Mechanistic and Systems-Level Limitations of Animal Models in Human Neural Instability and Consciousness Research

Across the last four decades, several well-documented failures in biological and food-related systemshave demonstrated a recurring pattern: regulatory frameworks tend to respond to harm ratherthan anticipate it (Renn, 2008; Hutter, 2011). These events are not isolated incidents but manifestationsof structural characteristics inherent to complex supply chains, high-throughput environments,and legacy assumptions about biological safety. Their significance lies not in emotiveimpact but in analytical value. They provide a factual basis for understanding how systems behaveunder pressure, how risk is distributed, and how oversight mechanisms adapt only after criticalthresholds are crossed (European Commission, 2012; WOAH, 2019). Neural systems operate across multiple spatial and temporal scales, from ion channel kinetics tolarge-scale network dynamics. Physical constraints such as conduction velocity, metabolic cost,and noise propagation shape the fidelity of information transfer (Koch et al., 2016). In engineeredsystems, these constraints are explicitly modelled; in biological systems, they must be inferredfrom observation (Dehaene & Changeux, 2011). When signals traverse multiple layers of organisation,small discrepancies can accumulate, producing emergent behaviours that are not predictablefrom lower-level components alone (Deco et al., 2011). This multiscale complexity limits the extentto which observations in one system can be directly extrapolated to another without accountingfor structural differences (Krakauer et al., 2017).3.2 Species-Specific Architectures and Model DivergenceAlthough many species share conserved molecular and cellular mechanisms, their neural architecturesdiverge significantly at the level of network organisation, cortical expansion, and integrativecapacity (Feinberg & Mallatt, 2013). These differences influence how information is processed,how uncertainty is resolved, and how behaviour emerges from neural activity (Seth, 2009). From aneuroengineering perspective, two systems with similar components can exhibit markedly differentfunctional properties if their architectures differ (Friston, 2010). This principle is well establishedin control theory and systems engineering, where topology and feedback structure determine systembehaviour more strongly than individual components.3.3 Constraints on Translational InferenceTranslational inference relies on the assumption that findings in one biological system can informunderstanding of another. Physics and engineering highlight the limitations of this assumption(Ioannidis, 2005). Systems with different boundary conditions, feedback loops, or scaling propertiescan respond differently to identical perturbations (Macleod et al., 2015). In neural systems,small variations in connectivity, receptor distribution, or developmental trajectory can produce divergentoutcomes (Pasca, 2018). These constraints do not imply incompatibility between species,but they underscore the need for caution when interpreting results across systems with distinctphysical and computational architectures (Krakauer et al., 2017).3.4 Energetic and Thermodynamic ConsiderationsNeural activity is constrained by energetic availability and thermodynamic efficiency. The humanbrain, for example, operates near the limits of metabolic capacity, with a high proportion of energydevoted to maintaining resting potentials and supporting long-range communication (Koch et al.,2016). Variations in brain size, cortical folding, and metabolic allocation across species influence5 the energetic landscape in which neural computation occurs (Feinberg & Mallatt, 2013). From aphysical standpoint, systems with different energy budgets and thermodynamic constraints cannotbe assumed to implement equivalent computational strategies, even when they share homologousstructures (Friston, 2010).