The brain’s complexity far exceeds simple, static narratives like “whole brain” or “localizer” views because it operates as a highly interconnected, adaptive, and context-sensitive system. Rather than a fixed map of regions with one-to-one functions, the brain embodies dynamic interactions, structural diversity, and context-dependent computation that collectively give rise to flexible cognition, perception, and behavior. Key points about why the brain is more complicated than simple models

• Rich, multi-scale organization
  • The brain spans multiple scales from molecular and microcircuit dynamics to large-scale networks. Interactions across these scales are nontrivial and can’t be fully captured by coarse, single-scale abstractions. This multi-scale nesting enables diverse computations, from fast synaptic plasticity to slower circuit reconfiguration, which complicates any attempt to summarize brain function with a single model. This complexity is reflected in how different circuits contribute to learning, memory, attention, and consciousness, often in non-additive ways.

• Nonlinear, context-dependent dynamics
  • Neural activity is governed by nonlinear interactions among neurons, synapses, and regional networks. The same brain region can participate in different functions depending on task demands, state of arousal, and prior experience. This contextual dependence means “localizers” cannot exhaustively predict function across tasks, and “whole-brain” models often need to account for state-dependent reconfiguration and nonlinear couplings to mirror real brain behavior.

• Emergent behavior and repertoire
  • Large-scale brain dynamics show emergent properties that are not straightforward extrapolations from individual components. Perturbation studies and measures of complexity (like the perturbative complexity index) reveal that the brain can reconfigure its functional repertoire in response to changing inputs and perturbations, highlighting a capacity for flexible computation beyond static mappings.

• Structural and functional diversity
  • Subcortical structures (e.g., hippocampus, basal ganglia, thalamus) and their unique microcircuitry contribute substantially to overall brain function. These regions have distinct coding schemes, plasticity rules, and connectivity patterns that collectively support memory, navigation, reward learning, and action selection. Treating the brain as a homogeneous “cortex-centric” system misses these varied contributions and the ways they interact with cortical areas.

• Individual variability and developmental dynamics
  • Brain organization varies across individuals and changes with development, learning, and pathology. This dependence on history means universal, one-size-fits-all models struggle to capture the full richness of brain function. Models that allow learning and adaptation over time tend to better reflect how brains differ and evolve.

• Computational capacity and efficiency trade-offs
  • The brain appears to optimize for energy-efficient, robust computation rather than brute-force speed. This leads to trade-offs between precision, speed, and metabolic cost, shaping network architectures and dynamics in ways that simple, static models do not capture. Complex models that incorporate sparsity, modularity, and hierarchical processing better reflect these trade-offs.

Why simplified “whole-brainer” or “localizer” views are insufficient

• Whole-brainer models capture global dynamics but may miss fine-grained, region-specific contributions that become critical under certain tasks or perturbations. Conversely, localizer-based views risk underestimating the combinatorial and context-sensitive nature of brain function, where regions participate in multiple networks depending on the situation. The most accurate understanding emerges from integrating both perspectives within flexible, state-aware models that can simulate how networks reconfigure under different demands.

• Contemporary research emphasizes the need to examine how complexity, perturbation response, and information processing capacity change across brain states, rather than assuming fixed functional roles. Studies using perturbation methods and complexity metrics illustrate that the brain’s computational capabilities are dynamic and situational, not static.

If you’d like, I can summarize recent experimental approaches that reveal these complexities (e.g., perturbational complexity index studies, strength- dependent perturbations in whole-brain models, and multi-scale simulations) and point to representative findings, while noting how they challenge simple dichotomies between “localizers” and “whole-brainers.”