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The New Jersey Institute of Technology's
Electronic Theses & Dissertations Project

Title: Biophysical mechanisms of frequency-dependence and its neuromodulation in neurons in oscillatory networks
Author: Fox, David Michael
View Online: njit-etd2017-125
(xiv, 147 pages ~ 6.6 MB pdf)
Department: Federated Biological Sciences Department of NJIT and Rutgers-Newark
Degree: Doctor of Philosophy
Program: Biology
Document Type: Dissertation
Advisory Committee: Nadim, Farzan (Committee co-chair)
Rotstein, Horacio G. (Committee co-chair)
Haspel, Gal (Committee member)
Golowasch, Jorge P. (Committee member)
Nusbaum, Michael Philip (Committee member)
Date: 2017-12
Keywords: Resonance
Oscillation
Neuromodulation
Modeling
Optimization
Stomatogastric
Availability: Unrestricted
Abstract:

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.


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