The hyperbolic tangent activation function, often abbreviated as tanh, is a widely used activation function in neural networks that maps input values into a range between -1 and 1, providing zero-centered outputs which can help in faster convergence during training. It is particularly effective in handling inputs that have both positive and negative values, allowing for better model performance in certain contexts compared to the sigmoid function, which only outputs positive values.