Abstract
Statistically inferring neuronal connections from observed spike train data is a standard procedure for understanding the structure of the underlying neural circuits. However, the inferred connections seldom reflect true synaptic connections, being skewed by factors such as model mismatch, unobserved neurons, and limited data. On the other hand, spike train covariances, sometimes referred to as “functional connections,” make no assumption of the underlying neuron models and provide a straightforward way to quantify the statistical relationships between pairs of neurons. The main drawback of functional connections compared to statistically inferred connections is that the former are not causal, whereas statistically inferred connections are often constrained to be. However, we show in this work that the inferred connections in spontaneously active networks modeled by generalized linear point process models strongly reflect covariances between neurons, not causal information. We investigate this relationship between the neuronal connections inferred with model-matched maximum likelihood inference and the corresponding spike train covariance in a nonlinear spiking neural network model. Strong correlations between inferred neuronal connections and spike train covariances are observed when many neurons are unobserved or when neurons are weakly coupled. This phenomenon occurs across different network structures, including random networks and balanced excitatory-inhibitory networks. A theoretical analysis of maximum likelihood solutions in analytically tractable cases elucidates how the inferred filters relate to ground-truth covariances of the neurons, and opens the door for future investigations.
Competing Interest Statement
The authors have declared no competing interest.
