Shah, NirmalNirmalShahSakhuja, JayatikaJayatikaSakhujaGanguly, UdayanUdayanGangulyLashkare, SandipSandipLashkareSomappa, LaxmeeshaLaxmeeshaSomappa2025-08-312025-08-312025-01-01[9798350356830]10.1109/ISCAS56072.2025.110440102-s2.0-105010577166http://repository.iitgn.ac.in/handle/IITG2025/28370This work proposes a neuromodulation-inspired spiking neural network using a ReRAM memory. A stashing-merging algorithm is realized to mimic the inherent neuromodulation in humans. While traditional pruning methods remove redundant parts of the network, stashing excludes well-trained neurons while training and restores all neurons at the end of training. This approach exhibits energy-efficient training in the context of a spiking neural network (SNN) since well-trained neurons can be easily identified using the spike count. The idea is validated using a ReRAM-based SNN with 10 conductance levels and performs close to a traditional artificial neural network (ANN) on an MNIST classification workload.falseNeuromodulation | neuromorphic computing | Neurons | ReRAM | spiking neural networks | Stashing algorithm | SynapseConference Paper20250cpConference Proceeding0