Neuroscience is the process of reverse engineering the brain.
That means reverse engineering how neurons give rise to everything from action and perception to intelligence and the emergence of consciousness itself.
There are two key processes that contribute to the form and function of the brain. At the macroscale, there is DNA. At the microscale there is synaptic learning. At the macroscale, DNA evolves the macrostructure of the brain over long time scales (centuries, millennia, eons). At the microscale, synaptic learning refines the microstructure circuitry over short time scales (minutes, hours, days, weeks, years).
Modelling plays a fundamental role in the process of reverse engineering the brain. The history of neuroscience shows a progression of modelling from the macroscale to the microscale; From anatomical classification and physiology to the mechanistic neural modelling usually described as Computational Neuroscience. As the emphasis has shifted from the macrostructure to the microstructure, the role of learning - synaptic learning - has increasingly come to the fore.
Once we have neuroscience models with neurons that feature learning, we are awfully close to what we think of today as Artificial intelligence. In fact, it seems a matter of time before Neuroscience converges with Artificial Intelligence, where the models we use for neuroscience comprise AI and where the study of AI comprises neuroscience.
At Chatable, this direct integration of neuroscience and AI is at the foundation of everything we do*.
*We call this Neuroscience-Led AI, but it could as easily be called AI-Led Neuroscience (and indeed this is a false dichotomy in the tradition of the proverbial chicken & egg).