Biological Plausibility of LLMs

Updated 4 November 2025

LLMs are high-dimensional neural networks using backpropagation that reveal fundamental differences from biological neural processes.
Critical neuron analysis shows that disabling a few key units can collapse performance, mirroring specialized roles in biological systems but lacking redundancy.
LLMs exhibit partial cognitive alignment through language metrics and causal modeling, highlighting the need for more biologically plausible training methods.

LLMs are high-dimensional artificial neural networks that leverage deep learning, typically based on transformer architectures, to generate and reason about human language and other sequential data. The question of their "biological plausibility" pertains to the degree to which these models reflect, align with, or diverge from the mechanisms and organizational principles found in biological neural systems—specifically the human brain. This topic spans algorithmic, architectural, cognitive, and representational comparisons, revealing both superficial behavioral parallels and deep mechanistic implausibilities.

1. Algorithmic Foundations: Backpropagation and Its Biological Limitations

The core learning algorithm for essentially all LLMs is backpropagation (BP), which performs gradient-based supervised learning by propagating error signals backwards through a multi-layered network. While BP underlies the recent progress in deep learning, it is fundamentally at odds with biological processes:

BP requires non-local computations—error signals must traverse multiple synaptic layers, violating known constraints in biological neural circuits, where only local information (at synapses or nearby neurons) is available.
In a Lagrangian optimization framework, supervised learning can be formulated such that the KKT conditions (first-order necessary conditions) map directly onto the forward and backward pass of BP.
Alternative, more biologically plausible algorithms can be constructed within this framework, based on searching for saddle points in the adjoint space of weights, neurons, and Lagrange multipliers. This formulation allows for credit assignment and error correction relying only on local information propagation, avoiding non-local communication.

In the context of LLMs, current architectures are trained using standard BP, hence inheriting its lack of biological plausibility. Theoretical alternatives, such as local learning algorithms derived from the Lagrangian framework, have not yet been adopted at scale in deployed LLMs, limiting their alignment with biological computation (Betti et al., 2018).

2. Neural Representations and Criticality: Comparison with Biological Neuronal Ensembles

Recent research demonstrates that LLMs contain ultra-sparse sets of "critical neurons"—neurons whose inactivation catastrophically impairs global model function:

Disabling as few as 3–10 neurons in models with billions of parameters results in performance collapse, with perplexity increases of up to 20 orders of magnitude, and total loss of function across all downstream tasks.
These critical neurons cluster disproportionately in the final layers of the architecture, particularly in MLP down_proj components—analogous to late-stage information bottlenecks in biological systems.
Performance drop-offs show sharp phase transitions, not gradual degradation, upon critical neuron masking.

This mirrors biological findings: small, highly specialized neuronal groups (e.g., in cortical or subcortical nuclei) are necessary for major cognitive or motor functions, with their lesioning causing profound dysfunction. However, LLMs display less redundancy and plasticity than biological systems—when critical artificial neurons are removed, models do not recover, unlike biological brains, which can exhibit route reorganization and recovery in some contexts (Qin et al., 11 Oct 2025).

3. Cognitive Benchmarks: Alignment with Human Speech and Language Processing

On the behavioral dimension, LLMs can be evaluated for biological and cognitive plausibility by measuring their ability to predict variables reflecting human speech production and comprehension:

LLMs fine-tuned on spoken conversational data (vs. written or mixed genres) outperform others in predicting production variables such as speech reductions and prosodic prominences—these are robust surface indicators of cognitive and physiological constraints in human language processing.
F1 scores for the best conversationally-trained English models approach .44–.45 for reduction and .39–.41 for prominence; French and Mandarin results follow similar patterns.
Model uncertainty (token-level surprisal) shows correlations with speech reduction and prominence labels, reflecting psycholinguistic findings and suggesting that LLMs encode some traces of human information economy and attentional marking.

However, models trained exclusively on written data are less aligned with these benchmarks, underscoring the necessity for training data that reflects the true cognitive ecology of language users for enhanced biological plausibility (Wang et al., 22 May 2025).

4. Representational Geometry: Logical Reasoning and Modularity

Biological plausibility extends beyond performance benchmarks to include the structure of internal representations and computational modularity:

LLMs encode both logical validity and world knowledge plausibility as near-parallel linear directions in their activation space, leading to systematic conflation ("content effects"): plausible arguments are judged as valid, even in the absence of logical entailment.
Steering vectors can causally shift model predictions along these directions. The cosine similarity between validity and plausibility vectors is high (≈0.5–0.6), and this alignment quantitatively predicts the extent of behavioral content effects across models.
In biological cognition, dual-process theories posit a modular dissociation between fast, heuristic, plausibility-based reasoning and deliberative, logic-based reasoning, supported by evidence for distinct neural substrates. In contrast, LLMs lack such a modular separation at the representational level—even when explicit reasoning (e.g., chain-of-thought prompting) reduces overt bias, representational entanglement remains (Bertolazzi et al., 8 Oct 2025).

5. Causal Modeling and Virtual Cell Applications

The utility of LLMs as models for biological systems has been further tested via their application to virtual cell modeling and gene regulatory network (GRN) discovery:

LLMs can be used as oracles to propose GRNs for single-cell RNA-seq data, capturing meaningful TF–gene relationships that, when deployed in synthetic data generators, yield statistically and biologically plausible scRNA-seq data (e.g., cosine distance ≈ 0.00023–0.00026 to real data; AUROC approaching random).
Hybrid pipelines integrating LLM-derived priors with statistical causal discovery outperform either method in isolation, indicating that LLMs encode useful world knowledge for complex biological inference.
More broadly, LLMs trained directly on omics data, or coordinating agentic biological workflows, can approximate robust cell-state annotation, predict perturbation outcomes, and recover regulatory logic. Nonetheless, generalizability across cell types, mechanistic interpretability, and perfect causal modeling remain limiting factors (Afonja et al., 21 Oct 2024, Li et al., 9 Oct 2025).

6. Cognitive Process Alignment and Human-like Asymmetries

LLMs exhibit only partial alignment with specific human-like cognitive effects, such as production-interpretation asymmetries in pronoun resolution:

Large instruction-tuned models (e.g., LLaMA-70B) replicate the direction of human production vs. interpretation asymmetry (with Yes/No prompts), but systematically underestimate effect sizes relative to humans (e.g., showing 19.4% vs. human 47.2% in production, 9.2% vs. 28.8% in interpretation).
The presence and magnitude of such effects depend strongly on model size and prompt formulation, and are not inherent to the generative mechanism, suggesting that LLMs internalize some global linguistic patterns but their mechanistic alignment with human cognition is incomplete (Lam et al., 21 Mar 2025).

7. Synthesis, Limitations, and Outlook

Current LLMs show varying degrees of alignment with biological and cognitive processes, from surface-level behavioral mimicry to deep structural differences. Direct algorithmic and architectural comparisons reveal low biological plausibility—particularly regarding learning rules (BP vs. local/plausible learning), credit assignment, representational modularity, and redundancy. Behavioral evaluation and domain-specific applications reveal that LLMs can, under certain training regimens and data selection, approximate cognitive traces seen in human language use. However, full mechanistic and causal biological plausibility—generalization across contexts, robust recovery from neurally localized dysfunction, and interpretable modular representation—remains an open challenge. Progress toward biologically plausible LLMs will require substantial advances in local learning algorithms, architectural redundancy and plasticity, integration of multi-modal and developmental data, and interpretability frameworks inspired by biological and cognitive neuroscience.