Simple low-dimensional computations explain variability in neuronal activity
Abstract
Our understanding of neural computation is founded on the assumption that neurons fire in response to a linear summation of inputs. Yet experiments demonstrate that some neurons are capable of complex computations that require interactions between inputs. Here we show, across multiple brain regions and species, that simple computations (without interactions between inputs) explain most of the variability in neuronal activity. Neurons 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 binary artificial neurons, predict complex higher-order dependencies and recover known features of synaptic connectivity. The inferred computations are low-dimensional, indicating a highly redundant neural code that is necessary for error correction. These results suggest that, despite intricate biophysical details, most neurons perform simple computations typically reserved for artificial models.