Biomedical subjects

Carmen C Canavier

Publications and source records attributed to Carmen C Canavier.

4 recordsLinked to original sources

PubMed2004

Multimodal behavior in a four neuron ring circuit: mode switching.

We study a four-neuron ring circuit comprised of oscillating burst-type neurons unidirectionally coupled via inhibitory synapses. Simple circuits of this type have been used previously to study gait patterns. The ring circuit itself is a variant of the basic reciprocal inhibition network, and it exhibits the property of multistability (multiple stable modes of behavior). That is, different gait modes can be achieved via appropriate initialization of and parameterization of this self-excited oscillatory network. We demonstrate three common gait modes with this circuit: the walk, the bound, and a slightly rotated trot mode. Attention is focused mainly on the mechanisms of rapidly and effectively switching between these modes. Our simulations suggest that neuron membrane dynamics, as well as synaptic junctional properties, strongly influence phase sensitivity in the network; each synapse is a combination of both and can be characterized by a transient phase response curve (PRC). We use the same bursting neuron model to characterize all network neurons, and shape different transient PRCs by using different synaptic properties. The characteristics of these PRCs determine the gait modes sustained in any network configuration, as well as, the ability to switch between modes. The mechanisms explored in this simple circuit, may find application in the switching of more complicated gait pattern networks, as well as, in the design of neuromorphic gait pattern circuits.

Action Potentials↗

PubMed2003

A modeling study suggests complementary roles for GABAA and NMDA receptors and the SK channel in regulating the firing pattern in midbrain dopamine neurons.

Midbrain dopaminergic (DA) neurons in vivo exhibit two major firing patterns: single-spike firing and burst firing. The firing pattern expressed is dependent on both the intrinsic properties of the neurons and their excitatory and inhibitory synaptic inputs. Experimental data suggest that the activation of N-methyl-D-aspartate (NMDA) and GABAA receptors is a crucial contributor to the initiation and suppression of burst firing, respectively, and that blocking Ca(2+)-activated potassium SK channels can facilitate burst firing. A multi-compartmental model of a DA neuron with a branching structure was developed and calibrated based on in vitro experimental data to explore the effects of different levels of activation of NMDA and GABAA receptors as well as the modulation of the SK current on the firing activity. The simulated tonic activation of GABAA receptors was calibrated by taking into account the difference in the electrotonic properties in vivo versus in vitro. Although NMDA-evoked currents are required for burst generation in the model, currents evoked by GABAA-receptor activation can also regulate the firing pattern. For example, the model predicts that increasing the level of NMDA receptor activation can produce excessive depolarization that prevents burst firing, but a concurrent increase in the activation of GABAA receptors can restore burst firing. Another prediction of the model is that blocking the SK channel current in vivo will facilitate bursting, but not as robustly as blocking the GABAA receptors.

Animals↗

PubMed2002

Electrical coupling between model midbrain dopamine neurons: effects on firing pattern and synchrony.

The role of gap junctions between midbrain dopamine (DA) neurons in mechanisms of firing pattern generation and synchronization has not been well characterized experimentally. We modified a multi-compartment model of DA neuron by adding a spike-generating mechanism and electrically coupling the dendrites of two such neurons through gap junctions. The burst-generating mechanism in the model neuron results from the interaction of a N-methyl-D-aspartate (NMDA)-induced current and the sodium pump. The firing patterns exhibited by the two model neurons included low frequency (2-7 Hz) spiking, high-frequency (13-20 Hz) spiking, irregular spiking, regular bursting, irregular bursting, and leader/follower bursting, depending on the parameter values used for the permeability for NMDA-induced current and the conductance for electrical coupling. All of these firing patterns have been observed in physiological neurons, but a systematic dependence of the firing pattern on the covariation of these two parameters has not been established experimentally. Our simulations indicate that electrical coupling facilitates NMDA-induced burst firing via two mechanisms. The first can be observed in a pair of identical cells. At low frequencies (low NMDA), as coupling strength was increased, only a transition from asynchronous to synchronous single-spike firing was observed. At high frequencies (high NMDA), increasing the strength of the electrical coupling in an identical pair resulted in a transition from high-frequency single-spike firing to burst firing, and further increases led to synchronous high-frequency spiking. Weak electrical coupling destabilizes the synchronous solution of the fast spiking subsystems, and in the presence of a slowly varying sodium concentration, the desynchronized spiking solution leads to bursts that are approximately in phase with spikes that are not in phase. Thus this transitional mechanism depends critically on action potential dynamics. The second mechanism for the induction of burst firing requires a heterogeneous pair that is, respectively, too depolarized and too hyperpolarized to burst. The net effect of the coupling is to bias at least one cell into an endogenously burst firing regime. In this case, action potential dynamics are not critical to the transitional mechanism. If electrical coupling is indeed more prominent in vivo due to basal level of modulation of gap junctions in vivo, these results may indicate why NMDA-induced burst firing is easier to observe in vivo as compared in vitro.

Action Potentials↗

PubMed2002

The influence of limit cycle topology on the phase resetting curve.

Understanding the phenomenology of phase resetting is an essential step toward developing a formalism for the analysis of circuits composed of bursting neurons that receive multiple, and sometimes overlapping, inputs. If we are to use phase-resetting methods to analyze these circuits, we can either generate phase-resetting curves (PRCs) for all possible inputs and combinations of inputs, or we can develop an understanding of how to construct PRCs for arbitrary perturbations of a given neuron. The latter strategy is the goal of this study. We present a geometrical derivation of phase resetting of neural limit cycle oscillators in response to short current pulses. A geometrical phase is defined as the distance traveled along the limit cycle in the appropriate phase space. The perturbations in current are treated as displacements in the direction corresponding to membrane voltage. We show that for type I oscillators, the direction of a perturbation in current is nearly tangent to the limit cycle; hence, the projection of the displacement in voltage onto the limit cycle is sufficient to give the geometrical phase resetting. In order to obtain the phase resetting in terms of elapsed time or temporal phase, a mapping between geometrical and temporal phase is obtained empirically and used to make the conversion. This mapping is shown to be an invariant of the dynamics. Perturbations in current applied to type II oscillators produce significant normal displacements from the limit cycle, so the difference in angular velocity at displaced points compared to the angular velocity on the limit cycle must be taken into account. Empirical attempts to correct for differences in angular velocity (amplitude versus phase effects in terms of a circular coordinate system) during relaxation back to the limit cycle achieved some success in the construction of phase-resetting curves for type II model oscillators. The ultimate goal of this work is the extension of these techniques to biological circuits comprising type II neural oscillators, which appear frequently in identified central pattern-generating circuits.

Models, Neurological↗