Author ORCID Identifier
0000-0002-4694-5734
Document Type
Dissertation
Date of Award
5-31-2025
Degree Name
Doctor of Philosophy in Biology - (Ph.D.)
Department
Federated Department of Biological Sciences
First Advisor
Farzan Nadim
Second Advisor
Horacio G. Rotstein
Third Advisor
Jorge P. Golowasch
Fourth Advisor
Victor Victorovich Matveev
Fifth Advisor
Astrid Prinz
Abstract
Neural systems can generate consistent outputs across a population despite substantial variability in the underlying components of individuals. This dissertation aims to identify mechanisms through which neuromodulation influences the relationship between parametric and output variability in neural systems. Through a combination of theoretical analysis, computational modeling, and data-driven approaches, the research addresses how excitatory neuromodulation can shape population-level activity variability and identifies key patterns that govern the production of consistent neural population output despite underlying parameter variability.
The theoretical foundation is established by considering how excitatory neuromodulation affects population variability in simplified neuronal models. Two fundamental patterns of variability reduction are identified: unbounded contraction, where population activity values converge independent of specific bounds, and bounded contraction, where the population approaches a shared limiting value. The dissertation explores different implementations of these mechanisms, showing that variability reduction can be both explicit (e.g., through fixed refractory periods) and implicit (emerging from the interaction of multiple cellular properties).
Bridging theory and experiments, the work incorporates experimental data to analyze how excitatory neuromodulation influences the variability of neuronal activity in the lateral pyloric (LP) neuron of the crustacean stomatogastric ganglion. Looking at multiple attributes of neuronal activity (frequency-current curves and rebound firing), the research shows how introducing excitatory neuromodulation through increasing the maximal conductance of an inward modulatory current can lead to reduction of activity variability.
Further, the dissertation leverages data-driven modeling to show how the combination of persistent (IMI) and transient (IMI-T) neuromodulator-activated currents can enable both stable baseline excitability and dynamic adaptation to changing network conditions. The calcium permeability of IMI-T creates an additional layer of burst regulation through calcium-dependent potassium current activation.
Circuit-level effects are examined through models of the pyloric circuit, demonstrating multiple mechanisms through which neuromodulation can regulate circuit variability. Analysis of frequency-dependent electrical coupling further reveals how network coordination can be maintained across varying conditions, enabled through neuromodulation. Finally, the challenges of parameter estimation in structurally degenerate systems are addressed using the Lambda-Omega model as a canonical example.
A key theme across all chapters is the importance of multiple, complementary mechanisms in maintaining reliable neural function. While biological systems often show substantial variation in underlying parameters, they can nevertheless produce reliable output patterns through mechanisms that reduce the mapping of parameter variability onto activity variability. These findings advance our understanding of how neuromodulation contributes to reliable neural function despite parameter variability and provide insight into fundamental aspects of neural circuit organization.
Recommended Citation
Itani, Omar, "The role of excitatory neuromodulation in managing variability of neural system output" (2025). Dissertations. 1834.
https://digitalcommons.njit.edu/dissertations/1834
