With approximately one hundred billion neurons and about the same amount of glial cells1 forming a labyrinthine network of trillions of synapses, the human healthy brain is still largely a mystery. As such, there are even more incalculable unknowns when it comes to diseases of the brain, including neurodegenerative conditions like Mild Cognitive Impairment (MCI), Alzheimer's Disease (AD) or Parkinson's Disease.
NeuroTech, an emerging industry at the crossroads of neuroscience and technology, holds promise for demystifying long-lingering questions about how the brain functions — and how human cognition and motion may degrade with aging and diseases. Artificial intelligence and machine learning (AI/ML) in particular, have an important role to play in this area of research. As recently reviewed by Yoshua Bengio et al.2, research on artificial neural networks was motivated by the observation that human intelligence emerges from highly parallel networks of relatively simple, non-linear neurons that learn by adjusting the strengths of their connections. This observation leads to a central computational question: How is it possible for networks of this general kind to learn the complicated internal representations that are required for difficult tasks such as recognizing objects or understanding language?