Learning with Chemical versus Electrical Synapses -- Does it Make a
  Difference?
Mónika Farsang Mathias Lechner David Lung Ramin Hasani Daniela Rus Radu Grosu
Bio-inspired neural networks have the potential to advance our understanding
of neural computation and improve the state-of-the-art of AI systems.
Bio-electrical synapses directly transmit neural signals, by enabling fast
current flow between neurons. In contrast, bio-chemical synapses transmit
neural signals indirectly, through neurotransmitters. Prior work showed that
interpretable dynamics for complex robotic control, can be achieved by using
chemical synapses, within a sparse, bio-inspired architecture, called Neural
Circuit Policies (NCPs). However, a comparison of these two synaptic models,
within the same architecture, remains an unexplored area. In this work we aim
to determine the impact of using chemical synapses compared to electrical
synapses, in both sparse and all-to-all connected networks. We conduct
experiments with autonomous lane-keeping through a photorealistic autonomous
driving simulator to evaluate their performance under diverse conditions and in
the presence of noise. The experiments highlight the substantial influence of
the architectural and synaptic-model choices, respectively. Our results show
that employing chemical synapses yields noticeable improvements compared to
electrical synapses, and that NCPs lead to better results in both synaptic
models.