Transfer Entropy Based Connectivity Estimation of Spontaneously Firing Hippocampal Cultures on Multi Electrode Arrays

Accurate estimation of interactions in neuronal circuits is critical in understanding neural information processing and the neuronal dynamics of emergent networks. Transfer entropy(TE) is a model-free information theoretic measure of flow of information between two random processes. TE has recently gained much popularity due to its effectiveness in estimating effective connectivity among neurons from simulated networks. However, experimental recordings inherently lack the ground truth information of neural connectivity - making it difficult to identify true connections from spurious connections. To tackle this problem, we present a superimposition method where the estimated connectivity is superimposed onto the spatial firing density plot. The firing density plot, on itself, does not provide connectivity information but we assume that frequently firing channels are more likely to have more active interactions. A neuron's firing depends on the presynaptic inputs, and highly interconnected neurons tend to have more presynaptic inputs resulting in more frequent firing. The firing density plot is organised in a spatial layout, which corresponds substantially to the structural layout. We demonstrated that the connectivity estimated closely corresponds to the firing density plot when superimposed onto the firing density plot. To strengthen the arguement, we analyse the firing count and the connectivity inferred for each randomly sampled channel and found a positively correlated relationship.