Abstract
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.
Highlights
State-of-the-art techniques allow researchers to record large numbers of spike trains in parallel for many hours
Because reconstructing connectivity is not guaranteed to reflect anatomical connectivity[31,32,33], we evaluate the accuracy of estimation by directly comparing the estimated connections with the true connections, using synthetic data generated by simulating a network of Hodgkin–Huxley (HH)-type neurons or a large network of leaky integrate-and-fire (LIF) neurons
We explore the CC for the evidence of a monosynaptic impact of a few milliseconds using the generalized linear model (GLM)
Summary
State-of-the-art techniques allow researchers to record large numbers of spike trains in parallel for many hours. The second approach, which we take here, is to use all of the data to carry out mesoscopic neuroanatomy, that is, to reveal the fine neuronal circuitry in which neural circuit computation is carried out From these high channel count recordings, one should be able to estimate neuronal connectivity by quantifying the degree to which firing from a given neuron is influenced by the firing of neurons from which the index neuron is receiving input[14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30]. Because reconstructing connectivity is not guaranteed to reflect anatomical connectivity[31,32,33], we evaluate the accuracy of estimation by directly comparing the estimated connections with the true connections, using synthetic data generated by simulating a network of Hodgkin–Huxley (HH)-type neurons or a large network of leaky integrate-and-fire (LIF) neurons We apply this method to spike trains recorded from rat hippocampus. We compare our estimates of whether an innervating connection is excitatory or inhibitory with the results obtained by manually analyzing other physiological information such as spike waveforms, autocorrelograms, and mean firing rate
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