Leveraging dendritic properties to advance machine learning and
  neuro-inspired computing
Michalis Pagkalos Roman Makarov Panayiota Poirazi
The brain is a remarkably capable and efficient system. It can process and
store huge amounts of noisy and unstructured information using minimal energy.
In contrast, current artificial intelligence (AI) systems require vast
resources for training while still struggling to compete in tasks that are
trivial for biological agents. Thus, brain-inspired engineering has emerged as
a promising new avenue for designing sustainable, next-generation AI systems.
Here, we describe how dendritic mechanisms of biological neurons have inspired
innovative solutions for significant AI problems, including credit assignment
in multilayer networks, catastrophic forgetting, and high energy consumption.
These findings provide exciting alternatives to existing architectures, showing
how dendritic research can pave the way for building more powerful and
energy-efficient artificial learning systems.