The Quantum Mechanics and the Topology of Conscions
Aug 23, 2025
Sungchul Ji, Ph.D. (with ChatGPT assistance) Emeritus Professor of Theoretical Cell Biology Ernest Mario School of Pharmacy, Rutgers University, Piscataway, NJ
1. Introduction
We propose that each discrete “now” of consciousness—a conscion—is a dissipative saddle-like brain state shaped jointly by quantum–neural fast–slow dynamics and the topology of a mixed-curvature surface (MCS) (see Figure 1) [1]. The biological engine IRVSE (Iterative Reproduction with Variation and Selection by Environment) acts as a measurement-like channel that stabilizes one pattern at a time. Conscions are not static equilibrium minima but finite-lifetime dissipative structures [2], akin to transition states [3] in chemical kinetics, whose existence depends on continuous metabolic free energy dissipation. The Conscion Operator formalizes this selection process, bridging quantum measurement, metastability, geometry, and broader cosmological principles.
2. From Stream to Frames
Experience feels continuous, yet neuroscience reveals discreteness: EEG/MEG microstates, attentional blinks, binocular rivalry, and rapid perceptual switches. We model each unit as a conscion [4]:
Conscion = short-lived, dissipative structure coherent enough to guide action.
Rather than a smooth stream, consciousness advances as a sequence of finite-lifetime saddle states.
3. Fast–Slow Quantum–Neural Coupling
Fast processes: molecular events—enzymatic rearrangements, ion channel dynamics—operate at quantum and sub-millisecond timescales.
Slow processes: neural assemblies, oscillatory coordination, perception–action loops evolve over tens to hundreds of milliseconds.
Pre-fit Principle: Fast events condition slow dynamics before the latter fully reorganize (a quantum-to-neural “measurement-like” bridge) [5, 6, 7].
Thus, molecular physics shapes which neural patterns stabilize into conscions—without requiring long-lived macroscopic quantum states.
4. IRVSE: The Selection Engine
IRVSE = Iterative Reproduction with Variation and Selection by Environment.
Steps:
Reproduce candidate patterns (familiar motifs).
Vary them (via noise, novelty, prediction errors).
Select one pattern, biased by context (task, attention, arousal, bodily state).
This iterative mechanism ensures only one conscion stabilizes at a time.
5. The Conscion Operator
Like a quantum measurement observable:
Question posed: “Which neural pattern best fits body, world, and task right now?”
Answers: eigenstates of the Conscion Operator = candidate conscions.
Selection: IRVSE implements the measurement channel that collapses many potentials into one realized conscion.
But crucially, conscions are not equilibrium minima. They are saddle-based dissipative states [2] stabilized for finite times by metabolic free energy, then dissolved as the system transitions across saddles to the next state.
6. Topology: Mixed-Curvature Surface (MCS)
We represent the brain’s dynamic landscape as an MCS, combining:
Concave regions (K > 0): fast, reversible quantum transitions.
Convex regions (K < 0): slow, irreversible neuronal transitions.
Saddle point (intersection of convex and concave lines): a finite-lifetime dissipative node where fast and slow processes couple.
Transition = leaving one saddle to enter another, mediated by IRVSE (see Figure 2).
Thus, the flow of consciousness = a chain of finite-lifetime dissipative saddles, not static minima.
7. Conscions as Dissipative Structures
Borrowing from Prigogine’s theory of dissipative structures [2], conscions require continuous energy throughput. They are:
Non-equilibrium: existing only while metabolic free energy flows.
Finite-lived: analogous to transition states [3] in chemical kinetics, with dwell times set by barrier heights and noise.
Coupling nodes: where fast reversible quantum processes and slow irreversible neural dynamics intersect (see Figure 1).
This view makes conscions engines of becoming, not passive equilibrium snapshots.
8. Case Study: Lifting a Cup
Conscions as dissipative saddle frames:
C1 perceive cup
C2 plan reach
C3 initiate reach
C4 pre-shape & grasp
C5 lift & stabilize
C6 adjust tilt/sip
Each corresponds to a finite-lifetime saddle stabilized by free energy flow. Transitions occur as IRVSE (see Figure 2) selects the next saddle.
9. Predictions
Plateaus with jumps: Measurable in EEG/MEG as microstates.
Finite dwell times: Conscions persist as long as metabolic free energy sustains the saddle.
Contextual shifts: Task/goals tilt the saddle dynamics, biasing which conscion stabilizes.
Fast–slow signatures: Perturbations at molecular or network scales destabilize saddle lifetimes.
Energy–information trade-offs: Under fatigue, dwell times shorten, producing noisier transitions.
10. Illustrative Figures
Figure 1. Quantum–Topological Landscape of Conscions
Conscions are modeled as dissipative saddle states on a mixed-curvature surface. Fast quantum/ molecular events (along concave surfaces, green line) pre-condition slower neural assemblies(along convex surfaces, red line) (pre-fit principle) [5], and IRVSE (see Figure 2 below) stabilizes one finite-lifetime saddle through the Conscion Operator. A saddle has both a minimum (along concave curve, green line) and a maximum (along a convex curve, red line) at the saddle point where the concave and the convex lines intersect. A minimum is ‘stable’ while a maximum is ‘unstable’, making the saddle point ‘metastable’. The metastability of a saddle affords its environment an opportunity for decision-making. Therefore, a saddle may be a prerequisite for decision-making.
Figure 2. Conscion Channel as Measurement-Like Process
IRVSE acts as a measurement channel that maps evolving brain states into discrete conscions. Conscions are not equilibrium minima but finite-lifetime dissipative saddles stabilized by energy flow.
Figure 3. Fast–Slow Coupling via the Franck–Condon Principle
The generalized Franck–Condon principle explains how metastable neural assemblies (slow) mediate between fast molecular input and output signals. When the “right” assembly forms, it couples the two fast processes, sustaining a dissipative conscion state.
Figure 4. Geometry of Reality and the Operation of IRVSE
The Geometry of Reality (GOR) [8] situates IRVSE as a five-step geometric engine driving self-knowing systems. Gnergitons—tokens of information, energy, and spirit—operate as dissipative saddle states linking the gnergic universe with consciousness. There are two commutative triangles embedded in GOR: (i) IRVSE (2) followed by IRVSE (1) leads to the same result as IRVSE (3), which is postulated to represent an evolution, and (ii) Mapping L followed by IRVSE (3) leads to the same result as IRVSE (1), which is postulated to represent Self-Knowledge [11].
Figure 5. The Shillongator model of the Universe: A Self-Knowing Universe
11. Extending the Framework: Monad vs. Gnergiton
The integration of Leibniz’s Monadology [11] with Ji’s Gnergitonics [9] shows a deep philosophical resonance:
Monads (1714) [9]: indivisible, immaterial units reflecting the universe through pre-established harmony.
Gnergitons (2024) [9]: triadic, evolving, dissipative saddle-tokens of Information, Energy, and Spirit, operating via IRVSE within the Geometry of Reality.
Using the 5D Comparative Analysis (5VCA) [9], we see monads as timeless structural types and gnergitons as temporal evolving tokens. Both embody the triadic PSGIT principle. The comparison suggests a Monad–Gnergiton Continuum: monads as proto-gnergitons, and gnergitons as dissipative engines of becoming.
12. Anesthetics as Evidence for the Quantum–Neural Bridge
Two classes of anesthetics illustrate the dual vulnerability of consciousness [10]:
Fast, molecular-level anesthetics disrupt quantum pre-fit processes (enzymes, ion channels).
Slow, network-level anesthetics dismantle neural assemblies and oscillatory synchrony.
Both prevent finite-lifetime saddle states (conscions) from forming, showing that disrupting either end of the fast–slow coupling abolishes consciousness.
One-Sentence Takeaway
Consciousness consists of dissipative saddle structures—finite-lifetime states sustained by metabolic free energy, arising where fast quantum concavity and slow neuronal convexity intersect [Figure 1]. IRVSE and the Conscion Operator stabilize these saddles, linking Prigogine’s dissipative structures [2] to the geometry of reality [8; 13, Figure 5] and to the Monad–Gnergiton continuum [9].
