I don’t think that’s actually how the 2025 report is using “generative” though. I think they mean, generative electrical models of neurons from structural information about neurons. This probably does involve ML (trained on performance at imitating animal models, I think).
Here are the sentences containing that word in the report:
In other cases where discontinuities or non-smoothness arise (e.g., certain spiking resets), generative methods can learn to map recordings to model parameters without needing direct backpropagation through each spike event.
Then I’ll just quote a whole subsection:
Structure to function
For larger mammalian brains, though, comprehensive, whole-brain functional data remain beyond present technology, making it impractical to learn all parameters directly from raw observations. Consequently, biophysical priors – such as those embedded in multi-compartment Hodgkin–Huxley or simpler spiking models – remain crucial to reduce parameter space and keep simulations biologically plausible. Yet even these more traditional approaches struggle to scale up to billions of cells; far more extensive measurement capabilities would be required to fit every neuron and synapse. A promising strategy for bridging the structure-to-function gap is to develop generative models that link morphological and molecular data to neuronal function. In smaller, more experimentally tractable organisms such as C. elegans or larval zebrafish, one can gather both a molecularly annotated connectome and rich functional data (e.g., through optogenetic perturbations and whole-brain recordings). These models learn how specific structural attributes translate into core biophysical parameters like channel conductances or short-term plasticity kinetics by correlating neuronal responses with ultrastructural features, such as synapse size, receptor distributions, and dendritic geometry. Once validated in systems with abundant ground-truth data, the same approach can be extended to large mammalian brains, where whole-brain functional measurements remain infeasible. In this context, Holler et al stands as a landmark demonstration of how morphological information alone can inform functional predictions: by combining slice electrophysiology with electron microscopy, they showed that synapse size reliably predicts synaptic strength in mouse somatosensory cortex (Holler et al., 2021). Their work exemplifies how structural features can serve as powerful proxies for synaptic function. Incorporating molecular-level data into such pipelines would enable generative frameworks to infer parameters with even greater precision and scale, providing a viable path toward biologically grounded simulations of large neural circuits. The necessary condition for that is an extensive database of slice synaptic electrophysiology data, such as Campagnola et al, 2022. In this way, integrating high-resolution structural data with targeted functional measurements can progressively narrow – and ultimately bridge – the gap between brain structure and brain function. This will require substantial investments in such aligned datasets.
Thanks! Interesting.
I don’t think that’s actually how the 2025 report is using “generative” though. I think they mean, generative electrical models of neurons from structural information about neurons. This probably does involve ML (trained on performance at imitating animal models, I think).
Here are the sentences containing that word in the report:
Then I’ll just quote a whole subsection: