Delves into entropy in neuroscience data and ecology, exploring the representation of sensory information and the diversity of biological populations.
Delves into quantifying entropy in neuroscience data, exploring how neuron activity represents sensory information and the implications of binary digit sequences.
Covers variational representation and information measures such as entropy and mutual information.
Covers the basics of information theory, focusing on Shannon's setting and channel transmission.
Explores the second principle of thermodynamics, emphasizing entropy's role in system evolution and equilibrium.
Covers information measures, tail bounds, subgaussions, subpossion, independence proof, and conditional expectation.
Covers source coding, Kraft's inequality, mutual information, Huffman procedure, and properties of tropical sequences.
