Jacques, Brandon G.Tiganj, ZoranHoward, Marc W.Sederberg, Per B.2022-04-192022-04-192021B. Jacques, Z. Tiganj, M. Howard, P. Sederberg. "DeepSITH: Efficient learning via decomposition of what and when across time scales." Advances in Neural Information Processing Systemshttps://hdl.handle.net/2144/44247Extracting temporal relationships over a range of scales is a hallmark of human perception and cognition -- and thus it is a critical feature of machine learning applied to real-world problems. Neural networks are either plagued by the exploding/vanishing gradient problem in recurrent neural networks (RNNs) or must adjust their parameters to learn the relevant time scales (e.g., in LSTMs). This paper introduces DeepSITH, a network comprising biologically-inspired Scale-Invariant Temporal History (SITH) modules in series with dense connections between layers. SITH modules respond to their inputs with a geometrically-spaced set of time constants, enabling the DeepSITH network to learn problems along a continuum of time-scales. We compare DeepSITH to LSTMs and other recent RNNs on several time series prediction and decoding tasks. DeepSITH achieves state-of-the-art performance on these problems.en-USConference materials708139