Abstract
The recent increase in reliable, simultaneous high channel count extracellular recordings is exciting for physiologists and theoreticians because it offers the possibility of reconstructing the underlying neuronal circuits. We recently presented a method of inferring this circuit connectivity from neuronal spike trains by applying the generalized linear model to cross-correlograms. Although the algorithm can do a good job of circuit reconstruction, the parameters need to be carefully tuned for each individual dataset. Here we present another method using a Convolutional Neural Network for Estimating synaptic Connectivity from spike trains. After adaptation to huge amounts of simulated data, this method robustly captures the specific feature of monosynaptic impact in a noisy cross-correlogram. There are no user-adjustable parameters. With this new method, we have constructed diagrams of neuronal circuits recorded in several cortical areas of monkeys.
Highlights
Background noiseBecause our model network is smaller than real mammalian cortical networks, we added a background current to represent inputs from many neurons, as previously done by Destexhe et al.[11,50].RIbg = gebg(t) vm − Eebg + gibg(t) vm − Eibg . (12)The summed conductance RIbg represents random bombardments from a number of excitatory and inhibitory neurons
We evaluated the accuracy of estimation by comparing the inference with the true connections, using synthetic data generated by simulating circuitries of model neurons, and compared the performance of Connectivity from spike Trains (CoNNECT) with that of GLMCC, as well as the classical cross-correlogram m ethod[19,20], the Jittering m ethod[4,5], and an extended generalized linear model (GLM) method[13]
We have found that the connections among recorded units are sparse; they are less than 1% for all three datasets
Summary
The summed conductance RIbg represents random bombardments from a number of excitatory and inhibitory neurons. The dynamics of excitatory or inhibitory conductances can be approximated as a stationary fluctuating process represented as the Ornstein–Uhlenbeck process[51], dgXbg dt gXbg − gXbg,[0] τsb,Xg. 2σXbg τsb,Xg ξ (t), (13). The hippocampal neurons are subject to the theta oscillation of the frequency range of 3 − 10 (Hz)[52]. To reproduce such oscillations that are observed in the cross-correlogram, we introduced slow oscillations into the background noise for excitatory neurons, as dgebg dt gebg − geb,g0 τsb,eg
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