Biophysical mechanisms of frequency-dependence and its neuromodulation in neurons in oscillatory networks

In response to oscillatory input, many isolated neurons exhibit a preferred frequency response in their voltage amplitude and phase shift. Membrane potential resonance (MPR), a maximum amplitude in a neuron’s input impedance at a non-zero frequency, captures the essential subthreshold properties of a neuron, which may provide a coordinating mechanism for organizing the activity of oscillatory neuronal networks around a given frequency. In the pyloric central pattern generator network of the crab Cancer borealis, for example, the pacemaker group pyloric dilator neurons show MPR at a frequency that is correlated with the network frequency. This dissertation uses the crab pyloric CPG to examine how, in one neuron type, interactions of ionic currents, even when expressed at different levels, can produce consistent MPR properties, how MPR properties are modified by neuromodulators and how such modifications may lead to distinct functional effects at different network frequencies. In the first part of this dissertation it is demonstrated that, despite the extensive variability of individual ionic currents in a neuron type such as PD, these currents can generate a consistent impedance profile as a function of input frequency and therefore result in stable MPR properties. Correlated changes in ionic current parameters are associated with the dependence of MPR on the membrane potential range. Synaptic inputs or neuromodulators that shift the membrane potential range can modify the interaction of multiple resonant currents and therefore shift the MPR frequency. Neuromodulators change the properties of voltage-dependent ionic currents. Since ionic current interactions are nonlinear, the modulation of excitability and the impedance profile may depend on all ionic current types expressed by the neuron. MPR is generated by the interaction of positive and negative feedback effects due to fast amplifying and slower resonant currents. Neuromodulators can modify existing MPR properties to generate antiresonance (a minimum amplitude response). In the second part of this dissertation, it is shown that the neuropeptide proctolin produces antiresonance in the follower lateral pyloric neuron, but not in the PD neuron. This finding is inconsistent with the known influences of proctolin. However, a novel proctolin-activated ionic current is shown to produce the antiresonance. Using linear models, antiresonance is then demonstrated to amplify MPR in synaptic partner neurons, indicating a potential function in the pyloric network. Neuromodulators are state dependent, so that their action may depend on the prior activity history of the network. It is shown that state-dependence may arise in part from the time-dependence of an inactivating inward current targeted by the neuromodulator proctolin. Due to the kinetics of inactivation, this current advances the burst phase and increases the duty cycle of the neuron, but mainly at higher network frequencies. These results demonstrate that the effect of neuromodulators on MPR in individual neuron types depends on the nonlinear interaction of modulator-activated and other ionic currents as well as the activation of currents with frequency-dependent properties. Consequently, the action of neuromodulators on the output of oscillatory networks may depend on the frequency of oscillations and be predictable from the MPR properties of the network neurons

A Role for Compromise: Synaptic Inhibition and Electrical Coupling Interact to Control Phasing in the Leech Heartbeat CPG

How can flexible phasing be generated by a central pattern generator (CPG)? To address this question, we have extended an existing model of the leech heartbeat CPG's timing network to construct a model of the CPG core and explore how appropriate phasing is set up by parameter variation. Within the CPG, the phasing among premotor interneurons switches regularly between two well defined states – synchronous and peristaltic. To reproduce experimentally observed phasing, we varied the strength of inhibitory synaptic and excitatory electrical input from the timing network to follower premotor interneurons. Neither inhibitory nor electrical input alone was sufficient to produce proper phasing on both sides, but instead a balance was required. Our model suggests that the different phasing of the two sides arises because the inhibitory synapses and electrical coupling oppose one another on one side (peristaltic) and reinforce one another on the other (synchronous). Our search of parameter space defined by the strength of inhibitory synaptic and excitatory electrical input strength led to a CPG model that well approximates the experimentally observed phase relations. The strength values derived from this analysis constitute model predictions that we tested by measurements made in the living system. Further, variation of the intrinsic properties of follower interneurons showed that they too systematically influence phasing. We conclude that a combination of inhibitory synaptic and excitatory electrical input interacting with neuronal intrinsic properties can flexibly generate a variety of phase relations so that almost any phasing is possible

Anterograde Signalling by Nitric Oxide: Characterisation and In Vitro Reconstitution of an Identified Nitrergic Synapse

Nitric oxide (NO) is recognized as a signaling molecule in the CNS where it is a candidate retrograde neurotransmitter. Here we provide direct evidence that NO mediates slow excitatory anterograde transmission between the NO synthase (NOS)-expressing B2 neuron and an NO-responsive follower neuron named B7nor. Both are motoneurons located in the buccal ganglia of the snail Lymnaea stagnalis where they participate in feeding behavior. Transmission between B2 and B7nor is blocked by inhibiting NOS and is suppressed by extracellular scavenging of NO. Furthermore, focal application of NO to the cell body of the B7nor neuron causes a depolarization that mimics the effect of B2 activity. The slow interaction between the B2 and B7nor neurons can be re-established when the two neurons are cocultured, and it shows the same susceptibility to NOS inhibition and NO scavenging. In cell culture we have also examined spatial aspects of NO signaling. We show that before the formation of an anatomical connection, the presynaptic neuron can cause depolarizing potentials in the follower neuron at distances up to 50 micro(m). The strength of the interaction increases when the distance between the cells is reduced. Our results suggest that NO can function as both a synaptic and a nonsynaptic signaling molecul

Using feed-forward networks to infer the activity of feedback neuronal networks

The nervous system is one of the most important organ systems in a multicellular body. Animals, including human beings perceive, learn, think and deliver motion instructions through their nervous system. The basic structural units of the nervous system are individual neurons which constitute different neuronal networks with distinct functions. In each network, constituent neurons are coupled with different connection patterns, for example, some neurons send feed-forward information to the coupling neurons while others are mutually coupled. Because it is often difficult to analyze large interconnected feedback neuronal networks, it is important to derive techniques to reduce the complexity of the analysis. My research focuses on using the information of different feed-forward neuronal networks to infer the activity of feedback networks. To accomplish this objective, I use geometric analysis combined with numerical simulations for some typical neuronal systems to determine the activity of the feedback neuronal network in the context of central pattern generating networks. In my study, I am interested in deriving reduced methods to understand the combined effect of short-term plasticity on the phase-locked activity of networks. I consider a network of two reciprocally coupled heterogenous neurons, A and B, with synaptic depression from neuron A to neuron B. Suppose we are given two pieces of feed-forward information, the effect of neuron A on the activity of neuron B in the feed-forward network of A entraining B and vice versa. Moreover, suppose these effects are not limited to the weak coupling regime. We have developed a method to combine these pieces of feed-forward information into a 2D map that predicts the activity phase of these two neurons when they are mutually coupled. The analysis of the map is based on certain geometric constructs that arise from each of the feed-forward processes. Our analysis has two parts corresponding to different intrinsic firing patterns of these two neurons. In the first part, we assume that neuron A is oscillatory, while neuron B is not. In the second part, both neurons A and B are assumed to be oscillatory. Both sets of assumptions lead to different feedback maps

Short Conduction Delays Cause Inhibition Rather than Excitation to Favor Synchrony in Hybrid Neuronal Networks of the Entorhinal Cortex

How stable synchrony in neuronal networks is sustained in the presence of conduction delays is an open question. The Dynamic Clamp was used to measure phase resetting curves (PRCs) for entorhinal cortical cells, and then to construct networks of two such neurons. PRCs were in general Type I (all advances or all delays) or weakly type II with a small region at early phases with the opposite type of resetting. We used previously developed theoretical methods based on PRCs under the assumption of pulsatile coupling to predict the delays that synchronize these hybrid circuits. For excitatory coupling, synchrony was predicted and observed only with no delay and for delays greater than half a network period that cause each neuron to receive an input late in its firing cycle and almost immediately fire an action potential. Synchronization for these long delays was surprisingly tight and robust to the noise and heterogeneity inherent in a biological system. In contrast to excitatory coupling, inhibitory coupling led to antiphase for no delay, very short delays and delays close to a network period, but to near-synchrony for a wide range of relatively short delays. PRC-based methods show that conduction delays can stabilize synchrony in several ways, including neutralizing a discontinuity introduced by strong inhibition, favoring synchrony in the case of noisy bistability, and avoiding an initial destabilizing region of a weakly type II PRC. PRCs can identify optimal conduction delays favoring synchronization at a given frequency, and also predict robustness to noise and heterogeneity

Determining how stable network oscillations arise from neuronal and synaptic mechanisms

Many animal behaviors involve the generation of rhythmic patterns and movements. These rhythmic patterns are commonly mediated by neural networks that produce an oscillatory activity pattern, where different neurons maintain a relative phase relationship. This thesis examines the relationships between the cellular and synaptic properties that give rise to stable activity in the form of phase maintenance, across different frequencies in a well-suited model system, the pyloric network of the crab Cancer borealis. The pyloric network has endogenously oscillating ‘pacemaker’ neurons that inhibit ‘follower’ neurons, which in turn feed back onto the pacemaker neurons. The focus of this thesis was to determine the methods by which phase maintenance is achieved in an oscillatory network. This thesis examines the idea that phase maintenance occurs through the actions of intrinsic properties of isolated neurons or through the dynamics of their synaptic connections or both. A combination of pharmacological and electrophysiological techniques a used to show how identified membrane properties and short-term synaptic plasticity are involved with phase maintenance over a range of biologically relevant oscillation frequencies. To examine whether network stability is due to the characteristic stable activity of the identified pyloric neuron types, the hypothesis that phase maintenance is an inherent property of synaptically-isolated individual neurons in the pyloric network was first tested. A set of parameters were determined (frequency-dependent activity profile) to define the response of each isolated pyloric neuron to sinusoidal input at different frequencies. The parameters that define the activity profile are: burst onset phase, burst end phase, resonance frequency and intra-burst spike frequency. Each pyloric neuron type was found to possess a unique activity profile, indicating that the individual neuron types are tuned to produce a particular activity pattern at different frequencies depending on their role in the network. To elucidate the biophysical properties underlying the frequency-dependent activity profiles of the neurons, the hyperpolarization activated current (Ih) was measured and found to possess frequency-dependent properties. This implies that Ih has a different influence on the activity phase of pyloric neurons at different frequencies. Additionally, it was found that the Ih contribution to the burst onset phase depends on the neuron type: in the pacemaker group neurons (PD) it had no influence on the burst onset phase at any frequency whereas in follower neurons it acted to advance the onset phase in one neuron type (LP) and, paradoxically, to delay it in a different neuron type (PY). The results from this part of the study provided evidence that stability is due in part to the intrinsic neuronal properties but that these intrinsic properties do not fully explain network stability. To address the contribution of pyloric synapses to network stability, the mechanisms by which synapses promote phase maintenance were investigated. An artificial synapse that mimicked the feedforward PD to LP synapse, was used so that the synaptic parameters could be varied in a controlled manner in order to examine the influence of the properties of this synapse on the postsynaptic LP neuron. It was found that a static synapse with fixed parameters (such as strength and peak phase) across frequencies cannot result in a constant activity phase in the LP neuron. However, if the synaptic strength decreases and the peak phase is delayed as a function of frequency, the LP neuron can maintain a constant activity phase across a large range of frequencies. These dynamic changes in the strength and peak phase of the PD to LP synapse are consistent with the short-term plasticity properties previously reported for this synapse. In the pyloric network, the follower neuron LP provides the sole transmitter-mediated feedback to the pacemaker neurons. To understand the role of this synapse in network stability, this synapse was blocked and replaced by an artificial synapse using the dynamic clamp technique. Different parameters of the artificial synapse, including strength, peak phase, duration and onset phase were found to affect the pyloric cycle period. The most effective parameters that influence cycle period were the synaptic duration and its onset phase. Overall this study demonstrated that both the intrinsic properties of individual neurons and the dynamic properties of the synapses are essential in producing stable activity phases in this oscillatory network. The insight obtained from this thesis can provide a general understanding of the contribution of intrinsic properties to neuronal activity phase and how short-term synaptic dynamics can act to promote phase maintenance in oscillatory networks