Local Plasticity Rules for Desired Network-Level Behavior in Spiking Neural Networks

Identify the local synaptic and structural plasticity rules that yield a specified network-level behavior in spiking neural networks implemented on neuromorphic hardware, thereby linking local learning mechanisms to emergent system-level functionality.

Background

The paper discusses the need for on-device and online learning in neuromorphic systems, emphasizing local plasticity as a promising approach. However, due to emergent dynamics, deriving system-level behaviors from local rules remains challenging.

The authors explicitly state that it is not clear which local rules produce particular global behaviors, noting that evolutionary search and meta-learning have been used to rediscover useful rules but without establishing a general mapping from rules to behaviors.

References

It is not clear what local rules will yield a particular network-level behavior, but evolutionary search and meta-learning have been used to (re-)discover desirable plasticity rules.

— Neuromorphic Programming: Emerging Directions for Brain-Inspired Hardware  (2410.22352 - Abreu et al., 2024) in Section 5 (Neuromorphic Programming), Online learning paragraph

One remaining hypothesis is a mismatch between the states represented during reward-producing bursts and those encountered at later selections; this explanation remains untested.

— The Fly That Stopped: Mushroom-Body-Inspired Habituation as a Reward-Free Scheduling Prior for Autonomous Penetration Testing  (2609.29126 - Moutesidis, 24 Sep 2026) in Section 6.2, “Five local-plasticity approaches under a fixed readout”