Electrosensory midbrain neurons optimally decode ascending input during object localization

Understanding how downstream brain areas decode sensory information represented by neural populations remains a central problem in neuroscience. While decoders that are optimized to extract the maximum amount of information have been extensively used in research, whether these are physiologically realistic remains at best unclear. Here we show that a physiologically realistic decoding scheme based on correlations between neural activities in the absence of stimulation can predict downstream neural responses as well as the optimal decoder. Simultaneous recordings were made from primary sensory neural populations and their downstream midbrain targets in the electrosensory system of Apteronotus leptorhynchus. We found that neural populations exhibited significant correlations in the absence of stimulation (i.e. 'baseline'), with downstream neural activity lagging primary sensory neural activity with a short latency. We then investigated how primary sensory neural activities were combined downstream. Overall, a decoder that assigned weights to each primary sensory neuron and was trained solely on baseline correlations performed as well as the optimal decoder trained on neural responses to stimulation. Interestingly, both decoders greatly outperformed schemes for which every neuron was assigned the same weight or when the weights were shuffled, indicating that neural identity is critical. Taken together, our results suggest that the brain uses decoding strategies that perform at optimal levels but are qualitatively different from those predicted from optimal solutions. KEY POINTS: How neural signals are decoded to give rise to perception remains poorly understood. We recorded from primary sensory neural populations and their downstream targets. A physiologically realistic decoder performed as well as the optimal solution to predict downstream responses. We found important qualitative differences between how information is decoded and the optimal solution. Our results demonstrate that the brain can do as well as an optimal decoder but uses a different strategy.