A major challenge in sensory neuroscience is to understand how complex stimuli are collectively encoded by neural circuits. In particular, the role of correlations between output neuronsis still unclear. Here we introduce a general strategy to equip an arbitrary model of stimulus encoding by single neurons with a network of couplings between output neurons. We develop a method for inferring both the parameters of the encoding model and the } couplings between neurons from simultaneously recorded retinal ganglion cells. The inference method fits the couplings to accurately account for noise correlations, without affecting the performance in predicting the mean response . We demonstrate that the inferred couplings are independent of the stimulus used for learning, and can be used to predict the correlated activity in response to more complex stimuli. The model offers a powerful and precise tool for assessing the impact of noise correlations on the encoding of sensory information .
Correlations in sensory neural networks have both extrinsic and intrinsic origins. Extrinsic or stimulus correlations arise from shared inputs to the network, and thus depend strongly on the stimulus ensemble. Intrinsic or noise correlations reflect biophysical mechanisms of interactions between neurons, which are expected to be robust to changes of the stimulus ensemble. Despite the importance of this distinction for understanding how sensory networks encode information collectively, no method exists to reliably separate intrinsic interactions from extrinsic correlations in neural activity data, limiting our ability to build predictive models of the network response. In this paper we introduce a general strategy to infer population models of interacting neurons that collectively encode stimulus information}. The key to disentangling intrinsic from extrinsic correlations is to infer the couplings between neurons separately from the encoding model, and to combine the two using corrections calculated in a mean-field approximation . We demonstrate the effectiveness of this approach on retinal recordings. The same coupling network is inferred from responses to radically different stimulus ensembles, showing that these couplings indeed reflect stimulus-independent interactions between neurons. The inferred model predicts accurately the collective response of retinal ganglion cell populations as a function of the stimulus .
R
A major challenge in sensory neuroscience is to understand how complex stimuli are collectively encoded by neural circuits. In particular, the role of correlations between output neuronsis still unclear. Here
Correlations in sensory neural networks have both extrinsic and intrinsic origins. Extrinsic or stimulus correlations arise from shared inputs to the network, and thus depend strongly on the stimulus ensemble. Intrinsic or noise correlations reflect biophysical mechanisms of interactions between neurons, which are expected to be robust to changes of the stimulus ensemble. Despite the importance of this distinction for understanding how sensory networks encode information collectively, no method exists to reliably separate intrinsic interactions from extrinsic correlations in neural activity data, limiting our ability to build predictive models of the network response. In this paper
R
equip an arbitrary model of stimulus encoding by single neurons with a network of couplings between output neurons. We develop a method for inferring both the parameters of the encoding model and the
infer
A
population models of interacting neurons that collectively encode stimulus information
A
. The key to disentangling intrinsic from extrinsic correlations is to infer the
R
from simultaneously recorded retinal ganglion cells. The inference method fits the couplings to accurately account for noise correlations, without affecting the performance in predicting the mean response
separately from the encoding model, and to combine the two using corrections calculated in a mean-field approximation
R
that the inferred couplings are independent of
the effectiveness of this approach on retinal recordings. The same coupling network is inferred from responses to radically different stimulus ensembles, showing that these couplings indeed reflect stimulus-independent interactions between neurons. The inferred model predicts accurately the collective response of retinal ganglion cell populations as a function of
D
used for learning, and can be used to predict the correlated activity in response to more complex stimuli. The model offers a powerful and precise tool for assessing the impact of noise correlations on the encoding of sensory information