Limits of optimal decoding under synaptic coarse-tuning
This paper demonstrates that while optimized linear decoders typically outperform naive population averaging, the presence of synaptic coarse-tuning—consistent with observed biological volatility—limits the performance of optimal decoders such that they saturate and become qualitatively similar to naive decoders, suggesting that neural computation may rely on a robust, low-dimensional manifold.