Efficiency turns the table on neural encoding, decoding and noise

- Neural population need to collaborate to represent information efficiently.
- Sensory neurons cannot be described in isolation with receptive fields/tuning curves.
- Most response variability is not noise but degeneracy in the code.
- We should look for invariant decoder or large neural populations.

Sensory neurons are usually described with an encoding model, for example, a function that predicts their response from the sensory stimulus using a receptive field (RF) or a tuning curve. However, central to theories of sensory processing is the notion of 'efficient coding'. We argue here that efficient coding implies a completely different neural coding strategy. Instead of a fixed encoding model, neural populations would be described by a fixed decoding model (i.e. a model reconstructing the stimulus from the neural responses). Because the population solves a global optimization problem, individual neurons are variable, but not noisy, and have no truly invariant tuning curve or receptive field. We review recent experimental evidence and implications for neural noise correlations, robustness and adaptation.