Reconstructing Neuronal Circuitry from Parallel Spike Trains

Abstract

State-of-the-art techniques allow researchers to record large numbers of spike trains parallel for many hours. With enough such data, we should be able to infer the connectivity among neurons. Here we develop a computationally realizable method for reconstructing neuronal circuitry by applying a generalized linear model (GLM) to spike crosscorrelations. Our method estimates interneuronal connections in units of postsynaptic potentials and the amount of spike recording needed for verifying connections. The performance of inference is optimized by counting the estimation errors using synthetic data from a network of Hodgkin-Huxley type neurons. By applying our method to rat hippocampal data, we show that the numbers and types of connections estimated from our calculations match the results inferred from other physiological cues. Our method provides the means to build a circuit diagram from recorded spike trains, thereby providing a basis for elucidating the differences in information processing in different brain regions.