Reconstructing neuronal circuitry from parallel spike trains

• Published in: Nature communications 2019-10, Vol.10 (1), p.4468-13, Article 4468
• Main Authors: Kobayashi, Ryota, Kurita, Shuhei, Kurth, Anno, Kitano, Katsunori, Mizuseki, Kenji, Diesmann, Markus, Richmond, Barry J., Shinomoto, Shigeru
• Format: Article
• Language: English
• Subjects: 631/378; 631/378/116; 631/378/116/2394; Action Potentials - physiology; Algorithms; Animals; Circuit diagrams; Circuits; Data processing; Generalized linear models; Hippocampus; Hippocampus - anatomy & histology; Hippocampus - cytology; Hippocampus - physiology; Humanities and Social Sciences; Information processing; Linear Models; Models, Neurological; multidisciplinary; Neural networks; Neural Pathways - physiology; Neurons; Neurons - cytology; Neurons - physiology; Rats; Science; Science (multidisciplinary); Spikes; Statistical models; Synaptic Potentials - physiology
• ISSN: 2041-1723; 2041-1723
• DOI: 10.1038/s41467-019-12225-2

State-of-the-art techniques allow researchers to record large numbers of spike trains in parallel for many hours. With enough such data, we should be able to infer the connectivity among neurons. Here we develop a method for reconstructing neuronal circuitry by applying a generalized linear model (GLM) to spike cross-correlations. Our method estimates connections between neurons in units of postsynaptic potentials and the amount of spike recordings needed to verify connections. The performance of inference is optimized by counting the estimation errors using synthetic data. This method is superior to other established methods in correctly estimating connectivity. By applying our method to rat hippocampal data, we show that the types of estimated connections match the results inferred from other physiological cues. Thus 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. Current techniques have enabled the simultaneous collection of spike train data from large numbers of neurons. Here, the authors report a method to infer the underlying neural circuit connectivity diagram based on a generalized linear model applied to spike cross-correlations between neurons.