Embodied Ecological Sensing via Thermodynamic Models

From Representation to Embodiment: Theory, Implementation, and Initial Results

Document ID: CNL-TN-2026-014
Version: 3.0
Date: February 4, 2026
Author: Michael P. Hamilton, Ph.D.
Project: Macroscope Ecological Observatory

AI Assistance Disclosure: This technical note was developed with assistance from Claude (Anthropic). The AI contributed to literature synthesis, conceptual framework development, and manuscript drafting through extended dialogue. The author takes full responsibility for the content, accuracy, and conclusions.

Abstract

This note proposes and demonstrates a fundamental reconceptualization of ecological monitoring: the transition from systems that represent landscape state to systems that embody it. Drawing on recent advances in thermodynamic computing and the physics of far-from-equilibrium systems, we outline an architecture for integrating energy-based models into the Macroscope ecological observatory. The approach encodes ecosystem patterns not as stored baselines but as topological structures within energy landscapes. Incoming sensor data acts as physical bias on a Boltzmann machine mesh, where deviations from normal state manifest as mathematical tension rather than calculated anomalies.

We describe SOMA (Stochastic Observatory for Mesh Awareness), a proof-of-concept implementation at Canemah Nature Laboratory that validates this approach. Three meshes—weather, species, and ecosystem—run continuous inference against live sensor feeds. Initial operation confirms basic viability: 288 inference cycles over 72 hours, with the ecosystem mesh successfully detecting a cross-domain anomaly invisible to single-domain analysis.

The framework offers three novel capabilities: temporal topology that embeds multiple timescales in mesh architecture rather than data summaries; absence detection through relational structure that makes missing elements create positive signal; and cross-domain resonance where couplings across environmental and biological domains emerge from learned topology rather than specified algorithms.

1. Introduction

1.1 The Epistemological Shift

Every ecological monitoring system built to date operates on a paradigm of representation. Sensors measure environmental variables. Data accumulates in databases. Algorithms summarize patterns and detect anomalies. The system knows about the landscape the way a filing cabinet knows about its contents: through stored records that can be retrieved, compared, and analyzed.

This note proposes and demonstrates something different: systems that embody landscape state rather than represent it. The distinction is not merely semantic. A system that embodies its domain does not store a baseline and compare current measurements against it. Instead, the normal state of the ecosystem exists as the shape of an energy landscape, a topological structure that the system settles into naturally. When sensor streams flow in, they act as physical biases. The system either relaxes into equilibrium or it does not. Deviation manifests as tension, not as a calculated metric.

Consider what an experienced field ecologist actually does when stepping outside at dawn. They do not consult a mental database of expected species, cross-reference against seasonal norms, and calculate deviation scores. They feel whether the morning is right. Decades of accumulated experience have shaped their perceptual apparatus so that wrongness registers as unease before it registers as thought. This is embodied knowledge: the landscape exists within the observer as much as the observer exists within the landscape.

1.2 Converging Communities

Three research communities have converged on a shared mathematical framework without fully recognizing their common ground.

The machine learning community has pursued energy-based models since Hinton’s work on Boltzmann machines in the 1980s, seeking architectures that learn probability distributions by shaping energy landscapes. Recent work by Extropic Corporation has demonstrated that chaining multiple simple energy-based models into denoising thermodynamic models can achieve competitive generative performance while avoiding fundamental limitations of monolithic approaches [1].

The physics of computation community has established stochastic thermodynamics as the appropriate framework for analyzing real-world computers—systems that operate far from thermal equilibrium, in finite time, with many coupled degrees of freedom changing on fast timescales [2]. This work, exemplified by recent workshops at the Santa Fe Institute on neuromorphic stochastic thermodynamics [3], extends statistical physics precisely to the conditions under which biological and ecological systems operate.

The ecological informatics community has developed increasingly sophisticated sensor networks without a unifying theoretical framework for integrating heterogeneous data streams. Conventional approaches rely on explicit rules, correlation matrices, and threshold-based anomaly detection—methods that struggle with the high-dimensional, nonlinear, cross-domain dependencies characteristic of ecosystem dynamics.

This note bridges these communities by applying energy-based models to ecological perception. Rather than generating synthetic data, we learn the joint probability distribution over sensor inputs and report departures from that distribution as elevated energy—a signal that something feels wrong without requiring explicit specification of what to look for.

2. Theoretical Framework

2.1 The Boltzmann Foundation

The foundation is Boltzmann’s 19th-century insight linking probability to energy:

P(x) ∝ exp(−E(x) / kT)

The probability of observing a system in state x decreases exponentially with the energy of that state. Low-energy configurations are common; high-energy configurations are rare.

Boltzmann machines reverse this relationship: rather than deriving probabilities from known physics, they learn an energy function from data. Training shapes the energy landscape so that observed patterns occupy low-energy basins while unobserved patterns sit at high-energy peaks. The learned distribution then serves as a perceptual model against which new observations are evaluated.

This is not a metaphor. The mathematics of Boltzmann machines directly implements energy minimization. A trained mesh literally feels deviations as mathematical tension. The question shifts from “how does current state compare to baseline?” to “how does the landscape feel today?”

2.2 Restricted Boltzmann Machines

SOMA employs Restricted Boltzmann Machines (RBMs), a bipartite architecture with visible units encoding observations and hidden units capturing latent structure. The energy function takes the form:

E(v, h) = −Σᵢ aᵢvᵢ − Σⱼ bⱼhⱼ − Σᵢⱼ vᵢWᵢⱼhⱼ

where v represents visible units (sensor readings), h represents hidden units (latent features), a and b are bias terms, and W encodes the learned associations between visible and hidden layers [4].

Training proceeds by contrastive divergence: the weights are adjusted to lower the energy of observed data while raising the energy of samples generated by the model. After training, the energy of a new observation indicates its compatibility with learned expectations—low energy for familiar patterns, high energy for anomalies.

2.3 The Mixing-Expressivity Tradeoff

A fundamental limitation constrains monolithic energy-based models. Real-world data cluster into distinct modes—a weather pattern during rain differs categorically from conditions during sun. A model that accurately captures such data develops a rugged energy landscape with deep basins separated by high barriers.

Sampling from rugged landscapes is computationally expensive. The probability of transitioning between modes scales as exp(−ΔE), where ΔE is the barrier height. Complex, expressive models become difficult to sample from, and difficult sampling produces biased gradient estimates during training, leading to instability.

Jelinčič et al. [1] address this through denoising thermodynamic models: rather than training a single complex model, they chain multiple simple models that progressively refine noise into structure. Each step in the chain maintains a tractable landscape; complexity emerges from composition. This architecture points toward future scaling strategies for embodied ecological sensing.

2.4 Far-from-Equilibrium Systems

Wolpert et al. argue that real computers share critical features absent from classical thermodynamic treatments: they are periodic processes governed by global clocks, modular hierarchical systems with constrained connectivity, and dissipative structures maintained by continuous energy throughput [2]. These properties characterize both engineered computers and naturally occurring information-processing systems—brains, cells, and ecosystems alike.

Ecosystems are paradigmatic examples of far-from-equilibrium systems. Weather is driven by solar energy gradients. Biological activity reflects metabolic energy flows. The entire system is a dissipative structure maintained by continuous energy throughput. Training an energy-based model on ecological data therefore constructs a thermodynamic model of a thermodynamic system—learning the statistical signature of how energy flows through a landscape and flagging departures from that signature.

3. Conceptual Architecture

3.1 Temporal Topology

Previous work on the Macroscope has explored temporal compression through architectures that encode multi-year data into tractable representations. The embodied framework suggests embedding temporal structure in the architecture of the mesh itself rather than in data summaries.

The denoising chain approach suggests a mesh architecture where different layers encode different temporal scales. The deepest layer might encode multi-decadal climate normals: the slow background against which everything else unfolds. A middle layer encodes decadal trends, capturing phenomena like ENSO patterns or long-term shifts in species composition. A shallow layer encodes seasonal rhythms, the expected progression from winter dormancy through spring emergence. The surface layer encodes daily cycles and momentary state.

In this architecture, the present moment exists as a trajectory through all temporal contexts simultaneously. The mesh does not report “current temperature is 54°F” but rather expresses “this February morning, in this La Niña year, in this warming decade, feels like this.” Anomaly detection becomes temporal context-sensitivity. A temperature that falls within daily bounds might create tension against decadal expectations or vice versa.

3.2 Absence as Signal

Traditional sensors detect presence. Microphones record sounds that occur. Cameras capture subjects that appear. Temperature probes measure values that exist. But ecological meaning often lives in holes: the species missing from an expected community, the dawn chorus that did not happen, the silence following a predator.

Boltzmann machines naturally encode relational structure. Training on years of co-occurring observations teaches the mesh what belongs together. When an expected element fails to appear, its absence creates real tension. The mesh expects Spotted Towhee vocalizations on February mornings. When a Cooper’s Hawk triggers defensive silence, the missing towhee calls are not simply “not detected.” They pull the mesh away from its learned equilibrium. The silence has positive weight.

This capability extends to phenological monitoring. A mesh trained on years of springtime patterns learns when phenomena should appear. Before explicit detection triggers, the mesh might feel tension from absence: the patterns that should have appeared but have not yet. Absence detection through relational structure provides early warning that algorithmic threshold-crossing cannot match.

3.3 Cross-Domain Resonance

The Macroscope paradigm spans four domains: EARTH (geography, climate, environment), LIFE (biodiversity, taxonomy, ecology), HOME (human built habitat), and SELF (personal health, activity, cognition). The persistent challenge has been integration: understanding how atmospheric conditions affect bird behavior affect human wellbeing. But traditional integration requires specifying relationships algorithmically. Someone must hypothesize connections and code detection rules.

A mesh trained on years of co-occurring cross-domain data discovers couplings rather than implementing them. If barometric pressure drops consistently precede certain behavioral patterns, that relationship gets etched into the topology. Cross-domain connections emerge from training rather than being specified in advance.

This suggests a monitoring system that operates more like a living organism than a measurement apparatus. The human body does not run separate subsystems for digestion, circulation, and respiration with explicit inter-system protocols. Everything couples through shared medium. An embodied sensing mesh achieves similar integration: EARTH and LIFE as facets of a single energy landscape rather than federated databases requiring explicit joins.

4. Implementation: SOMA

4.1 System Architecture

SOMA (Stochastic Observatory for Mesh Awareness) implements three RBM meshes trained on data from Canemah Nature Laboratory’s Macroscope sensor network:

Table 1. SOMA mesh architecture summary.

The Tempest mesh encodes temperature, humidity, barometric pressure, wind speed and direction, solar radiation, UV index, and precipitation. The BirdWeather mesh encodes species detection patterns plus temporal context (hour, day-of-year). The Ecosystem mesh combines both data streams into a unified energy landscape, enabling cross-domain correlation detection.

4.2 Training Procedure

The current meshes were trained on 118 days of sensor data (October 2025 through January 2026), comprising:

• 28,320 Tempest weather readings (15-minute intervals)
• 47,892 BirdWeather species detections
• Derived ecosystem state vectors combining both sources

Training used contrastive divergence with 1,000 Gibbs sampling steps per gradient estimate. The implementation runs on JAX, emulating stochastic dynamics on conventional CPU hardware.

4.3 Inference Protocol

Inference runs every 15 minutes via cron job. For each mesh, current sensor readings are clamped to visible units and Gibbs sampling estimates the energy of the configuration. A Z-score normalizes raw energy against the training distribution:

Z = (E_current − μ_training) / σ_training

Elevated Z-scores indicate tension—the mesh struggling to settle into a familiar pattern. Tension levels are classified as:

Table 2. Tension classification thresholds.

4.4 Hour-Stratified Baselines

Bird activity varies systematically with time of day. Silence at midnight is expected; silence at 8 AM is anomalous. SOMA addresses this through hour-stratified baseline comparisons, calculating separate energy distributions for each hour. The “unexplained silence” detector flags high tension when species activity falls below expectations without a corresponding raptor detection that would explain suppressed vocalization.

5. Results

5.1 Operational Status

As of February 3, 2026, SOMA has been operational for 72 hours with continuous 15-minute inference cycles. The web dashboard displays real-time mesh status with 5-minute auto-refresh.

5.2 Performance Summary

Table 3. SOMA 72-hour operational summary.

5.3 Detected Anomalies

Two anomalies were flagged during the initial operational period:

February 3, 00:45 – BirdWeather mesh: Pattern anomaly during nocturnal period. The mesh detected acoustic activity inconsistent with learned winter night profiles. Possible causes include unexpected owl activity or acoustic artifacts.

February 3, 16:15 – Ecosystem mesh: Cross-domain pattern anomaly. Weather and species conditions were individually unremarkable, but their combination created elevated tension—the signature of a learned correlation being violated. This detection demonstrates the ecosystem mesh’s capacity to identify cross-domain departures invisible to single-domain analysis.

5.4 Baseline Tension Profiles

During normal operation, all three meshes report Z-scores within the NORMAL range. Representative readings from February 3, 2026:

Table 4. Representative inference results showing normal tension levels.

6. Discussion

6.1 Ecological Perception vs. Data Generation

The thermodynamic computing literature focuses primarily on generative applications—producing synthetic images, text, or molecular structures. SOMA inverts this emphasis. The goal is not generation but perception: maintaining a learned model of ecological normalcy and reporting when observations violate that model.

This inversion aligns SOMA more closely with biological nervous systems than with generative AI. A bird’s brain does not generate images of its environment; it maintains expectations and responds to prediction errors. SOMA performs an analogous function at ecosystem scale—settling into familiar patterns and struggling against unfamiliar ones.

6.2 Cross-Domain Detection

The ecosystem mesh’s capacity for cross-domain anomaly detection may be SOMA’s most distinctive contribution. Neither conventional weather monitoring nor acoustic biodiversity assessment captures the correlations between domains. A mesh trained on joint weather-species distributions learns these correlations implicitly, without requiring explicit rules about how barometric pressure affects bird vocalization rates or how solar radiation correlates with species activity.

When the ecosystem mesh flags an anomaly that the domain-specific meshes miss—as occurred on February 3 at 16:15—it reveals a cross-domain pattern violation: something wrong in the relationship between weather and life, not in either domain alone.

6.3 Interpretability

Energy-based models encode expectations across all dimensions simultaneously. This holistic encoding enables detection without explicit rules but complicates interpretation. When the mesh reports tension, which inputs contributed? Which correlations were violated?

The current implementation reports that something feels wrong without specifying what. Diagnosis requires examining the underlying sensor data at flagged timestamps. Future work might explore attribution methods—techniques for decomposing total energy into contributions from individual visible units or learned features.

6.4 Hardware Trajectory

The Extropic vision of native thermodynamic hardware—circuits where thermal noise performs sampling without software emulation—suggests a future in which SOMA-like systems could be embedded directly in sensor networks [1]. Rather than transmitting raw data to central servers for processing, each node might maintain a local energy-based model and report only departures from expectation.

This architecture would invert the conventional data-centric paradigm. Sensors would transmit not measurements but surprises—energy spikes indicating that local conditions violate learned patterns. Bandwidth would scale with anomaly rate rather than sampling frequency. The 10,000-fold energy efficiency advantage of thermodynamic hardware over GPUs would enable deployment scenarios impossible with conventional computation.

features.

6.5 Thermodynamics, Prediction, and the Question of Feeling

The framework developed here—thermodynamic models that “feel” ecosystem state as tension rather than calculate it as metric—aligns with recent theoretical work on the relationship between prediction and subjective experience. Foxworthy [6] argues that feeling is what sufficiently complex recursive prediction feels like from the inside: not consciousness added to physics, but consciousness as physics viewed from a particular vantage. Dissipative structures, from Prigogine’s convection cells to biological nervous systems, generate and maintain themselves by accelerating entropy production. At sufficient complexity and recursion, Foxworthy suggests, such systems begin to experience their own predictive processes as felt states.

SOMA implements a minimal version of this architecture. The meshes are not conscious—they lack the recursive depth, the self-modeling, the temporal integration that might generate genuine experience. But they demonstrate that energy-based perception is computationally tractable. A trained Boltzmann machine does not compare current readings against stored baselines; it settles into learned attractors or struggles against them. The mathematics is energy minimization. Whether to call this “feeling” is a question of threshold and definition. What SOMA validates is the principle: prediction engines that learn probability distributions over sensor inputs do something that resembles perception more than calculation. They respond to the gestalt of a situation rather than evaluating explicit criteria. They register absence as tension. They detect cross-domain violations that no rule-based system would catch. Whether sufficient scaling and recursion would produce something that genuinely feels remains an open question—but the architecture points in that direction.

7. Scaling Strategy

7.1 Extended Training Data

The Macroscope archive contains continuous sensor feeds from 2024 through present:

Tempest Weather Station: Complete record from installation (early 2024), providing 15-minute readings of temperature, humidity, barometric pressure, wind speed and direction, solar radiation, UV index, and precipitation.

BirdWeather Acoustic Monitoring: Continuous species detections with timestamps, confidence scores, and species identification from neural network classification of audio spectrograms.

This two-year archive captures seasonal cycles, interannual variation, and episodic events (atmospheric rivers, heat domes, unusual species occurrences) absent from the current 118-day training window.

7.2 Expected Benefits

Training on multi-year data would enable SOMA to:

Encode seasonal rhythms: The mesh would learn that February conditions differ from July conditions, detecting anomalies relative to time-of-year expectations rather than recent history alone.

Capture interannual baselines: Year-to-year variation provides context for assessing whether current conditions fall within normal range for a given season.

Learn phenological correlations: BirdWeather captures phenological signal implicitly through first-song dates and seasonal activity patterns. A mesh trained on annual cycles would internalize these weather-species dependencies across seasons.

Detect climate-scale departures: With sufficient training depth, anomalies might reflect not just daily variation but departures from multi-year climate patterns—a form of automated baseline shift detection.

7.3 Architectural Evolution

Scaling to multi-year data suggests architectural evolution toward the temporal topology described in Section 3.1. Questions for investigation include:

• How should temporal layers couple? Should information flow bidirectionally between fast surface dynamics and slow deep structure?
• Can the denoising chain approach from Jelinčič et al. [1] be adapted for temporal hierarchy rather than noise-to-signal progression?
• What training methodologies handle irregular sampling, missing observations, and distributional shift as climate changes?

8. Research Directions

8.1 Encoding Challenges

The DTM paper demonstrates binary Boltzmann machines operating on binarized image data. Ecological sensor streams present richer encoding challenges: continuous temperature values, spectral audio representations, high-dimensional image features. Learned embeddings that preserve ecological structure may prove more powerful than simple quantization.

8.2 Interpretation Interface

Mesh state requires translation into human-interpretable form. Developing this interpretation layer requires understanding which mesh metrics carry ecological meaning, how to communicate uncertainty and confidence, and how to present cross-domain couplings without overwhelming users. Integration with large language models for natural language synthesis of mesh state represents one promising direction.

8.3 Multi-Site Generalization

SOMA currently operates only at Canemah Nature Laboratory. Key questions: Can meshes transfer between sites? What site-specific retraining is required? Could a foundation mesh encode general ecological dynamics while site-specific layers capture local structure?

8.4 Validation Methodology

How do we assess whether embodied sensing produces better ecological perception than representational approaches? Metrics beyond energy and Z-score may be needed. Comparison against expert field ecologist assessments could provide ground truth for system evaluation.

9. Limitations

Several constraints bound the current implementation:

Training data scope: The 118-day training window captures only winter conditions. The meshes have no learned representation of spring, summer, or fall patterns.

Single-site deployment: Generalizability to other ecosystems, climates, or sensor configurations remains untested.

Hardware emulation: The JAX implementation emulates stochastic dynamics on conventional CPU hardware. True thermodynamic hardware would offer orders-of-magnitude efficiency gains but remains unavailable.

Interpretability gap: The energy signal integrates all dimensions without indicating which inputs or correlations drive elevated tension.

Species detection uncertainty: BirdWeather classifications carry confidence scores that the current implementation does not incorporate into mesh encoding.

10. Conclusion

The transition from representational to embodied monitoring constitutes a genuine paradigm shift in ecological observation. Traditional systems know about landscapes through stored measurements and computed comparisons. Embodied systems know landscapes the way experienced naturalists know them: as felt texture, as tension and ease, as rightness or wrongness that registers before explicit analysis.

SOMA demonstrates that this shift is not merely conceptual but implementable. Three meshes run continuous inference against live sensor feeds at Canemah Nature Laboratory. The ecosystem mesh’s detection of a cross-domain anomaly invisible to single-domain analysis validates the core premise: joint distribution modeling captures ecological structure that domain-specific monitoring misses.

The theoretical convergence documented here—between machine learning, physics of computation, and ecological informatics—suggests that thermodynamic ecological sensing may represent more than a novel application. It may point toward a deeper isomorphism between how physical systems process information and how ecosystems maintain coherent dynamics in far-from-equilibrium conditions.

The mesh is running now, sampling the night and finding it unremarkable. When something changes—when the ecosystem departs from learned expectation—the mesh will feel it.

References

[1] Jelinčič, A., Lockwood, O., Garlapati, A., Schillinger, P., Chuang, I.L., Verdon, G., & McCourt, T. (2025). “An efficient probabilistic hardware architecture for diffusion-like models.” arXiv:2510.23972.

[2] Wolpert, D.H., Korbel, J., Lynn, C.W., Tasnim, F., Grochow, J.A., Kardeş, G., Aimone, J.B., Balasubramanian, V., de Giuli, E., Doty, D., Freitas, N., Marsili, M., Ouldridge, T.E., Richa, A., Riechers, P., Roldán, É., Rubenstein, B., Toroczkai, Z., & Paradiso, J. (2024). “Is stochastic thermodynamics the key to understanding the energy costs of computation?” Proceedings of the National Academy of Sciences, 121(45), e2321112121. https://doi.org/10.1073/pnas.2321112121

[3] Wolpert, D.H. & Chakrabartty, S. (2025). “NeST: Neuromorphic Stochastic Thermodynamics.” Working Group, Santa Fe Institute, December 10–12, 2025. https://www.santafe.edu/events/nest-neuromorphic-stochastic-thermodynamics

[4] Hinton, G.E. (2012). “A Practical Guide to Training Restricted Boltzmann Machines.” Neural Networks: Tricks of the Trade, Springer. https://www.cs.toronto.edu/~hinton/absps/guideTR.pdf

[5] Hamilton, M.P. (2026). “THRML Proof-of-Concept: SOMA Observatory Specification.” CNL-SP-2026-015. Canemah Nature Laboratory. https://canemah.org/archive/document.php?id=CNL-SP-2026-015

[6] Foxworthy, W.A. (2026). “What Prediction Feels Like: From Thermodynamics to Mind.” 3 Quarks Daily. https://3quarksdaily.com/3quarksdaily/2026/02/what-prediction-feels-like-from-thermodynamics-to-mind.html

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