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Bibliographic Details
Main Author:	Cidad, Sue
Format:	Recurso digital
Language:	English
Published:	Zenodo 2026
Subjects:	neuroscience animal rights anthropology sociology
Online Access:	https://doi.org/10.5281/zenodo.18674709
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Table of Contents:

Across the last four decades, several well-documented failures in biological and food-related systems have demonstrated a recurring pattern: regulatory frameworks tend to respond to harm rather than anticipate it (Renn, 2008; Hutter, 2011). These events are not isolated incidents but manifestations of structural characteristics inherent to complex supply chains, high-throughput environments, and legacy assumptions about biological safety. Their significance lies not in emotive impact but in analytical value. They provide a factual basis for understanding how systems behave under pressure, how risk is distributed, and how oversight mechanisms adapt only after critical thresholds are crossed (European Commission, 2012; WOAH, 2019). Neural systems operate across multiple spatial and temporal scales, from ion channel kinetics to large-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 engineered systems, these constraints are explicitly modelled; in biological systems, they must be inferred from observation (Dehaene & Changeux, 2011). When signals traverse multiple layers of organisation, small discrepancies can accumulate, producing emergent behaviours that are not predictable from lower-level components alone (Deco et al., 2011). This multiscale complexity limits the extent to which observations in one system can be directly extrapolated to another without accounting for structural differences (Krakauer et al., 2017). 3.2 Species-Specific Architectures and Model Divergence Although many species share conserved molecular and cellular mechanisms, their neural architectures diverge significantly at the level of network organisation, cortical expansion, and integrative capacity (Feinberg & Mallatt, 2013). These differences influence how information is processed, how uncertainty is resolved, and how behaviour emerges from neural activity (Seth, 2009). From a neuroengineering perspective, two systems with similar components can exhibit markedly different functional properties if their architectures differ (Friston, 2010). This principle is well established in control theory and systems engineering, where topology and feedback structure determine system behaviour more strongly than individual components. 3.3 Constraints on Translational Inference Translational inference relies on the assumption that findings in one biological system can inform understanding of another. Physics and engineering highlight the limitations of this assumption (Ioannidis, 2005). Systems with different boundary conditions, feedback loops, or scaling properties can respond differently to identical perturbations (Macleod et al., 2015). In neural systems, small variations in connectivity, receptor distribution, or developmental trajectory can produce divergent outcomes (Pasca, 2018). These constraints do not imply incompatibility between species, but they underscore the need for caution when interpreting results across systems with distinct physical and computational architectures (Krakauer et al., 2017). 3.4 Energetic and Thermodynamic Considerations Neural activity is constrained by energetic availability and thermodynamic efficiency. The human brain, for example, operates near the limits of metabolic capacity, with a high proportion of energy devoted to maintaining resting potentials and supporting long-range communication (Koch et al., 2016). Variations in brain size, cortical folding, and metabolic allocation across species influence 5 the energetic landscape in which neural computation occurs (Feinberg & Mallatt, 2013). From a physical standpoint, systems with different energy budgets and thermodynamic constraints cannot be assumed to implement equivalent computational strategies, even when they share homologous structures (Friston, 2010).

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