Abstract:
Shannon’s information theory provides a valuable framework for analyzing neural coding and information transmission. However, Shannon limits are only achievable asymptotically, as the complexity of encoding and decoding — as well as the associated delays — grow without bound, a scenario unlikely in biological systems. In this talk, we explore the finite-size effects and decoding errors that arise in realistically constrained neural populations. We then show that it may be possible to match the statistics of the input (stimulus) and neuronal noise in such a way that uncoded transmission becomes exactly optimal in the Shannon sense. Because uncoded transmission is entirely analog, it avoids both source discretization and block coding. We thus hypothesize that it may represent a viable strategy for information transmission in real neural systems.