One-core neuron deep learning for time series prediction.

The enormous computational requirements and unsustainable resource consumption associated with massive parameters of large language models and large vision models have given rise to challenging issues. Here, we propose an interpretable 'small model' framework characterized by only a single core-neuron, i.e. the one-core-neuron system (OCNS), to significantly reduce the number of parameters while maintaining performance comparable to the existing 'large models' in time-series forecasting. With multiple delay feedback designed in this single neuron, our OCNS is able to convert one input feature vector/state into one-dimensional time-series/sequence, which is theoretically ensured to fully represent the states of the observed dynamical system. Leveraging the spatiotemporal information transformation, the OCNS shows excellent and robust performance in forecasting tasks, in particular for short-term high-dimensional systems. The results collectively demonstrate that the proposed OCNS with a single core neuron offers insights into constructing deep learning frameworks with a small model, presenting substantial potential as a new way for achieving efficient deep learning.