Simple recurrent neuron architecture demonstrating temporal dependency modeling through weight matrices and hidden state feedback. Input sequence X(t) flows through stacked neuron cells with shared weights W_x and recurrent weight w_y, where each neuron's output feeds into the next timestep's computation. This compact-to-unrolled visualization clarifies how RNNs maintain memory across time steps via the recurrence relation y(t) = σ(W_x^T·X(t) + w_y·y(t-1) + b). Understanding this foundational pattern is essential for building sequence models, time-series forecasting, and natural language processing systems. Fork this diagram on Diagrams.so to customize neuron counts, activation functions, or extend it with LSTM/GRU variants for your documentation or research.