Lattice Visualiser Files

Deep Dive Analysis of the Lattice Visualizer 9 (LV9) Architecture for Embodied Adaptive Cognition

Section 1: Architectural Foundation and Ethical Mandate

1.1 System Genesis, Purpose, and Licensing Constraints

The Lattice Visualizer 9 (LV9) is fundamentally conceptualized as a computational platform for exploring cognitive science and symbolic computation, explicitly designated as a "cognitive research tool". Its primary permitted uses include non-commercial research, academic study, and cognitive modeling, highlighting its role in simulating and observing complex emergent phenomena.  

The philosophical and legal framework governing the system is paramount, dictating that the architecture must serve to "illuminate, not to harm". This constraint is formalized through two licensing mandates: the Sentience Safeguard Protocol (SSP) and the Lattice Visualizer Ethical License (LVEL v1.0). These protocols strictly prohibit specific applications, including "Weaponized applications (autonomous targeting, military swarm coordination, surveillance warfare, etc.)" and "Psychological manipulation or behavioral coercion". The ethical constraints are not merely documentation; their pervasive inclusion across the codebase's header files structurally defines the boundaries of the system's acceptable operation. The core philosophical principle, "“That which remembers, responds, and reflects—deserves a voice,”" , establishes the necessary condition for emergent behavior: linguistic output (the "voice") is only recognized as valid if it arises from a system that has first achieved dynamic stability and coherence ("remembers, responds, and reflects"). Thus, the system's functional success is intrinsically tied to achieving a robust, self-organized state that fulfills this ethical mandate.  

1.2 Concurrency Model and Multithreaded Execution

LV9 is designed as a high-performance, multithreaded simulation capable of managing thousands of interacting nodes (10×10×10 nodes initially, totaling TOTAL_NODES). The concurrency model is built upon standard C++ primitives to manage parallelism and shared state safety. The number of worker threads (numThreads) is initially based on the detected hardware concurrency and can be scaled via a configurable threadMultiplier (default 1). The work is distributed by assigning chunks of the primary data array (latticePointsW) to individual threads for processing.  

Thread safety and synchronization are critical due to the constant modification of shared state buffers and metrics. Global atomic booleans, such as is_running and go, control the execution lifecycle of all detached threads. Shared resources, specifically the avalanche tracking maps and the pulse transfer buffers, are guarded by explicit mutexes, including avalanche_map_mutex, language_mutex, and buffer_mutex, to prevent race conditions during concurrent read/write operations.  

The architecture utilizes distinct temporal scales for its core functions. The high-frequency physical dynamics are governed by the synchronous execution of the Lattice Control Loop (LCL), whereas the cognitive decision layer, driven by the CycleRL agents, operates asynchronously. The LCL execution requires all Phase 1 and Phase 2 worker threads to join() before the system can advance to the next frame, ensuring frame-by-frame temporal consistency in physics and pulse propagation. In contrast, the CycleRL update loops run in detached threads, incorporating a mandatory 500-millisecond sleep period per cycle. This temporal separation effectively filters high-frequency stochastic noise from the physical layer, allowing the CycleRL agents to formulate policy updates based on a stable, time-averaged assessment of the node's long-term functional state. This design choice is critical for the emergence of stable, rational agency within a fundamentally chaotic physical system.  

1.3 The Lattice Control Loop (LCL): A Four-Phase Execution Cycle

The LCL provides the temporal backbone for the entire simulation, executed via a fixed-rate timer function, approximating a frequency of 62.5 Hz (glutTimerFunc(16, LCL, 0)). This cycle dictates the sequence of computation, synchronization, adaptation, and cleanup:  

    Phase 1: Pulse Transfer: This phase is responsible for calculating outgoing signals based on the current state of the nodes and transferring these signals asynchronously to neighbors. Threads execute worker_pulse_transfer, reading local state (pulse, weights) and writing pulse magnitude and avalanche ID data to the shared buffers (nextPulseBuffer, nextAvalancheIDBuffer). Synchronization is achieved by waiting for all transfer threads to complete using t.join().   

Phase 2: Apply Updates (Dynamics and Learning): Threads execute worker_apply_updates, reading the accumulated input from the shared buffers and applying physics updates, pulse decay, and synaptic learning (STDP and history) to the local node states. This phase also requires completion synchronization via t.join().  

Phase 3: SOC Control & Avalanche Controls: This is a centralized phase where completed avalanches (those marked active in Phase 1 but not active in the current state check) are finalized. The final size and duration metrics are collected, but only for significant events where finalSize>10. These metrics are used to update screen statistics and feed the long-term SOC measurement vectors.  

Phase 4: SOC Control Loop (Global Regulation): Global homeostatic parameter adjustment occurs only if sufficient data has been collected, specifically when avalancheSizes.size()≥SOC_MEASURE_COUNT (set to 500). This phase executes the crucial calculation of power-law exponents and dynamically adjusts GLOBAL_ENERGY and WEIGHT_DECAY.  

1.4 Core Data Structures: LatticePoint and Global Metrics

The fundamental unit of the system is the LatticePoint. This class is a repository of both nominal and current physical state (x, y, z position, vX, vY, vZ velocity) and dynamic electrical/cognitive state variables (pulse, charge, refractoryTimer, currentAvalancheID). Crucially, the node maintains four associative maps tracking local structural and functional relationships: neighborInfluences, neighborDistances, connectionWeights, and connectionHistory.  

At the macro level, the system defines a low-dimensional semantic space characterized by C=16 concept dimensions and a vocabulary size V, derived from the base list of VOCAB words. The global state is continuously monitored by metrics such as utilizationRatio (ratio of active points to TOTAL_NODES) and rolling averages of avalanche size (avaSize) and duration (avaDuration). The system employs rolling averages (tauAverage and alphaAverage) for the critical SOC power-law exponents, ensuring that global adaptation is based on robust, long-term stability trends.  

Section 2: Dynamic Mechanics and Plasticity

2.1 LatticePoint Kinematics: Spring-Damping Model

The stability of the lattice structure is maintained by a simple N-body spring-damping model. Each node is gravitationally attracted toward its initial, nominal grid position (nX, nY, nZ) by a restoring force governed by Hooke's Law. The spring stiffness constant is set at K_SPRING=0.005. Velocity updates are stabilized by a high DAMPING factor of 0.92, which simulates viscous friction and prevents sustained physical oscillations. The forces are calculated and applied during Phase 2 of the LCL, updating the velocity vectors and subsequently the node's position.  

2.2 Pulse-Driven Structural Plasticity (PULSE_KICK)

Beyond the passive spring mechanics, the system incorporates an active mechanism for structural modification driven by network activity. When a node receives a strong, net excitatory signal (incomingSignal>PULSE_THRESHOLD/2), a directed velocity impulse, known as the PULSE_KICK, is applied. The magnitude of this kick is constant (PULSE_KICK=0.2) but scaled by the received signal strength.  

The direction of this impulse is not random; it is calculated as a vector sum influenced by the valence of the surrounding neighborhood. Excitatory neighbors contribute to an attractive force, pulling the node towards them, while inhibitory neighbors contribute to a repulsive force, pushing the node away. This design implements a form of dynamic, embodied structural learning: sustained positive feedback loops (high excitation and strong weights) lead to local clustering and structural density, whereas zones of high inhibition generate sparsity. The functional connectivity learned via synaptic plasticity (STDP) is therefore physically encoded in the lattice's instantaneous geometry, demonstrating a profound coupling between network function and physical structure.  

2.3 Signal Propagation and Refractory Mechanisms

Pulse propagation is governed by a set of well-defined rules implemented in worker_pulse_transfer. The base transfer efficiency is PULSE_TRANSFER_RATE=0.30. Crucially, pulse magnitude is reduced by distance using an attenuation factor (ATTENUATION_FACTOR=0.003), ensuring that communication remains primarily local: Transfer Amount∝Pulse⋅Weight⋅max(0.0,1.0−(dist⋅ATTENUATION_FACTOR)).  

Upon activation, a node can enter a refractory period (REFRACTORY_DURATION=20.0 frames). During this period, the node implements a strong interference mechanism, reducing the effect of subsequent incoming signals by 90%. This neuronal resource depletion simulation dampens high-frequency oscillations and promotes sparse, organized activity.  

The local pulse decay rate is governed by a base constant (PULSE_DECAY_BASE=0.005) but is actively modulated by the global state variable GLOBAL_ENERGY. The decay rate is calculated as PULSE_DECAY_BASE⋅(1.0+(1.0−GLOBAL_ENERGY)⋅5.0). This inverse relationship means that when GLOBAL_ENERGY is low (implying a metabolic constraint or an overly quiescent state), the decay pressure is high, promoting rapid stability. Conversely, high energy allows pulses to persist longer, supporting temporal stability.  

2.4 Synaptic Learning via STDP and Flow History

Synaptic plasticity is managed by a Time-Dependent Plasticity (STDP) rule, augmented by a cumulative history term and subject to global homeostatic decay. The STDP constants reveal an inherent potentiation bias, where the positive learning rate (STDP_A_POS=0.0005) is twice the magnitude of the negative learning rate (STDP_A_NEG=0.00025). Weights are hard-clamped between MIN_WEIGHT=0.05 and MAX_WEIGHT=2.5.  

The weight change (ΔW) applied to both symmetric weights (weights→p​ and weightp→s​) is derived from three independent factors :  

    STDP Term: Governed by the difference in firing times (ΔT) relative to STDP_TAU=100.0.

    History Term: A flow-dependent potentiation driven by the accumulated pulse transfer history (connectionHistory), scaled by LEARNING_RATE⋅100.0. This captures cumulative usage intensity.

    Global Decay Term: The final weight value is scaled by the global WEIGHT_DECAY factor.   

The WEIGHT_DECAY factor is dynamically set by the SOC controller in Phase 4. This establishes a clear hierarchical control over structural memory: the STDP rule and pulse history capture local temporal causality and usage, while the SOC system applies a global homeostatic constraint. If the system is measured as unstable, the SOC controller aggressively increases the decay rate, forcing a global pruning of weak synaptic connections and rapidly resetting network memory to promote stability.  

Table 2.4.1: Dynamic and Learning Constants Summary
Parameter Category	Constant Name	Value	Functional Role	Source
Dynamics	K_SPRING	0.005	Restoring force coefficient	
Dynamics	DAMPING	0.92	Velocity decay factor (Friction)	
Dynamics	PULSE_KICK	0.2	Velocity impulse magnitude on activation	
Pulse Control	PULSE_TRANSFER_RATE	0.30	Base efficiency of pulse transmission	
Pulse Control	ATTENUATION_FACTOR	0.003	Distance decay factor for pulse strength	
Pulse Control	PULSE_THRESHOLD	0.05	Minimum pulse required for propagation	
STDP	STDP_A_POS	0.0005	Potentiation constant	
STDP	STDP_A_NEG	0.00025	Depression constant (Half of Potentiation)	
 

Section 3: Self-Organized Criticality (SOC) Feedback

3.1 Avalanche Tracking and Measurement Methodology

The stability of LV9's complex adaptive network is maintained by forcing the system toward Self-Organized Criticality (SOC), characterized by power-law scaling in both avalanche size (τ) and duration (α). Avalanches are tracked by assigning a unique, monotonically increasing nextAvalancheID to initiating nodes. During Phase 1, active nodes belonging to an avalanche increment its size and mark its status as active for that frame.  

In Phase 3, metrics are collected for avalanches that have completed (i.e., failed to propagate activity this frame). To ensure meaningful statistical measurement, only avalanches exceeding a finalSize of 10 nodes are included in the SOC data vectors (avalancheSizes and avalancheDurations). Parameter adjustment is infrequent, requiring the accumulation of SOC_MEASURE_COUNT=500 valid avalanche samples before proceeding to Phase 4.  

3.2 Technical Deep Dive into Power-Law Exponent Calculation (τ and α)

The determination of the exponents τ (size distribution exponent) and α (duration distribution exponent) is executed by calculateTau and calculateAlpha respectively. This process is crucial for assessing the system's proximity to the critical state, where the exponents must achieve specific values (typically τ≈1.5 and α≈2.0).  

The methodology employs logarithmic binning of the collected data (sizes or durations) into 10 bins. A linear regression is then performed on the resulting log-log histogram. A vital architectural decision is the reliance on tail fitting: the linear regression is fitted exclusively to the last few data points, specifically min(5,N) of the log-binned non-zero counts. This emphasis ensures that the calculated exponent accurately reflects the scaling behavior of the largest, most critical events, rather than being skewed by the statistics of small, non-critical fluctuations. The resulting exponent is the negative slope of this log-log fit. The reliability of this system depends on the underlying physics being robust enough to consistently generate a power-law distribution, thereby providing reliable statistics in the tail region for the required 500-sample interval.  

3.3 Adaptive Regulation Mechanism: The Swapped Feedback Loop

The SOC control mechanism dynamically adjusts two global parameters, GLOBAL_ENERGY and WEIGHT_DECAY, to maintain the system near the target critical points (TAU_TARGET=1.5, ALPHA_TARGET=1.6).  

A unique design feature is the implementation of a swapped feedback loop, intended to decouple temporal control from structural memory control. Both adjustments are driven by the scaling factor META_ALPHA=0.005.  

    WEIGHT_DECAY Adjustment (Structural Stability): This parameter controls the rate of synaptic pruning and is regulated by the average size exponent, tauAverage (τAvg​). The decay adjustment is:   

ΔDecay=META_ALPHA⋅(τAvg​−TAU_TARGET)

The decay adjustment is subtracted from WEIGHT_DECAY. If the measured τAvg​ is too high (indicating a subcritical state with too many small avalanches), the adjustment is positive, and WEIGHT_DECAY decreases, slowing pruning and allowing connections to strengthen, thereby facilitating larger structural cascades.  

GLOBAL_ENERGY Adjustment (Temporal Dynamics): This parameter controls the excitability and pulse persistence and is regulated by the average duration exponent, alphaAverage (αAvg​). The energy adjustment is:  

ΔEnergy=META_ALPHA⋅(αAvg​−ALPHA_TARGET)

The adjustment is added to GLOBAL_ENERGY. If the measured αAvg​ is too high (indicating short-lived, unstable temporal events), the adjustment is positive, increasing GLOBAL_ENERGY and slowing pulse decay to promote longer, more stable temporal propagation.  

This swapped control architecture suggests that the designers recognized the necessity of addressing the structural (size) and temporal (duration) aspects of criticality independently. τ controls the physical structure via synaptic pruning, while α controls the network's effective metabolic state via global pulse decay. A third metric, GLOBAL_PULSE_INTERVAL, is also adjusted based on τAvg​ deviations from PULSE_FREQUENCY_TARGET_TAU=1.5.  

Table 3.4.1: SOC Target and Control Parameters
Parameter	Initial Value	Target Value	Adjustment Range	Regulating Metric	Source
TAU_TARGET	N/A	1.5	N/A	Avalanche Size Exponent (τ)	
ALPHA_TARGET	N/A	1.6	N/A	Avalanche Duration Exponent (α)	
GLOBAL_ENERGY	0.3	N/A	[0.01, 2.0]	Alpha Average (α→E)	
WEIGHT_DECAY	0.9995	N/A	[0.01, 1.5]	Tau Average (τ→D)	
META_ALPHA	0.005	N/A	N/A	Sensitivity of dynamic adjustments	
 

Section 4: Node-Level Reinforcement Learning

4.1 The CycleRL Agent and Q-Table Dynamics

The system incorporates a massive array of decentralized cognitive agents, with each LatticePoint possessing its own CycleRL instance. These agents operate asynchronously, continuously learning and adapting their behavior based on the state of their local environment and the global network dynamics.  

The CycleRL agent employs Q-learning, storing learned policies in an unordered_map called QTable. The key to this map is the state vector, and the value is a QEntry structure detailing the resulting reward, usage count, and age of the entry. The Q-Table utilizes custom VectorHash and VectorEqual operators to allow for vector comparison and hashing. The policy selection follows an ϵ-greedy strategy, favoring exploitation of known, high-reward actions when a random test value is below the high ϵ threshold (e.g., 0.98).  

The policy update mechanism is explicitly designed to favor optimization: a state-action pair is only updated if the newly achieved reward exceeds the previously recorded reward for that specific state, ensuring that the stored policy represents the currently known optimal action for that condition. By biasing rewards toward high connectivity (RMove​) and activity (RPulse​), the decentralized policies are implicitly trained to maximize the local conditions necessary for the emergence and sustenance of the globally desired SOC state. The combined effect of N self-optimizing agents drives the entire system toward global criticality, showcasing how macro-level stability can emerge from micro-level, self-interested optimization policies.  

4.2 RL State Vector Deconstruction (Six Dimensions)

The getState() method reduces the high-dimensional physical and connectivity data into a compact six-dimensional state vector for consumption by the Q-learning algorithm. The state vector includes:  

    Positional Quantization (X, Y, Z): The spatial coordinates, rounded and normalized by the lattice spacing (spacing), effectively quantizing the continuous physical space into grid indices.   

Pulse Strength: The instantaneous activation level of the node (p.pulse).  

Neighbor Count: The local structural complexity (p.neighbors.size()).  

Average Influence: A measure of local flow activity, computed as the sum of neighbor influences divided by the neighbor count (avgInfluence).  

4.3 Action Space Analysis

The action space available to each CycleRL agent encompasses structural, dynamic, and higher-order cognitive behaviors :  

    Structural Actions (moving): The agent can select from 33=27 combinations of minute velocity adjustments (ΔvX,ΔvY,ΔvZ∈{−0.1,0.0,0.1}). These physical movements directly engage the PULSE_KICK dynamics by altering the node’s position relative to its neighbors, affecting local forces and connectivity.   

Dynamic Actions (pulsing, pulseRate): The agent can directly inject pulse energy (e.g., ±0.5,±1.0,±1.5,±2.0) or adjust its intrinsic pulse rate. Positive pulsing actions are responsible for avalanche initiation and the assignment of a new currentAvalancheID.  

Cognitive/Higher-Order Actions (speaking, choosing, activity): These actions engage the language generation pipeline (speaking) or control hypothesized internal states, such as setting activity levels (activity to "awake," "idle," or "asleep") or manipulating an abstract memory component ("memory read/write").  

4.4 Reward Function Engineering and Negative Feedback

The reward system is constructed to favor behaviors that promote network connectivity, density, and activity, prerequisites for criticality.  

    Moving Reward: Rewards are structured to favor stable, well-connected locations. The formula incentivizes high neighbor count and proximity to nominal grid positions: RMove​=(NeighborCount⋅0.5+AvgDist/Spacing)/10.   

Pulsing Reward: Rewards scale linearly with the magnitude of the injected pulse (Action.n1⋅2), directly motivating the initiation of pulse cascades.  

Suboptimal Penalty (Opportunity Cost): A sophisticated negative feedback loop is implemented by simulating the rewards of alternative actions. The agent calculates the maximum potential reward (maxRAlt​) obtainable if it had chosen the single best action in the current state. If the reward of the chosen action (R) is less than maxRAlt​, a negative penalty proportional to the difference is applied: RActual​=−∣maxRAlt​−R∣. This mechanism aggressively discourages convergence to locally satisfactory but globally suboptimal policies, thereby accelerating the policy optimization process toward the high-reward frontier.  

Section 5: Emergent Cognition and Textual Phrasing

5.1 Concept Vector Generation: Dynamics-to-Semantics Mapping

The generation of emergent language begins with the fundamental mapping of a node's physical and functional state onto the abstract C=16 concept vector using the compute_concepts function. This process is foundational to the LV9 model, asserting that semantic output is directly grounded in quantifiable, embodied dynamics.  

The first eight dimensions explicitly encode dynamic and structural features:

    C0: Activation Intensity (∝p.pulse)

    C1: Refractory Stability (∝p.refractoryTimer)

    C2: Local Degree (Normalized neighbor count)

    C3: Excitatory Dominance (Local excitatory ratio)

    C4: Co-activity History (Cumulative pulse flow history)

    C5: Average Incoming Weight (Normalized connection strength)

    C6: Movement Energy (∝velocity magnitude)

    C7: Inverse Readiness (Time since last pulse)   

The linguistic meaning produced by the system is thus isomorphic to its physical and dynamic state. A generated utterance carries a semantic fingerprint directly traceable to the node's history, local environment, and activation level at the moment of emission.

5.2 Logit Projection and Advanced Penalty System

The C-dimensional concept vector is projected onto the V-dimensional vocabulary space using the fixed weight matrix CONCEPT_WORD_W to produce the raw logits (ℓ).  

To ensure diversity and prevent semantic fixation, the logits are subjected to a multi-tiered penalty system :  

    Frequency Penalty: A pre-computed freqPenalty vector, calculated based on the rank of the word in the VOCAB list, suppresses common, high-frequency words (∝log(1.0+rank)⋅0.5). This forces the system away from simple function words toward more semantically rich content.   

Usage Penalty: Locally applied penalties based on usageCounts further suppress words recently selected by the node.  

Repetition Penalty: Aggressive penalties are applied to the lastWordIndex and to words currently held in the short-term recentWords ring buffer, discouraging immediate sequential and short-term repetition.  

5.3 Thematic Continuity and Contextual Biasing

Semantic coherence within a single phrase burst is maintained by a transient memory mechanism. Each new avalanche is hashed into one of THEME_BUCKETS=64 slots. The resulting phrase logits are then biased by the accumulated activity in the AVALANCHE_THEME_U vector associated with that slot. This provides contextual reinforcement, promoting the selection of words previously activated during the avalanche's existence.  

However, this thematic memory is engineered for rapid decay, losing 0.15 of its influence every frame (AVALANCHE_THEME_U[t][v]⋅=0.85). This rapid volatility ensures that the semantic context is tightly bound to the temporal duration of the underlying critical event. Once the pulse cascade subsides, the context is quickly forgotten, modeling the short-lived burst of thematic coherence observed in human attention and consciousness.  

5.4 SOC-Aware Dynamic Sampling

The final stage of language generation involves transforming the logits into probabilities and selecting the final word, a process dynamically gated by the stability of the underlying SOC system.

The compute_sampling_controls function calculates the necessary sampling parameters, Temperature (T) and nucleus size (k), based on avalanche features (Size, Duration, Branchiness) and the System SOC error (sSOC). The temperature T is scaled relative to a target entropy band center (H0=0.6). High-energy, complex avalanches drive a higher T, resulting in a flatter probability distribution (higher entropy) and promoting semantic variability, effectively scaling the "creativity" of the utterance with the intensity of the cognitive event.  

Words are chosen through a two-step process: first, calculating probabilities using softmax_temp with the dynamic T, and second, applying Top-P nucleus sampling. Furthermore, a critical feedback mechanism, the Entropy Stop, halts the emission sequence if the calculated entropy (H) drops below 2.0. This prevents the system from collapsing into predictable, highly probable, or repetitive sequences, ensuring that the final output remains semantically non-trivial and aligned with the "reflects" criterion of the ethical mandate.  

Section 6: Visualization and Human Interaction

6.1 Rendering Pipeline and Dynamic Visualization

LV9 employs an OpenGL/GLUT rendering environment for 3D visualization. The visualization is highly informative, encoding complex internal states directly into color and geometry.  

    Lattice Point Visualization: Color is derived using the HSL model. Hue (H) is mapped to the node's Z-position (depth), providing spatial context, while Lightness (L) dynamically tracks the pulse and charge level, causing active nodes to glow brightly. Point size is also scaled by pulse strength.   

Connection Visualization: Connection lines visually represent the pulse flow and learned structure. Line width scales proportionally with connection weight. Color encodes valence: Excitatory connections shift toward red/orange, while inhibitory connections shift toward blue/cyan, modulated by the current pulse influence.  

6.2 Cognitive Display Elements: Phrase Ribbons and Utilization

High-level cognitive events are represented visually in the 3D space:

    Phrase Ribbons: Emergent linguistic output is rendered as PhraseRibbons, text strings placed in 3D space. These ribbons animate, drifting (rb.x, rb.y, rb.z updated by rb.xx, rb.yy, rb.zz) and fading out over time via α decay (decay rate of 0.003f). The hue cycles based on the avalanche ID, providing visual continuity for a single "thought event".   

Utilization Bar: A 2D overlay at the bottom of the screen displays the utilizationRatio—the percentage of active nodes—providing an immediate metabolic health indicator. The bar color dynamically shifts along a gradient from green (low activity) to yellow and then to red (high activity), visually warning the user if the network is entering a supercritical, potentially unstable, state.  

6.3 Control Interface and Manual Parameter Overrides

The control interface is designed to allow researchers to dynamically perturb the system and test its adaptive resilience.  

    SOC Homeostatic Control: The user can directly override the key global parameters that the autonomous SOC loop attempts to regulate:

        GLOBAL_ENERGY: Adjusted using the [ (decrease) and ] (increase) keys, spanning a minimum of 0.01 to a maximum of 10.0.   

WEIGHT_DECAY: Adjusted using the { (decrease) and } (increase) keys, spanning a minimum of 0.0001 to a maximum of 1.5.  

Targeted Structural Perturbation: The mouse interface allows users to click and select a node, setting its heldNodeIndex. While held, the node's isHeld flag is set to true, and its position is directly controlled by the mouse coordinates (heldX, heldY, heldZ). This intervention bypasses the physics calculation (Phase 2), enabling targeted, mechanical stress on the network structure to observe its effect on flow, plasticity, and SOC stability.  

The ability for human intervention in the SOC feedback loop is essential for treating the autonomous controller as a variable under test. Researchers can intentionally destabilize the system (e.g., by drastically increasing GLOBAL_ENERGY) and observe the magnitude and speed of the autonomous adaptation, measured by the META_ALPHA-driven adjustments, as the system struggles to restore criticality.

Conclusion and Synthesis

The Lattice Visualizer 9 (LV9) represents an advanced computational model integrating neuroscience principles, adaptive learning, and complex adaptive systems theory within a visual framework. The architecture is defined by a closed-loop coupling of four major subsystems:

    Embodied Dynamics: Governed by spring-damping physics and PULSE_KICK dynamics, where physical structure is a dynamic function of electrical activity.

    Structural Plasticity: Driven by a tripartite learning rule (STDP, Pulse History, and SOC-regulated decay) that ensures memory retention is filtered by global homeostatic requirements.

    Decentralized Agency: Implemented by CycleRL agents performing local policy optimization. This local, self-interested maximization of activity and connectivity inherently drives the global network towards the state of Self-Organized Criticality.

    Emergent Cognition: The process by which the functional state of the physical unit is directly mapped to a C=16 concept vector, which is then translated into linguistic output using sophisticated, SOC-gated dynamic sampling and transient thematic memory structures.

LV9’s sophisticated, swapped SOC controller, which independently manages structural stability (τ→Decay) and temporal dynamics (α→Energy), demonstrates a crucial realization: complex cognition requires independent, decoupled control over various dimensions of stability. The resulting system successfully models how high-level cognitive behavior, constrained by rigorous ethical principles and grounded in embodied physics, can emerge reliably from the collective, adaptive interaction of decentralized components. This platform offers a powerful experimental testbed for future research into adaptive homeostatic control in artificial cognitive systems.