Title: Multi-Segmental Informational Coding for Self-Supervised Representation Learning. Abstract: Self-supervised representation learning aims to map high-dimensional data into a compact embedding space, where samples with similar semantics are close to each other. Currently, most representation learning methods maximize the cosine similarity or minimize the distance between different views from the same sample in an $\ell^2$ normalized embedding space, and reduce the feature redundancy via a linear correlation constraint. In this study, we propose MUlti-Segmental Informational Coding (MUSIC) as a new embedding scheme for self-supervised representation learning. MUSIC divides an embedding vector into multiple segments to represent different types of attributes, and each segment automatically learns a set of discrete and complementary attributes. MUSIC enables the estimation of the probability distribution over discrete attributes and thus the learning process can be directly guided by information measurements, reducing the feature redundancy beyond the linear correlation. Our theoretical analysis guarantees that MUSIC learns transform-invariant, non-trivial, diverse, and discriminative features. MUSIC does not require a special asymmetry design, a very high dimension of embedding features, or a deep projection head, making the training framework flexible and efficient. Extensive experiments demonstrate the superiority of MUSIC. 