Welcome to PituitaryBaselineStates , a simulation toolkit designed to explore the hidden rhythms of the pituitary gland. This project dives into how aperiodic signal components shape spontaneous activity and contribute to baseline hormonal dynamics.
These simulations support ideas presented in our early career perspective, which introduces a novel framework to evaluate baseline activation in the pituitary gland.
📄 Manuscript link coming soon — currently under revision.
While the pituitary’s responsiveness to external hormonal cues is well understood, its spontaneous, intrinsic activity remains underexplored, yet may hold the key to understanding hormonal adaptability and internal homeostasis.
In our perspective, we propose that:
Baseline pituitary activity emerges from the delicate interplay between excitatory and inhibitory signals, stochastic fluctuations, and feedback regulation — forming a flexible and self-regulated network.
We explore:
- Functional E:I balance in spontaneous pituitary states
- The role of aperiodic signal components in measuring baseline activation
- How information theory metrics (like entropy or mutual information) can reveal deeper signal complexity
- Novel computational models that simulate the structural and functional plasticity of pituitary networks
Here’s what you’ll find inside this repository:
🔄 Adapted from:
Conversion of Spikers to Bursters in Pituitary Cell Networks: Is it Better to Disperse for Maximum Exposure or Circle the Wagons?
by Mehran Fazli & Richard Bertram, PLOS Computational Biology, January 2024.
DOI: 10.1371/journal.pcbi.1011811
We extended their model by adding a randomized connectivity matrix that allows you to vary the proportion of bursting (burster-type) cells and observe how this affects synthesized electrical and calcium signals.
🧩 Want to understand how population density shifts influence pituitary dynamics? Start here.
Simulates two distinct cell-cell interaction patterns observed in pituitary networks:
- 🧭 Synchronous-only signals with low aperiodicity
- 🎭 Mixed-mode signals showing both synchronous and asynchronous interactions
These patterns reflect different organizational strategies and dynamic states within the gland. They're particularly useful when testing hypotheses about network coherence and signal heterogeneity.
This is your lab for signal decomposition:
- Explore the balance between periodic (oscillatory) and aperiodic (noise-like) components
- Test how different aperiodic exponent values affect the dynamics of small networks (default: 5 cells)
- Visualize how signal shape shifts with changes in stochastic background activity
🎛 Think of this as a sandbox to understand complexity and connectivity in endocrine microcircuits.
Have a question? Want to collaborate or contribute? I'm always open to feedback, new ideas, and interdisciplinary dialogue.
📫 Contact
Happy exploring