of sodium activation and spike frequency adaptation account for the observed diversity of firing rate responses. Because the firing rate response will determine population rate dynamics during asynchronous neocortical activity, our results show that cortical populations are functionally strongly inhomogeneous in young mouse visual cortex, which should have important consequences on the strategies of cortical computation at early stages of sensory processing.

Asunto(s)

Modelos Neurológicos , Células Piramidales/fisiología , Corteza Visual/fisiología , Animales , Femenino , Técnicas In Vitro , Masculino , Potenciales de la Membrana , Ratones , Técnicas de Placa-Clamp , Canales de Sodio/fisiología

RESUMEN

OBJECTIVE: Electrophysiological recordings of single neurons in brain tissues are very common in neuroscience. Glass microelectrodes filled with an electrolyte are used to impale the cell membrane in order to record the membrane potential or to inject current. Their high resistance induces a high voltage drop when passing current and it is essential to correct the voltage measurements. In particular, for voltage clamping, the traditional alternatives are two-electrode voltage-clamp technique or discontinuous single electrode voltage-clamp (dSEVC). Nevertheless, it is generally difficult to impale two electrodes in a same neuron and the switching frequency is limited to low frequencies in the case of dSEVC. We present a novel fully computer-implemented alternative to perform continuous voltage-clamp recordings with a single sharp-electrode. APPROACH: To reach such voltage-clamp recordings, we combine an active electrode compensation algorithm (AEC) with a digital controller (AECVC). MAIN RESULTS: We applied two types of control-systems: a linear controller (proportional plus integrative controller) and a model-based controller (optimal control). We compared the performance of the two methods to dSEVC using a dynamic model cell and experiments in brain slices. SIGNIFICANCE: The AECVC method provides an entirely digital method to perform continuous recording and smooth switching between voltage-clamp, current clamp or dynamic-clamp configurations without introducing artifacts.

Asunto(s)

Potenciales de Acción/fisiología , Potenciales de la Membrana/fisiología , Microelectrodos , Neuronas/fisiología , Lóbulo Occipital/fisiología , Técnicas de Placa-Clamp/instrumentación , Técnicas de Placa-Clamp/métodos , Animales , Conductividad Eléctrica , Diseño de Equipo , Análisis de Falla de Equipo , Retroalimentación , Ratas

RESUMEN

Many hardware-based solutions now exist for the simulation of bio-like neural networks. Less conventional than software-based systems, these types of simulators generally combine digital and analog forms of computation. In this paper we present a mixed hardware-software platform, specifically designed for the simulation of spiking neural networks, using conductance-based models of neurons and synaptic connections with dynamic adaptation rules (Spike-Timing-Dependent Plasticity). The neurons and networks are configurable, and are computed in 'biological real time' by which we mean that the difference between simulated time and simulation time is guaranteed lower than 50 mus. After presenting the issues and context involved in the design and use of hardware-based spiking neural networks, we describe the analog neuromimetic integrated circuits which form the core of the platform. We then explain the organization and computation principles of the modules within the platform, and present experimental results which validate the system. Designed as a tool for computational neuroscience, the platform is exploited in collaborative research projects together with neurobiology and computer science partners.

Asunto(s)

Simulación por Computador , Modelos Neurológicos , Red Nerviosa , Redes Neurales de la Computación , Neuronas , Potenciales de Acción , Plasticidad Neuronal , Programas Informáticos , Sinapsis

RESUMEN

We designed a model-based analysis to predict the occurrence of population patterns in distributed spiking activity. Using a maximum entropy principle with a Markovian assumption, we obtain a model that accounts for both spatial and temporal pairwise correlations among neurons. This model is tested on data generated with a Glauber spin-glass system and is shown to correctly predict the occurrence probabilities of spatiotemporal patterns significantly better than Ising models only based on spatial correlations. This increase of predictability was also observed on experimental data recorded in parietal cortex during slow-wave sleep. This approach can also be used to generate surrogates that reproduce the spatial and temporal correlations of a given data set.

Asunto(s)

Potenciales de Acción/fisiología , Biofisica/métodos , Neuronas/fisiología , Animales , Entropía , Humanos , Cadenas de Markov , Modelos Neurológicos , Modelos Estadísticos , Modelos Teóricos , Actividad Motora , Neuronas/metabolismo , Probabilidad , Reproducibilidad de los Resultados , Sueño

RESUMEN

In awake animals, the activity of the cerebral cortex is highly complex, with neurons firing irregularly with apparent Poisson statistics. One way to characterize this complexity is to take advantage of the high interconnectivity of cerebral cortex and use intracellular recordings of cortical neurons, which contain information about the activity of thousands of other cortical neurons. Identifying the membrane potential (Vm) to a stochastic process enables the extraction of important statistical signatures of this complex synaptic activity. Typically, one estimates the total synaptic conductances (excitatory and inhibitory) but this type of estimation requires at least two Vm levels and therefore cannot be applied to single Vm traces. We propose here a method to extract excitatory and inhibitory conductances (mean and variance) from single Vm traces. This "VmT method" estimates conductance parameters using maximum likelihood criteria, under the assumption that synaptic conductances are described by gaussian stochastic processes and are integrated by a passive leaky membrane. The method is illustrated using models and is tested on guinea-pig visual cortex neurons in vitro using dynamic-clamp experiments. The VmT method holds promises for extracting conductances from single-trial measurements, which has a high potential for in vivo applications.

Asunto(s)

Conductividad Eléctrica , Potenciales de la Membrana/fisiología , Neuronas/fisiología , Sinapsis/fisiología , Animales , Fenómenos Biofísicos , Cobayas , Técnicas In Vitro , Modelos Neurológicos , Inhibición Neural/fisiología , Lóbulo Occipital/citología , Técnicas de Placa-Clamp , Procesos Estocásticos

RESUMEN

Many complex systems display self-organized critical states characterized by 1/f frequency scaling of power spectra. Global variables such as the electroencephalogram, scale as 1/f, which could be the sign of self-organized critical states in neuronal activity. By analyzing simultaneous recordings of global and neuronal activities, we confirm the 1/f scaling of global variables for selected frequency bands, but show that neuronal activity is not consistent with critical states. We propose a model of 1/f scaling which does not rely on critical states, and which is testable experimentally.

Asunto(s)

Encéfalo/fisiología , Neuronas/metabolismo , Animales , Biofisica/métodos , Encéfalo/metabolismo , Mapeo Encefálico , Gatos , Electroencefalografía/métodos , Ambiente , Humanos , Modelos Neurológicos , Modelos Estadísticos , Fenómenos Fisiológicos , Sinapsis

RESUMEN

Local field potentials (LFPs) are routinely measured experimentally in brain tissue, and exhibit strong low-pass frequency filtering properties, with high frequencies (such as action potentials) being visible only at very short distances (approximately 10 microm) from the recording electrode. Understanding this filtering is crucial to relate LFP signals with neuronal activity, but not much is known about the exact mechanisms underlying this low-pass filtering. In this paper, we investigate a possible biophysical mechanism for the low-pass filtering properties of LFPs. We investigate the propagation of electric fields and its frequency dependence close to the current source, i.e., at length scales in the order of average interneuronal distances. We take into account the presence of a high density of cellular membranes around current sources, such as glial cells. By considering them as passive cells, we show that under the influence of the electric source field, they respond by polarization. Because of the finite velocity of ionic charge movements, this polarization will not be instantaneous. Consequently, the induced electric field will be frequency-dependent, and much reduced for high frequencies. Our model establishes that this situation is analogous to an equivalent RC circuit, or better yet a system of coupled RC circuits. We present a number of numerical simulations of an induced electric field for biologically realistic values of parameters, and show the frequency filtering effect as well as the attenuation of extracellular potentials with distance. We suggest that induced electric fields in passive cells surrounding neurons are the physical origin of frequency filtering properties of LFPs. Experimentally testable predictions are provided allowing us to verify the validity of this model.

Asunto(s)

Potenciales de Acción/fisiología , Encéfalo/fisiología , Electroencefalografía/métodos , Modelos Neurológicos , Red Nerviosa/fisiología , Neuronas/fisiología , Animales , Simulación por Computador , Humanos

RESUMEN

Synaptically generated subthreshold membrane potential (Vm) fluctuations can be characterized within the framework of stochastic calculus. It is possible to obtain analytic expressions for the steady-state Vm distribution, even in the case of conductance-based synaptic currents. However, as we show here, the analytic expressions obtained may substantially deviate from numerical solutions if the stochastic membrane equations are solved exclusively based on expectation values of differentials of the stochastic variables, hence neglecting the spectral properties of the underlying stochastic processes. We suggest a simple solution that corrects these deviations, leading to extended analytic expressions of the Vm distribution valid for a parameter regime that covers several orders of magnitude around physiologically realistic values. These extended expressions should enable finer characterization of the stochasticity of synaptic currents by analyzing experimentally recorded Vm distributions and may be applicable to other classes of stochastic processes as well.

Asunto(s)

Potenciales de la Membrana/fisiología , Conducción Nerviosa/fisiología , Sinapsis/fisiología , Animales , Modelos Estadísticos , Estadística como Asunto , Procesos Estocásticos

Asunto(s)

Neuronas/fisiología , Núcleo Subtalámico/fisiología , Adaptación Fisiológica , Electrofisiología , Humanos , Núcleo Subtalámico/citología

RESUMEN

In vivo, in vitro and computational studies were used to investigate the impact of the synaptic background activity observed in neocortical neurons in vivo. We simulated background activity in vitro using two stochastic Ornstein-Uhlenbeck processes describing glutamatergic and GABAergic synaptic conductances, which were injected into a cell in real time using the dynamic clamp technique. With parameters chosen to mimic in vivo conditions, layer 5 rat prefrontal cortex cells recorded in vitro were depolarized by about 15 mV, their membrane fluctuated with a S.D. of about 4 mV, their input resistances decreased five-fold, their spontaneous firing had a high coefficient of variation and an average firing rate of about 5-10 Hz. Brief changes in the variance of the alpha-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid (AMPA) synaptic conductance fluctuations induced time-locked spiking without significantly changing the average membrane potential of the cell. These transients mimicked increases in the correlation of excitatory inputs. Background activity was highly effective in modulating the firing-rate/current curve of the cell: the variance of the simulated gamma-aminobutyric acid (GABA) and AMPA conductances individually set the input/output gain, the mean excitatory and inhibitory conductances set the working point, and the mean inhibitory conductance controlled the input resistance. An average ratio of inhibitory to excitatory mean conductances close to 4 was optimal in generating membrane potential fluctuations with high coefficients of variation. We conclude that background synaptic activity can dynamically modulate the input/output properties of individual neocortical neurons in vivo.

Asunto(s)

Modelos Neurológicos , Neuronas/fisiología , Sinapsis/fisiología , Transmisión Sináptica/fisiología , Animales , Simulación por Computador , Impedancia Eléctrica , Técnicas In Vitro , Potenciales de la Membrana/fisiología , Neocórtex/citología , Neocórtex/fisiología , Conducción Nerviosa , Inhibición Neural , Técnicas de Placa-Clamp , Probabilidad , Ratas , Ratas Sprague-Dawley , Factores de Tiempo

RESUMEN

Synaptic noise due to intense network activity can have a significant impact on the electrophysiological properties of individual neurons. This is the case for the cerebral cortex, where ongoing activity leads to strong barrages of synaptic inputs, which act as the main source of synaptic noise affecting on neuronal dynamics. Here, we characterize the subthreshold behavior of neuronal models in which synaptic noise is represented by either additive or multiplicative noise, described by Ornstein-Uhlenbeck processes. We derive and solve the Fokker-Planck equation for this system, which describes the time evolution of the probability density function for the membrane potential. We obtain an analytic expression for the membrane potential distribution at steady state and compare this expression with the subthreshold activity obtained in Hodgkin-Huxley-type models with stochastic synaptic inputs. The differences between multiplicative and additive noise models suggest that multiplicative noise is adequate to describe the high-conductance states similar to in vivo conditions. Because the steady-state membrane potential distribution is easily obtained experimentally, this approach provides a possible method to estimate the mean and variance of synaptic conductances in real neurons.

Asunto(s)

Modelos Neurológicos , Neuronas/fisiología , Membrana Celular/fisiología , Electricidad , Potenciales de la Membrana/fisiología , Umbral Sensorial/fisiología

RESUMEN

Neurons of the central nervous system display a broad spectrum of intrinsic electrophysiological properties that are absent in the traditional "integrate-and-fire" model. A network of neurons with these properties interacting through synaptic receptors with many time scales can produce complex patterns of activity that cannot be intuitively predicted. Computational methods, tightly linked to experimental data, provide insights into the dynamics of neural networks. We review this approach for the case of bursting neurons of the thalamus, with a focus on thalamic and thalamocortical slow-wave oscillations. At the single-cell level, intrinsic bursting or oscillations can be explained by interactions between calcium- and voltage-dependent channels. At the network level, the genesis of oscillations, their initiation, propagation, termination, and large-scale synchrony can be explained by interactions between neurons with a variety of intrinsic cellular properties through different types of synaptic receptors. These interactions can be altered by neuromodulators, which can dramatically shift the large-scale behavior of the network, and can also be disrupted in many ways, resulting in pathological patterns of activity, such as seizures. We suggest a coherent framework that accounts for a large body of experimental data at the ion-channel, single-cell, and network levels. This framework suggests physiological roles for the highly synchronized oscillations of slow-wave sleep.

Asunto(s)

Potenciales de Acción/fisiología , Corteza Cerebral/fisiología , Electroencefalografía , Tálamo/fisiología , Animales , Relojes Biológicos/fisiología , Corteza Cerebral/citología , Humanos , Neuronas/fisiología , Tálamo/citología

RESUMEN

In vivo recordings have shown that the discharge of cortical neurons is often highly variable and can have statistics similar to a Poisson process with a coefficient of variation around unity. To investigate the determinants of this high variability, we analyzed the spontaneous discharge of Hodgkin-Huxley type models of cortical neurons, in which in vivo-like synaptic background activity was modeled by random release events at excitatory and inhibitory synapses. By using compartmental models with active dendrites, or single compartment models with fluctuating conductances and fluctuating currents, we found that a high discharge variability was always paralleled with a high-conductance state, while some active and passive cellular properties had only a minor impact. Furthermore, a balance between excitation and inhibition was not a necessary condition for high discharge variability. We conclude that the fluctuating high-conductance state caused by the ongoing activity in the cortical network in vivo may be viewed as a natural determinant of the highly variable discharges of these neurons.

Asunto(s)

Potenciales de Acción/fisiología , Vías Aferentes/fisiología , Compartimento Celular/fisiología , Dendritas/fisiología , Neocórtex/fisiología , Células Piramidales/fisiología , Sinapsis/fisiología , Vías Aferentes/citología , Animales , Gatos , Dendritas/ultraestructura , Potenciales Postsinápticos Excitadores/fisiología , Variación Genética/fisiología , Modelos Neurológicos , Neocórtex/citología , Red Nerviosa/citología , Red Nerviosa/fisiología , Inhibición Neural/fisiología , Células Piramidales/citología , Receptores de GABA/fisiología , Receptores de Glutamato/fisiología , Transmisión Sináptica/fisiología

RESUMEN

Broad amplitude variability and skewed distributions are characteristic features of quantal synaptic currents (minis) at central synapses. The relative contributions of the various underlying sources are still debated. Through computational models of thalamocortical neurons, we separated intra- from extra-synaptic sources. Our simulations indicate that the external factors of local input resistance and dendritic filtering generate equally small amounts of negatively skewed synaptic variability. The ability of these two factors to reduce positive skew increased as their contribution to variability increased, which in control trials for morphological, biophysical, and experimental parameters never exceeded 10% of the range. With these dendritic factors ruled out, we tested multiple release models, which led to distributions with clearly non-physiological multiple peaks. We conclude that intra-synaptic organization is the primary determinant of synaptic variability in thalamocortical neurons and, due to extra-synaptic mechanisms, is more potent than the data suggested. Thalamortical neurons, especially in rodents, constitute a remarkably favorable system for molecular genetic studies of synaptic variability and its functional consequence.

RESUMEN

To investigate the basis of the fluctuating activity present in neocortical neurons in vivo, we have combined computational models with whole-cell recordings using the dynamic-clamp technique. A simplified 'point-conductance' model was used to represent the currents generated by thousands of stochastically releasing synapses. Synaptic activity was represented by two independent fast glutamatergic and GABAergic conductances described by stochastic random-walk processes. An advantage of this approach is that all the model parameters can be determined from voltage-clamp experiments. We show that the point-conductance model captures the amplitude and spectral characteristics of the synaptic conductances during background activity. To determine if it can recreate in vivo-like activity, we injected this point-conductance model into a single-compartment model, or in rat prefrontal cortical neurons in vitro using dynamic clamp. This procedure successfully recreated several properties of neurons intracellularly recorded in vivo, such as a depolarized membrane potential, the presence of high-amplitude membrane potential fluctuations, a low-input resistance and irregular spontaneous firing activity. In addition, the point-conductance model could simulate the enhancement of responsiveness due to background activity. We conclude that many of the characteristics of cortical neurons in vivo can be explained by fast glutamatergic and GABAergic conductances varying stochastically.

Asunto(s)

Potenciales de Acción/fisiología , Ácido Glutámico/metabolismo , Neocórtex/fisiología , Células Piramidales/fisiología , Sinapsis/fisiología , Transmisión Sináptica/fisiología , Ácido gamma-Aminobutírico/metabolismo , Potenciales de Acción/efectos de los fármacos , Animales , Gatos , Compartimento Celular/fisiología , Dendritas/fisiología , Canales Iónicos/efectos de los fármacos , Canales Iónicos/fisiología , Modelos Neurológicos , Neocórtex/citología , Neocórtex/efectos de los fármacos , Red Nerviosa/efectos de los fármacos , Red Nerviosa/fisiología , Inhibición Neural/efectos de los fármacos , Inhibición Neural/fisiología , Técnicas de Cultivo de Órganos , Técnicas de Placa-Clamp , Células Piramidales/citología , Células Piramidales/efectos de los fármacos , Ratas , Ratas Sprague-Dawley , Receptores AMPA/efectos de los fármacos , Receptores AMPA/fisiología , Procesos Estocásticos , Sinapsis/efectos de los fármacos , Transmisión Sináptica/efectos de los fármacos , Tetrodotoxina/farmacología

RESUMEN

Neocortical pyramidal neurons in vivo are subject to an intense synaptic background activity that has a significant impact on various electrophysiological properties and dendritic integration. Using detailed biophysical models of a morphologically reconstructed neocortical pyramidal neuron, in which synaptic background activity was simulated according to recent measurements in cat parietal cortex in vivo, we show that the responsiveness of the cell to additional periodic subthreshold stimuli can be significantly enhanced through mechanisms similar to stochastic resonance. We compare several paradigms leading to stochastic resonance-like behavior, such as varying the strength or the correlation in the background activity. A new type of resonance-like behavior was obtained when the correlation was varied, in which case the responsiveness is sensitive to the statistics rather than the strength of the noise. We suggest that this type of resonance may be relevant to information processing in the cerebral cortex.

Asunto(s)

Potenciales de Acción/fisiología , Relojes Biológicos/fisiología , Neocórtex/fisiología , Células Piramidales/fisiología , Sinapsis/fisiología , Transmisión Sináptica/fisiología , Animales , Gatos , Membrana Celular/fisiología , Potenciales Postsinápticos Excitadores/fisiología , Canales Iónicos/fisiología , Modelos Neurológicos , Neocórtex/citología , Red Nerviosa/citología , Red Nerviosa/fisiología , Conducción Nerviosa/fisiología , Inhibición Neural/fisiología , Células Piramidales/citología

RESUMEN

We investigated the resonance behavior in model neurons receiving a large number of random synaptic inputs, whose distributed nature permits one to introduce correlations between them and investigate its effect on cellular responsiveness. A change in the strength of this background led to enhanced responsiveness, consistent with stochastic resonance. Altering the correlation revealed a type of resonance behavior in which the neuron is sensitive to statistical properties rather than the strength of the noise. Remarkably, the neuron could detect weak correlations among the distributed inputs within millisecond time scales.

Asunto(s)

Modelos Neurológicos , Neocórtex/fisiología , Neuronas/fisiología , Animales , Dendritas/fisiología , Red Nerviosa/fisiología , Procesos Estocásticos , Sinapsis/fisiología , Transmisión Sináptica/fisiología

RESUMEN

Slow-wave sleep consists in slowly recurring waves that are associated with a large-scale spatio-temporal synchrony across neocortex. These slow-wave complexes alternate with brief episodes of fast oscillations, similar to the sustained fast oscillations that occur during the wake state. We propose that alternating fast and slow waves consolidate information acquired previously during wakefulness. Slow-wave sleep would thus begin with spindle oscillations that open molecular gates to plasticity, then proceed by iteratively 'recalling' and 'storing' information primed in neural assemblies. This scenario provides a biophysical mechanism consistent with the growing evidence that sleep serves to consolidate memories.

Asunto(s)

Encéfalo/fisiología , Sueño/fisiología , Animales , Nivel de Alerta/fisiología , Relojes Biológicos/fisiología , Encéfalo/citología , Simulación por Computador , Electroencefalografía , Potenciales Postsinápticos Excitadores/fisiología , Hipocampo/citología , Hipocampo/fisiología , Humanos , Memoria/fisiología , Modelos Neurológicos , Neocórtex/citología , Neocórtex/fisiología , Red Nerviosa/fisiología , Plasticidad Neuronal/fisiología , Células Piramidales/fisiología , Sueño REM/fisiología , Tálamo/citología , Tálamo/fisiología

RESUMEN

Thalamic circuits have an intrinsic capacity to generate state-dependent oscillations of different frequency and degrees of synchrony, but little is known of how synchronized oscillation is controlled in the intact brain or what function it may serve. The influence of cortical feedback was examined using slice preparations of the visual thalamus and computational models. Cortical feedback was mimicked by stimulating corticothalamic axons, triggered by the activity of relay neurons. This artificially coupled network had the capacity to self-organize and to generate qualitatively different rhythmical activities according to the strength of corticothalamic feedback stimuli. Weak feedback (one to three shocks at 100-150 Hz) phase-locked the spontaneous spindle oscillations (6-10 Hz) in geniculate and perigeniculate nuclei. However, strong feedback (four to eight shocks at 100-150 Hz) led to a more synchronized oscillation, slower in frequency (2-4 Hz) and dependent on GABA(B) receptors. This increase in synchrony was essentially attributable to a redistribution of the timing of action potential generation in lateral geniculate nucleus cells, resulting in an increased output of relay cells toward the cortex. Corticothalamic feedback is thus capable of inducing highly synchronous slow oscillations in physiologically intact thalamic circuits. This modulation may have implications for a better understanding of the descending control of thalamic nuclei by the cortex, and the genesis of pathological rhythmical activity, such as absence seizures.

Asunto(s)

Relojes Biológicos/fisiología , Corteza Cerebral/fisiología , Modelos Neurológicos , Tálamo/fisiología , Vías Visuales/fisiología , Potenciales de Acción/efectos de los fármacos , Potenciales de Acción/fisiología , Animales , Axones/fisiología , Simulación por Computador , Estimulación Eléctrica , Potenciales Postsinápticos Excitadores/efectos de los fármacos , Potenciales Postsinápticos Excitadores/fisiología , Retroalimentación/fisiología , Hurones , Antagonistas del GABA/farmacología , Antagonistas de Receptores de GABA-B , Cuerpos Geniculados/fisiología , Técnicas In Vitro , Redes Neurales de la Computación , Neuronas/efectos de los fármacos , Neuronas/metabolismo , Tiempo de Reacción/efectos de los fármacos , Tiempo de Reacción/fisiología , Receptores AMPA/metabolismo , Receptores de GABA-A/metabolismo , Receptores de GABA-B/metabolismo , Estimulación Química