Beyond Biological Fidelity: Why AI Neurons Stay Static

The field of artificial intelligence often draws inspiration from biology, particularly in its neural network architectures. However, a significant divergence exists in how individual "neurons" function. While biological neurons are complex, dynamic systems, their artificial counterparts typically rely on static activation functions. This disparity raises a fundamental question: why don't AI systems leverage dynamical neurons, even when the biological blueprint clearly demonstrates their effectiveness? The answer lies in a complex interplay of computational feasibility, stability, and practical application.

The Enormous Computational and Memory Challenge

One of the primary roadblocks to adopting dynamical neurons in AI is the sheer computational burden and memory bandwidth requirements. Modern large language models (LLMs) operate with billions of parameters, effectively simulating billions of static connections and activations. Extending this to dynamical neurons, which would involve continuous state updates and more intricate calculations for each neuron, would exponentially increase the demand on current computer architectures. Simulating even a fraction of such a system, akin to the scale of the human brain, remains a formidable challenge limited by memory bandwidth and processing power.

Navigating the Treacherous Waters of Instability

Beyond the raw computational cost, integrating dynamical neurons introduces significant instability, particularly during the critical training phase of neural networks. Artificial neural networks are already notoriously difficult to train, prone to issues like vanishing or exploding gradients. Dynamic weights, which would continuously evolve based on their internal state and inputs, could amplify these instabilities. This heightened volatility could lead to models that fail to converge, or worse, end up in "totally unusable states" at inference time, rendering them practically worthless. The perceived limited practical gain from this increased biological fidelity often doesn't outweigh the enormous computational cost and the amplified risk of instability.

The Practicality of Simpler Architectures and Past Successes

It's worth noting that AI's journey isn't solely defined by massive, biologically-inspired networks. Early artificial neural networks were significantly smaller and, while not performing tasks at the scale of modern LLMs, achieved remarkable results in various non-generative areas. These successes were often accomplished with orders of magnitude fewer parameters and simpler activation functions, demonstrating that complexity isn't always a prerequisite for utility. This historical context highlights a pragmatic approach in AI development: adopting architectures and mechanisms that offer the best practical gain for a given computational cost and stability profile. For the current paradigm of large-scale, general-purpose AI, static activations represent a critical simplification that enables current capabilities.

Conclusion

Ultimately, the choice to use static activations over dynamical neurons in most current AI models is a pragmatic one, balancing biological inspiration with engineering realities. The twin challenges of immense computational cost and amplified training instability currently make dynamical neurons a niche or research-intensive area rather than a mainstream approach. As hardware advances and novel training paradigms emerge, this balance might shift, but for now, the static neuron remains a cornerstone of scalable and stable AI.