Simple input–output dependencies explain neuronal activity

Our understanding of neural computation is founded on the assumption that neurons fire in response to a linear summation of inputs. However, experiments demonstrate that some neurons are capable of complex functions that require interactions between inputs. Here we show that direct dependencies—without interactions between inputs—explain most of the variability in neuronal activity. Neurons across multiple brain regions and species are quantitatively described by models that capture the measured dependence on each input individually but assume nothing about combinations of inputs. These minimal models, which are equivalent to logistic artificial neurons, predict complex higher-order dependencies and recover known features of synaptic connectivity. The inferred neural network is sparse, indicating a highly redundant neural code that is robust to perturbations. These results suggest that, despite intricate biophysical details, most neurons can be described by simple artificial models. In neurons, the mapping from inputs to output involves complex biophysical processes. Despite this complexity, it is now shown that simple artificial models explain a large fraction of the variability in neuronal activity.