PubMed Health⌕ Search

Biomedical subjects

D Hansel

Publications and source records attributed to D Hansel.

At least 19 recordsLinked to original sources
PubMed2004

How shunting inhibition affects the discharge of lumbar motoneurones: a dynamic clamp study in anaesthetized cats.

In the present work, dynamic clamp was used to inject a current that mimicked tonic synaptic activity in the soma of cat lumbar motoneurones with a microelectrode. The reversal potential of this current could be set at the resting potential so as to prevent membrane depolarization or hyperpolarization. The only effect of the dynamic clamp was then to elicit a constant and calibrated increase of the motoneurone input conductance. The effect of the resulting shunt was investigated on repetitive discharges elicited by current pulses. Shunting inhibition reduced very substantially the firing frequency in the primary range without changing the slope of the current-frequency curves. The shift of the I-f curve was proportional to the conductance increase imposed by the dynamic clamp and depended on an intrinsic property of the motoneurone that we called the shunt potential. The shunt potential ranged between 11 and 37 mV above the resting potential, indicating that the sensitivity of motoneurones to shunting inhibition was quite variable. The shunt potential was always near or above the action potential voltage threshold. A theoretical model allowed us to interpret these experimental results. The shunt potential was shown to be a weighted time average of membrane voltage. The weighting factor is the phase response function of the neurone that peaks at the end of the interspike interval. The shunt potential indicates whether mixed synaptic inputs have an excitatory or inhibitory effect on the ongoing discharge of the motoneurone.

Action Potentials↗
PubMed2003

Asynchronous states and the emergence of synchrony in large networks of interacting excitatory and inhibitory neurons.

We investigate theoretically the conditions for the emergence of synchronous activity in large networks, consisting of two populations of extensively connected neurons, one excitatory and one inhibitory. The neurons are modeled with quadratic integrate-and-fire dynamics, which provide a very good approximation for the subthreshold behavior of a large class of neurons. In addition to their synaptic recurrent inputs, the neurons receive a tonic external input that varies from neuron to neuron. Because of its relative simplicity, this model can be studied analytically. We investigate the stability of the asynchronous state (AS) of the network with given average firing rates of the two populations. First, we show that the AS can remain stable even if the synaptic couplings are strong. Then we investigate the conditions under which this state can be destabilized. We show that this can happen in four generic ways. The first is a saddle-node bifurcation, which leads to another state with different average firing rates. This bifurcation, which occurs for strong enough recurrent excitation, does not correspond to the emergence of synchrony. In contrast, in the three other instability mechanisms, Hopf bifurcations, which correspond to the emergence of oscillatory synchronous activity, occur. We show that these mechanisms can be differentiated by the firing patterns they generate and their dependence on the mutual interactions of the inhibitory neurons and cross talk between the two populations. We also show that besides these codimension 1 bifurcations, the system can display several codimension 2 bifurcations: Takens-Bogdanov, Gavrielov-Guckenheimer, and double Hopf bifurcations.

Excitatory Postsynaptic Potentials↗
PubMed2002

How noise contributes to contrast invariance of orientation tuning in cat visual cortex.

The width of the orientation tuning curves of the spike response of neurons in V1 is invariant to contrast. This property constrains the possible mechanisms underlying orientation selectivity. It has been suggested that noise circumvents the iceberg effect that would prevent contrast invariance in the purely feedforward mechanism. Here we investigate systematically how noise contributes to the contrast invariance of orientation tuning curves in V1. We study three models of increasing complexity: a simple threshold-linear firing rate model, a leaky integrate-and-fire model, and a conductance-based model. We show that the noise transmutes the threshold nonlinearity of the input-output relationships into an approximate power law without a threshold within some firing rate range. This implies that, under certain conditions which are derived here, the tuning of the neuron output is approximately contrast invariant. In particular we show that this mechanism for contrast invariance requires that the neuron firing rate must not be too large and that increasing or lowering the contrast too much destroys this invariance. We also show that if this mechanism operates in V1, the spike response, R, and average voltage response V of the neurons in V1 should vary with the contrast, C, according to R(C)gamma proportional to V(C)gamma. The exponent gamma can be estimated from the amount by which the spike tuning curve is sharpened with respect to the voltage tuning curves of the neurons. This prediction does not depend on the specifics of the model and can be tested experimentally.

Animals↗
PubMed2001

Existence and stability of persistent states in large neuronal networks.

We study the existence and stability of persistent states in large networks of quadratic integrate-and-fire neurons. The networks consist of two populations, one excitatory and one inhibitory. The stability of the asynchronous state is studied analytically. Our study demonstrates the role of recurrent inhibition and inhibitory-inhibitory interactions in stable persistent activity in large neuronal networks.

Action Potentials↗
PubMed2001

Synchrony of rest tremor in multiple limbs in parkinson's disease: evidence for multiple oscillators.

Recent evidence points to involvement of central nervous system oscillators in Parkinson's disease (PD) rest tremor. It remains unknown whether one or multiple oscillators cause tremor in multiple limbs. Based on the prediction that multiple oscillators would cause low coherence even with similar average frequency, we studied 22 PD patients using accelerometers on multiple limbs. Records were digitized and spectral analysis was performed. Peak frequencies in the arms, legs, and chin were similar, indicating that biomechanical factors did not determine the frequency. Coherence between different axes of individual accelerometers and between different segments of the same limb was high. However, coherence between tremor in different limbs was low. There was no consistent pattern across patients of ipsi- vs. contralateral predominance of coherence. These data suggest that tremor in PD is generated by multiple oscillatory circuits, which operate on similar frequencies.

Aged↗
PubMed2001

On synchrony of weakly coupled neurons at low firing rate.

The dynamics of a pair of weakly interacting conductance-based neurons, firing at low frequency, nu, is investigated in the framework of the phase-reduction method. The stability of the antiphase and the in-phase locked state is studied. It is found that for a large class of conductance-based models, the antiphase state is stable (resp., unstable) for excitatory (resp., inhibitory) interactions if the synaptic time constant is above a critical value tau(c)(s), which scales as the absolute value of log nu when nu goes to zero.

Action Potentials↗
PubMed2001

Patterns of synchrony in neural networks with spike adaptation.

We study the emergence of synchronized burst activity in networks of neurons with spike adaptation. We show that networks of tonically firing adapting excitatory neurons can evolve to a state where the neurons burst in a synchronized manner. The mechanism leading to this burst activity is analyzed in a network of integrate-and-fire neurons with spike adaptation. The dependence of this state on the different network parameters is investigated, and it is shown that this mechanism is robust against inhomogeneities, sparseness of the connectivity, and noise. In networks of two populations, one excitatory and one inhibitory, we show that decreasing the inhibitory feedback can cause the network to switch from a tonically active, asynchronous state to the synchronized bursting state. Finally, we show that the same mechanism also causes synchronized burst activity in networks of more realistic conductance-based model neurons.

Animals↗
PubMed2000

Synchrony in heterogeneous networks of spiking neurons.

The emergence of synchrony in the activity of large, heterogeneous networks of spiking neurons is investigated. We define the robustness of synchrony by the critical disorder at which the asynchronous state becomes linearly unstable. We show that at low firing rates, synchrony is more robust in excitatory networks than in inhibitory networks, but excitatory networks cannot display any synchrony when the average firing rate becomes too high. We introduce a new regime where all inputs, external and internal, are strong and have opposite effects that cancel each other when averaged. In this regime, the robustness of synchrony is strongly enhanced, and robust synchrony can be achieved at a high firing rate in inhibitory networks. On the other hand, in excitatory networks, synchrony remains limited in frequency due to the intrinsic instability of strong recurrent excitation.

Action Potentials↗
PubMed2000

The number of synaptic inputs and the synchrony of large, sparse neuronal networks.

The prevalence of coherent oscillations in various frequency ranges in the central nervous system raises the question of the mechanisms that synchronize large populations of neurons. We study synchronization in models of large networks of spiking neurons with random sparse connectivity. Synchrony occurs only when the average number of synapses, M, that a cell receives is larger than a critical value, Mc. Below Mc, the system is in an asynchronous state. In the limit of weak coupling, assuming identical neurons, we reduce the model to a system of phase oscillators that are coupled via an effective interaction, gamma. In this framework, we develop an approximate theory for sparse networks of identical neurons to estimate Mc analytically from the Fourier coefficients of gamma. Our approach relies on the assumption that the dynamics of a neuron depend mainly on the number of cells that are presynaptic to it. We apply this theory to compute Mc for a model of inhibitory networks of integrate-and-fire (I&F) neurons as a function of the intrinsic neuronal properties (e.g., the refractory period Tr), the synaptic time constants, and the strength of the external stimulus, Iext. The number Mc is found to be nonmonotonous with the strength of Iext. For Tr = 0, we estimate the minimum value of Mc over all the parameters of the model to be 363.8. Above Mc, the neurons tend to fire in smeared one-cluster states at high firing rates and smeared two-or-more-cluster states at low firing rates. Refractoriness decreases Mc at intermediate and high firing rates. These results are compared to numerical simulations. We show numerically that systems with different sizes, N, behave in the same way provided the connectivity, M, is such that 1/Meff = 1/M - 1/N remains constant when N varies. This allows extrapolating the large N behavior of a network from numerical simulations of networks of relatively small sizes (N = 800 in our case). We find that our theory predicts with remarkable accuracy the value of Mc and the patterns of synchrony above Mc, provided the synaptic coupling is not too large. We also study the strong coupling regime of inhibitory sparse networks. All of our simulations demonstrate that increasing the coupling strength reduces the level of synchrony of the neuronal activity. Above a critical coupling strength, the network activity is asynchronous. We point out a fundamental limitation for the mechanisms of synchrony relying on inhibition alone, if heterogeneities in the intrinsic properties of the neurons and spatial fluctuations in the external input are also taken into account.

Algorithms↗
PubMed1998

On numerical simulations of integrate-and-fire neural networks.

It is shown that very small time steps are required to reproduce correctly the synchronization properties of large networks of integrate-and-fire neurons when the differential system describing their dynamics is integrated with the standard Euler or second-order Runge-Kutta algorithms. The reason for that behavior is analyzed, and a simple improvement of these algorithms is proposed.

Action Potentials↗
PubMed1998

Physiology of MPTP tremor.

Rhesus and vervet monkeys respond differently to treatment with 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine hydrochloride neurotoxin (MPTP). Both species develop akinesia, rigidity, and severe postural instability. However, rhesus monkeys only develop infrequent, short episodes of high-frequency tremor, whereas vervet monkeys have many prolonged episodes of low-frequency tremor. After MPTP treatment, the spiking activity of many pallidal neurons became oscillatory and highly correlated. Oscillatory autocorrelation functions were dominated by lower frequencies, cross-correlograms by higher frequencies. The phase shift distribution of the oscillatory cross-correlograms of pallidal cells in MPTP-treated vervet monkey were clustered around 0 phase shift, unlike the oscillatory correlograms in the MPTP-treated rhesus monkey, which were widely distributed between 0 degrees and 180 degrees. Analysis of the instantaneous phase differences between tremors of two limbs in the MPTP monkeys and human parkinsonian patients showed short periods of tremor synchronization. We thus concluded that the rhesus and the vervet models of MPTP-induced parkinsonism may represent the tremulous and nontremulous variants of human parkinsonism. We suggest that the tremor phenomena of Parkinson's disease (PD) are related to the emergence of synchronous neuronal oscillations in the basal ganglia. Finally, the oscillating neuronal assemblies in the pallidum of tremulous parkinsonian primates are more stable (in time and in space) than those of parkinsonian primates without overt tremor.

1-Methyl-4-phenyl-1,2,3,6-tetrahydropyridine↗
PubMed1997

Traveling waves and the processing of weakly tuned inputs in a cortical network module.

Recent studies have shown that local cortical feedback can have an important effect on the response of neurons in primary visual cortex to the orientation of visual stimuli. In this work, we study the role of the cortical feedback in shaping the spatiotemporal patterns of activity in cortex. Two questions are addressed: one, what are the limitations on the ability of cortical neurons to lock their activity to rotating oriented stimuli within a single receptive field? Two, can the local architecture of visual cortex lead to the generation of spontaneous traveling pulses of activity? We study these issues analytically by a population-dynamic model of a hypercolumn in visual cortex. The order parameter that describes the macroscopic behavior of the network is the time-dependent population vector of the network. We first study the network dynamics under the influence of a weakly tuned input that slowly rotates within the receptive field. We show that if the cortical interactions have strong spatial modulation, the network generates a sharply tuned activity profile that propagates across the hypercolumn in a path that is completely locked to the stimulus rotation. The resultant rotating population vector maintains a constant angular lag relative to the stimulus, the magnitude of which grows with the stimulus rotation frequency. Beyond a critical frequency the population vector does not lock to the stimulus but executes a queasi-periodic motion with an average frequency that is smaller than that of the stimulus. In the second part we consider the stable intrinsic state of the cortex under the influence of isotropic stimulation. We show that if the local inhibitory feedback is sufficiently strong, the network does not settle into a stationary state but develops spontaneous traveling pulses of activity. Unlike recent models of wave propagation in cortical networks, the connectivity pattern in our model is spatially symmetric, hence the direction of propagation of these waves is arbitrary. The interaction of these waves with an external-oriented stimulus is studied. It is shown that the system can lock to a weakly tuned rotating stimulus if the stimulus frequency is close to the frequency of the intrinsic wave.

Animals↗
PubMed1996

Chaos and synchrony in a model of a hypercolumn in visual cortex.

Neurons in cortical slices emit spikes or bursts of spikes regularly in response to a suprathreshold current injection. This behavior is in marked contrast to the behavior of cortical neurons in vivo, whose response to electrical or sensory input displays a strong degree of irregularity. Correlation measurements show a significant degree of synchrony in the temporal fluctuations of neuronal activities in cortex. We explore the hypothesis that these phenomena are the result of the synchronized chaos generated by the deterministic dynamics of local cortical networks. A model of a "hypercolumn" in the visual cortex is studied. It consists of two populations of neurons, one inhibitory and one excitatory. The dynamics of the neurons is based on a Hodgkin-Huxley type model of excitable voltage-clamped cells with several cellular and synaptic conductances. A slow potassium current is included in the dynamics of the excitatory population to reproduce the observed adaptation of the spike trains emitted by these neurons. The pattern of connectivity has a spatial structure which is correlated with the internal organization of hypercolumns in orientation columns. Numerical simulations of the model show that in an appropriate parameter range, the network settles in a synchronous chaotic state, characterized by a strong temporal variability of the neural activity which is correlated across the hypercolumn. Strong inhibitory feedback is essential for the stabilization of this state. These results show that the cooperative dynamics of large neuronal networks are capable of generating variability and synchrony similar to those observed in cortex. Auto-correlation and cross-correlation functions of neuronal spike trains are computed, and their temporal and spatial features are analyzed. In other parameter regimes, the network exhibits two additional states: synchronized oscillations and an asynchronous state. We use our model to study cortical mechanisms for orientation selectivity. It is shown that in a suitable parameter regime, when the input is not oriented, the network has a continuum of states, each representing an inhomogeneous population activity which is peaked at one of the orientation columns. As a result, when a weakly oriented input stimulates the network, it yields a sharp orientation tuning. The properties of the network in this regime, including the appearance of virtual rotations and broad stimulus-dependent cross-correlations, are investigated. The results agree with the predictions of the mean field theory which was previously derived for a simplified model of stochastic, two-state neurons. The relation between the results of the model and experiments in visual cortex are discussed.

Models, Neurological↗
PubMed1996

Synchronized chaos in local cortical circuits.

Neurons in cortical slices emit spikes or bursts of spikes regularly in response to a suprathreshold current injection. This is in marked contrast to the behavior of cortical neurons in-vivo, whose response to electrical or sensory input displays a strong degree of irregularity. Correlation measurements show significant degree of synchrony in the temporal fluctuations of neuronal activities in cortex. We explore the hypothesis that these phenomena are the result of the synchronized chaos generated by the deterministic dynamics of local cortical networks. A model of a "hypercolumn" in the visual cortex is studied. It consists of two populations of neurons, one inhibitory and one excitatory. The dynamics of the neurons is based on a Hodgkin-Huxley type model with several cellular and synaptic conductances. The pattern of connectivity is correlated with the internal organization of hypercolumns in orientation columns. Numerical simulations of the model show that in an appropriate parameter range, the network settles in a synchronous chaotic state, characterized by a strong temporal variability of the neural activity which is correlated across the hypercolumn. Strong inhibitory feedback is essential for the stabilization of this state. Auto-correlation and cross-correlation functions of neuronal spike trains are computed, and analyzed. The relation between the results of the model and experiments in visual cortex is discussed.

Animals↗
PubMed1995

Synchrony in excitatory neural networks.

Synchronization properties of fully connected networks of identical oscillatory neurons are studied, assuming purely excitatory interactions. We analyze their dependence on the time course of the synaptic interaction and on the response of the neurons to small depolarizations. Two types of responses are distinguished. In the first type, neurons always respond to small depolarization by advancing the next spike. In the second type, an excitatory postsynaptic potential (EPSP) received after the refractory period delays the firing of the next spike, while an EPSP received at a later time advances the firing. For these two types of responses we derive general conditions under which excitation destabilizes in-phase synchrony. We show that excitation is generally desynchronizing for neurons with a response of type I but can be synchronizing for responses of type II when the synaptic interactions are fast. These results are illustrated on three models of neurons: the Lapicque integrate-and-fire model, the model of Connor et al., and the Hodgkin-Huxley model. The latter exhibits a type II response, at variance with the first two models, that have type I responses. We then examine the consequences of these results for large networks, focusing on the states of partial coherence that emerge. Finally, we study the Lapicque model and the model of Connor et al. at large coupling and show that excitation can be desynchronizing even beyond the weak coupling regime.

Computer Simulation↗