Topological Phase Transitions in Whole-Brain Dynamics Driven by Spatially Heterogeneous Receptor Gain Modulation: A Receptor-Constrained Dynamical Topology (RCDT) Hypothesis
Wang, H.
Show abstract
The mechanistic link between molecular pharmacology and global brain dynamics remains unresolved--a "scale bridge" problem central to consciousness research. We propose the Receptor-Constrained Dynamical Topology (RCDT) hypothesis: that the functional impact of neuromodulatory drugs propagates from local ligand-receptor binding events through spatially heterogeneous gain modulation to produce qualitative reorganizations of the whole-brain dynamical attractor. Using a biophysically grounded whole-brain model--Wilson- Cowan excitatory-inhibitory dynamics on structural connectivity with axonal delays--we implement pharmacology via gain modulation weighted by 5-HT2A receptor density (Be-liveau et al., 2017). Topological state-space analysis via Takens embedding and persistent homology reveals a mapping from molecular receptor distribution to the global topological manifold. We define ego dissolution operationally as a breakdown of low-dimensional Betti-1 stability: a transition from a single dominant 1-cycle (constrained dynamics) to fragmented or higher-dimensional topological structure. The RCDT hypothesis is explicitly falsifiable: receptor-shuffling controls and concentration-topology response curves provide clear failure criteria. This work establishes a formal framework for bridging pharmacology, dynamics, and topology without invoking phenomenological experience as a premise.
Matching journals
The top 3 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Capturing the emergent dynamical structure in biophysical neural models 97%
- Robust switches in thalamic network activity require a timescale separation between sodium and T-type calcium channel activations 96%
- Homeostatic control of synaptic rewiring in recurrent networks induces the formation of stable memory engrams 95%
Similar papers in this journal
Similar papers in this journal
Similar papers in this journal
- A prefrontal network model operating near steady and oscillatory states links spike desynchronization and synaptic deficits in schizophrenia 96%
- A General Framework for Characterizing Optimal Communication in Brain Networks 95%
- Learning predictive cognitive maps with spiking neurons during behaviour and replays. 95%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.