Nonlinear mixed selectivity supports reliable neural computation

Abstract

Neuronal activity in the brain is variable, yet both perception and behavior are generally reliable. How does the brain achieve this? Here, we show that the conjunctive coding of multiple stimulus features, commonly known as nonlinear mixed selectivity, may be used by the brain to support reliable information transmission using unreliable neurons. Nonlinear mixed selectivity (NMS) has been observed widely across the brain, from primary sensory to decision-making to motor areas. Representations of stimulus features are nearly always mixed together, rather than represented separately or with only additive (linear) mixing, as in pure selectivity. NMS has been previously shown to support flexible linear decoding for complex behavioral tasks. Here, we show that NMS has another important benefit: it requires as little as half the metabolic energy required by pure selectivity to achieve the same level of transmission reliability. This benefit holds for sensory, motor, and more abstract, cognitive representations. Further, we show experimental evidence that NMS exists in the brain even when it does not enable behaviorally useful linear decoding. This suggests that NMS may be a general coding scheme exploited by the brain for reliable and efficient neural computation.