Rune W. Berg

Influence of Phasic and Tonic Dopamine Release on Receptor Activation

Tonic and phasic dopamine release is implicated in learning, motivation, and motor functions. However, the relationship between spike patterns in dopaminergic neurons, the extracellular concentration of dopamine, and activation of dopamine receptors remains unresolved. In the present study, we develop a computational model of dopamine signaling that give insight into the relationship between the dynamics of release and occupancy of D1 and D2 receptors. The model is derived from first principles using experimental data. It has no free parameters and offers unbiased estimation of the boundaries of dopaminergic volume transmission. Bursts primarily increase occupancy of D1 receptors, whereas pauses translate into low occupancy of D1 and D2 receptors. Phasic firing patterns, composed of bursts and pauses, reduce the average D2 receptor occupancy and increase average D1 receptor occupancy compared with equivalent tonic firing. Receptor occupancy is crucially dependent on synchrony and the balance between tonic and phasic firing modes. Our results provide quantitative insight in the dynamics of volume transmission and complement experimental data obtained with electrophysiology, positron emission tomography, microdialysis, amperometry, and voltammetry.

Anatomical loops and their electrical dynamics in relation to whisking by rat.

An accumulation of anatomical, behavioral, and electrophysiological evidence allows us to identify the neuronal circuitry that is involved with vibrissa-mediated sensation and the control of rhythmic vibrissa movement. Anatomical evidence points to a multiplicity of closed sensorimotor loops, while electrophysiological data delineate the flow of electrical signals in these pathways. These loops process sensory input from the vibrissae and send projections to direct vibrissa movement, starting at the level of the hindbrain and proceeding toward loops that involve multiple structures in the forebrain. The nature of the vibrissa-related electrical signals in behaving animals has been studied extensively at the level of neocortical loops. Two types of spike signal are observed that serve as a reference of vibrissa motion: a fast signal that correlates with the relative phase of the vibrissae within a whisk cycle and a slow signal that correlates with the amplitude, and possibly the set-point, of the vibrissae during a whisk. Both signals are observed in vibrissa primary sensory (S1) cortex, and in some cases they are sufficiently robust to allow vibrissa position to be accurately estimated from the spike train of a single neuron. Unlike the case for S1 cortex, only the slow signal has been observed in vibrissa primary motor (M1) cortex. The control capabilities of M1 cortex were estimated from experiments with anesthetized animals in which progressive areas along the vibrissa motor branch were microstimulated with rhythmically applied currents. The motion of the vibrissae followed stimulation of M1 cortex only for rates that were well below the frequency of rhythmic whisking; in contrast, the vibrissae followed stimulation of the facial nucleus, whose cells directly drive the vibrissae, for rates above that of whisking. In toto, the evidence implies that there is fast signaling from the facial nucleus, through the mystacial pad and the vibrissae and up through sensory cortex, but only slow signaling at the level of the motor cortex and down through the superior colliculus to the facial nucleus. The transformation from fast sensory signals to slow motor control is an unresolved issue. On the other hand, there is a candidate scheme to understand how the fast reference of vibrissa motion in the whisk cycle may be used to decode the angle of the vibrissae upon their contact with an object. We discuss a circuit in which servo mechanisms are used to determine the angle of contact relative to the preferred phase of the fast reference signals. Support for this scheme comes from results with anesthetized animals on the frequency and phase entrainment of intrinsic neuronal oscillators in S1 cortex. A prediction based on this scheme is that the output from a decoder circuit is maximal when the angle of contact differs from the preferred phase of a fast regerence signal. In contrast, for correlation-based schemes the output is maximal when the angle of contact equals the preferred phase.

Unilateral vibrissa contact: changes in amplitude but not timing of rhythmic whisking

Electromyographic recordings from the mystacial pad of rats were used to assess the effect of unilateral vibrissa contact on the bilateral movement of the vibrissae. A first group of animals was trained to whisk freely in air and served to establish the baseline variability in bilateral symmetry. We observed that the electromyogram (EMG) activity across the two mystacial pads was rhythmic and synchronous to within 2 ms on a whisk-by-whisk basis; this value is small in comparison with the ˜50 ms required for protraction during the whisk cycle. A second group of animals was trained to use their vibrissae to contact a sensor that was located on one side of the head. The average EMG activity across the two pads was synchronous at the time of vibrissa contact, albeit with higher variability than for the case of free whisking. In contrast, the average amplitude of the activity on the contact vs noncontact side of the face was transiently greater, by 25% or ˜10°, at the time of contact. These data show that the amplitude of the vibrissae on the two sides of the face can be controlled independently, while the timing of vibrissa movement is largely synchronous.

Intense synaptic activity enhances temporal resolution in spinal motoneurons.

In neurons, spike timing is determined by integration of synaptic potentials in delicate concert with intrinsic properties. Although the integration time is functionally crucial, it remains elusive during network activity. While mechanisms of rapid processing are well documented in sensory systems, agility in motor systems has received little attention. Here we analyze how intense synaptic activity affects integration time in spinal motoneurons during functional motor activity and report a 10-fold decrease. As a result, action potentials can only be predicted from the membrane potential within 10 ms of their occurrence and detected for less than 10 ms after their occurrence. Being shorter than the average inter-spike interval, the AHP has little effect on integration time and spike timing, which instead is entirely determined by fluctuations in membrane potential caused by the barrage of inhibitory and excitatory synaptic activity. By shortening the effective integration time, this intense synaptic input may serve to facilitate the generation of rapid changes in movements.

Exploratory Whisking by Rat Is Not Phase Locked to the Hippocampal Theta Rhythm

The rat has a strong 6–9 Hz rhythm of electrical activity in the hippocampus, known as the theta rhythm. Exploratory whisking, i.e., the rhythmic movement of the rat’s vibrissas to acquire tactile information, occurs within the same frequency range as the theta rhythm and provides a model system to examine the relationship between theta rhythm and active sensory movements. In particular, it has been postulated that these two rhythms are phase locked as a means to synchronize sensory and hippocampal processing. We tested this hypothesis in rats trained to whisk in air. Theta activity was measured via field electrodes in the hippocampus, and whisking was measured via the mystacial electromyogram. We calculated the spectral coherence between these two signals as a means to quantify the extent of phase locking. First, we found that the fraction of epochs with high coherence is not significantly greater than that expected by chance (seven of eight animals and as a population average). Second, we found that the trial-averaged coherence is low (coherence, < 0.1) and, as an average across all animals, statistically insignificant. We further asked whether the strength of the theta rhythm correlated with that of whisking, independent of the lack of cycle-by-cycle coherence. We observe that the correlation is weak and insignificant (six of eight animals and as a population average). We conclude that there is no relationship between the whisking and theta rhythms, at least when animals whisk in air.

Synaptic inhibition and excitation estimated via the time constant of membrane potential fluctuations

When recording the membrane potential, V, of a neuron it is desirable to be able to extract the synaptic input. Critically, the synaptic input is stochastic and nonreproducible so one is therefore often restricted to single-trial data. Here, we introduce means of estimating the inhibition and excitation and their confidence limits from single sweep trials. The estimates are based on the mean membrane potential, V, and the membrane time constant, τ. The time constant provides the total conductance (G = capacitance/τ) and is extracted from the autocorrelation of V. The synaptic conductances can then be inferred from V when approximating the neuron as a single compartment. We further employ a stochastic model to establish limits of confidence. The method is verified on models and experimental data, where the synaptic input is manipulated pharmacologically or estimated by an alternative method. The method gives best results if the synaptic input is large compared with other conductances, the intrinsic conductances have little or no time dependence or are comparably small, the ligand-gated kinetics is faster than the membrane time constant, and the majority of synaptic contacts are electrotonically close to soma (recording site). Although our data are in current clamp, the method also works in V-clamp recordings, with some minor adaptations. All custom made procedures are provided in Matlab.

Premotor spinal network with balanced excitation and inhibition during motor patterns has high resilience to structural division.

Direct measurements of synaptic inhibition (I) and excitation (E) to spinal motoneurons can provide an important insight into the organization of premotor networks. Such measurements of flexor motoneurons participating in motor patterns in turtles have recently demonstrated strong concurrent E and I as well as stochastic membrane potentials and irregular spiking in the adult turtle spinal cord. These findings represent a departure from the widespread acceptance of feedforward reciprocal rate models for spinal motor function. The apparent discrepancy has been reviewed as an experimental artifact caused by the distortion of local networks in the transected turtle spinal cord. We tested this assumption in the current study by performing experiments to assess the integrity of motor functions in the intact spinal cord and the cord transected at segments D9/D10. Excitatory and inhibitory synaptic inputs to motoneurons were estimated during rhythmic motor activity and demonstrated primarily intense inputs that consisted of qualitatively similar mixed E/I before and after the transection. To understand this high functional resilience, we used mathematical modeling of networks with recurrent connectivity that could potentially explain the balanced E/I. Both experimental and modeling data support the concept of a locally balanced premotor network consisting of recurrent E/I connectivity, in addition to the well known reciprocal network activity. The multifaceted synaptic connections provide spinal networks with a remarkable ability to remain functional after structural divisions.

Lognormal firing rate distribution reveals prominent fluctuation–driven regime in spinal motor networks

When spinal circuits generate rhythmic movements it is important that the neuronal activity remains within stable bounds to avoid saturation and to preserve responsiveness. Here, we simultaneously record from hundreds of neurons in lumbar spinal circuits of turtles and establish the neuronal fraction that operates within either a ‘mean-driven’ or a ‘fluctuation–driven’ regime. Fluctuation-driven neurons have a ‘supralinear’ input-output curve, which enhances sensitivity, whereas the mean-driven regime reduces sensitivity. We find a rich diversity of firing rates across the neuronal population as reflected in a lognormal distribution and demonstrate that half of the neurons spend at least 50 % of the time in the ‘fluctuation–driven’ regime regardless of behavior. Because of the disparity in input–output properties for these two regimes, this fraction may reflect a fine trade–off between stability and sensitivity in order to maintain flexibility across behaviors. DOI: http://dx.doi.org/10.7554/eLife.18805.001

Signaling in large-scale neural networks

We examine the recent finding that neurons in spinal motor circuits enter a high conductance state during functional network activity. The underlying concomitant increase in random inhibitory and excitatory synaptic activity leads to stochastic signal processing. The possible advantages of this metabolically costly organization are analyzed by comparing with synaptically less intense networks driven by the intrinsic response properties of the network neurons.

Stereological Estimate of the Total Number of Neurons in Spinal Segment D9 of the Red-Eared Turtle

The red-eared turtle is an important animal model for investigating the neural activity in the spinal circuit that generates motor behavior. However, basic anatomical features, including the number of neurons in the spinal segments involved, are unknown. In the present study, we estimate the total number of neurons in segment D9 of the spinal cord in the red-eared turtle (Trachemys scripta elegans) using stereological cell counting methods. In transverse spinal cord sections stained with modified Giemsa, motoneurons (MNs), interneurons (INs), and non-neuronal cells were distinguished according to location and morphology. Each cell type was then counted separately using an optical disector with the cell nucleus as counting item. The number of cells in segment D9 was as follows (mean ± SE): MNs, 2049 ± 74; INs, 16,135 ± 316; non-neuronal cells, 47,504 ± 478 (n = 6). These results provide the first estimate of the total number of neurons in a spinal segment in a terrestrial vertebrate based on unbiased stereological methods and an upper bound on the number of neurons involved in segmental sensorimotor activity. These findings also form a crucial quantitative foundation for integrating electrophysiological data into mathematical circuit models.