A convolutional neural network for estimating synaptic connectivity from spike trains

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.

Deconvolution improves the detection and quantification of spike transmission gain from spike trains

“…To quantify the effect of the ACH on the estimation of effective connectivity, we used five different methods: tails, jitter, median, GLMCC 40 , and CoNNECT 41 . While the tails, jitter, and median filtering methods yield estimates of effective connectivity measured as gain (STG; Fig.…”

Distilling multi-scale neural mechanisms from diverse unlabeled experimental data using deep domain-adaptive inference framework

“…The connectivity between neuron pairs can be reflected by their cross-correlograms (CCGs) (Ostojic et al, 2009). CCGs are a series of time-lagged correlations of two spike trains, which are widely used to characterize the patterns of neural connectivity (Aertsen & Gerstein, 1985; Endo et al,2021; English et al, 2017; Kobayashi et al, 2019). Therefore, CCG is the input to our framework, while the presence or absence of connections between neuron pairs is the output ( Fig.…”

“…The copyright holder for this preprint this version posted October 7, 2022. We compared the inference accuracy of our framework with two SOTA methods that also use CCGs to infer monosynaptic connectivity, CoNNECT (Endo et al, 2021) and GLMCC (Kobayashi et al, 2019) (Methods). The experimental data were collected by simultaneous extra-and juxtacellular recordings in the CA1 region of freely running mice (English et al, 2017).…”

“…To tackle this challenge, many advanced machine learning techniques have been introduced and made significant progress (Ben-Shalom et al, 2019; Endo et al, 2021; Gonçalves et al,2020; Sheng et al, 2021), as a result of their extraordinary abilities in dealing with complex high-dimensional data. The common approach used by them is synthesizing a training dataset with “ground-truth” labels generated by computational models and then applying the trained machine learning model directly to experimental data.…”

From End to End: Gaining, Sorting, and Employing High-Density Neural Single Unit Recordings

“…Up to this point, we presented spike sorting as a complete procedure that seeks to assign a signal to a particular source, but regarding it as a piece of puzzle toward another problem solution is also valid. Spike sorting can be embedded in synaptic connectivity estimation algorithms, thus helping construct neuronal circuit diagrams (Endo et al, 2021 ). By combining spike sorting with phasic unit selection, we can recognize firing patterns in structures that have timekeeping properties (Chrobok et al, 2021 ).…”