A new inference approach for training shallow and deep generalized linear models of noisy interacting neurons
This work addresses the challenge of building robust models for predicting neuronal activity in neuroscience, which is incremental as it refines an existing paradigm for better generalization.
The authors tackled the problem of generalized linear models failing to generalize across different stimulus statistics when predicting neuronal activity, by developing a two-step inference strategy that separates stimulus correlations from network interactions. They demonstrated improved performance, stability, and generalization in retinal ganglion cell responses to complex visual stimuli, with the method extending to deep convolutional neural networks for high predictive accuracy.
Generalized linear models are one of the most efficient paradigms for predicting the correlated stochastic activity of neuronal networks in response to external stimuli, with applications in many brain areas. However, when dealing with complex stimuli, the inferred coupling parameters often do not generalize across different stimulus statistics, leading to degraded performance and blowup instabilities. Here, we develop a two-step inference strategy that allows us to train robust generalized linear models of interacting neurons, by explicitly separating the effects of correlations in the stimulus from network interactions in each training step. Applying this approach to the responses of retinal ganglion cells to complex visual stimuli, we show that, compared to classical methods, the models trained in this way exhibit improved performance, are more stable, yield robust interaction networks, and generalize well across complex visual statistics. The method can be extended to deep convolutional neural networks, leading to models with high predictive accuracy for both the neuron firing rates and their correlations.