Leaky-Integrate-and-Fire Neuron-Like Long-Short-Term-Memory Units as Model System in Computational Biology - Gerum, Richard; Erpenbeck, André; Krauß, Patrick; Schilling, Achim (2023 International Joint Conference on Neural Networks (IJCNN), 2023)
Biological neural networks encode information very efficiently, and dynamically react to sensory input on very small time scales. In contrast to most contemporary machine learning approaches which rely on rate neurons with continuous output, biological neural networks are based on spiking neurons with quasi-binary discrete output. In Artificial Intelligence (AI) Research, time series data are efficiently encoded in Long-Short- Term-Memory (LSTM) networks. Despite their strength in encoding time series data and making predictions, LSTM units are assumed to be biologically implausible. Nevertheless, recent studies show that LSTM unit networks indeed behave similar to biological neural networks. In this study, we show that a particular choice of parameters for the weights and gates in peephole LSTM units causes these units to show similar dynamic behaviour as biologically plausible leaky integrate-and-fire (LIF) neurons, which represent a simple biologically inspired spiking neuron model. We analyzed the spiking characteristics of the restricted peephole LSTM units and characterize the parameter space, in which these units show certain spiking characteristics. We conclude that tackling complex cognitive tasks with biologically plausible and explainable artificial neural networks is an important step to make progress in both fields, neuroscience and artificial intelligence.
CRIS-Team Scopus Importer:2024-12-16