This is great Konrad, and I would add one more advantage of such models, which is that they can expand our intuitions about how complex systems work, and this in turn allows for the cognitive development of new ideas that wouldn't have been possible otherwise. As an example, deep learning has made it possible to think more clearly about how simple ideas can scale up. We already had the core idea of interacting layers of feature detectors with the pandemonium model from many decades ago, but when we first developed image computable models such as Alexnet (and for me the early work Riesenhuber, Serre & Poggio did this) , our intuitions as scientists were expanded. This upgrade has substantially changed the way that vision scientists are approaching their work by allowing us to think more concretely about image processing rather than being limited to simple feature processing.
Of course there were vision scientists who thought about image processing before deep learning (e.g. Aude Oliva, Mary Potter), but they were in the minority. Image work in human vision science is now rapidly becoming mainstream, not just with modellers but also with experimentalists.
> Mechanistic models can feel extremely powerful at the scale of single neurons or synapses yet can become unwieldy at the scale of many neurons or synapses with many relevant ion channels. The computational complexity balloons, and the resulting equations can no longer be understood by mere mortals. If we are to believe in the promise of mechanistic understanding, we may still have to leave the space of human-understandable models.
When we have huge mechanistic models that are no longer human-understandable, they start to feel like DNNs in the sense that "understanding" amounts to prediction and perturbation/ablation analysis (maybe there is more?). Is the only difference then that large mechanistic models have building blocks that are more biologically plausible than DNNs?
As a PhD student working on mechanistic models, I often find myself stuck between making them analyzable and keeping them biologically realistic, and sometimes I just feel lost. It becomes even more challenging to maintain this balance as the experimental data we observe today grows more complex and large-scale. It’s really encouraging to hear that you still view mechanistic models as the best way to understand many systems.
This is great Konrad, and I would add one more advantage of such models, which is that they can expand our intuitions about how complex systems work, and this in turn allows for the cognitive development of new ideas that wouldn't have been possible otherwise. As an example, deep learning has made it possible to think more clearly about how simple ideas can scale up. We already had the core idea of interacting layers of feature detectors with the pandemonium model from many decades ago, but when we first developed image computable models such as Alexnet (and for me the early work Riesenhuber, Serre & Poggio did this) , our intuitions as scientists were expanded. This upgrade has substantially changed the way that vision scientists are approaching their work by allowing us to think more concretely about image processing rather than being limited to simple feature processing.
Of course there were vision scientists who thought about image processing before deep learning (e.g. Aude Oliva, Mary Potter), but they were in the minority. Image work in human vision science is now rapidly becoming mainstream, not just with modellers but also with experimentalists.
> Mechanistic models can feel extremely powerful at the scale of single neurons or synapses yet can become unwieldy at the scale of many neurons or synapses with many relevant ion channels. The computational complexity balloons, and the resulting equations can no longer be understood by mere mortals. If we are to believe in the promise of mechanistic understanding, we may still have to leave the space of human-understandable models.
When we have huge mechanistic models that are no longer human-understandable, they start to feel like DNNs in the sense that "understanding" amounts to prediction and perturbation/ablation analysis (maybe there is more?). Is the only difference then that large mechanistic models have building blocks that are more biologically plausible than DNNs?
Possibly. In fact, in some cases we may need to use DNNs to approximate the biological components. Very good point.
As a PhD student working on mechanistic models, I often find myself stuck between making them analyzable and keeping them biologically realistic, and sometimes I just feel lost. It becomes even more challenging to maintain this balance as the experimental data we observe today grows more complex and large-scale. It’s really encouraging to hear that you still view mechanistic models as the best way to understand many systems.