Model-based detection of putative synaptic connections from spike recordings with latency and type constraints

Abstract

Detecting synaptic connections using large-scale extracellular spike recordings presents a statistical challenge. While previous methods often treat the detection of each putative connection as a separate hypothesis test, here we develop a modeling approach that infers synaptic connections while incorporating circuit properties learned from the whole network. We use an extension of the Generalized Linear Model framework to describe the cross-correlograms between pairs of neurons and separate correlograms into two parts: a slowly varying effect due to background fluctuations and a fast, transient effect due to the synapse. We then use the observations from all putative connections in the recording to estimate two network properties: the presynaptic neuron type (excitatory or inhibitory) and the relationship between synaptic latency and distance between neurons. Constraining the presynaptic neuron's type, synaptic latencies, and time constants improves synapse detection. In data from simulated networks, this model outperforms two previously developed synapse detection methods, especially on the weak connections. We also apply our model to in vitro multielectrode array recordings from mouse somatosensory cortex. Here our model automatically recovers plausible connections from hundreds of neurons, and the properties of the putative connections are largely consistent with previous research.

A convolutional neural network for estimating synaptic connectivity from spike trains

“…There are many algorithms that were developed to estimate synaptic connections from spike trains [1-13, 19, 29-31]. We compared CoNNECT with the conventional cross-correlation method (CC) [19], Jittering method [4], Extended GLM [13], and GLMCC [21] for their ability to estimate connectivity using synthetic data. Figure 3 shows connection matrices determined by the four methods referenced to the true connection matrices.…”

“…There were many possible sources for the lack of reliability and specificity, such as large fluctuations produced by external signals or higher-order interactions among neurons. Over the years, there have been many attempts to minimize the presence of such spurious connections, by shuffling spike trains [2], by jittering spike times [3][4][5][6], or by taking fluctuating inputs into account [7][8][9][10][11][12][13]. These, in general, helped eliminate the FPs, but they then tended to be conservative, giving rise to false negatives (FNs), i.e., missing existing connections.…”

“…These methods, therefore, have different strengths and weaknesses and can be used in combination in a complementary manner. Namely, the inference given by CoNNECT can be used for guiding GLMCC to search for suitable parameters, and GLMCC can provide interpretation.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 CoNNECT with that of GLMCC, as well as the classical cross-correlogram method [19,20], the Jittering method [4,5], and an extended GLM method [13]. After confirming the performance of the model, we applied CoNNECT to parallel spike signals recorded from three cortical areas of monkeys and obtained estimation of the local neuronal circuitry among many neurons.…”

A convolutional neural network for estimating synaptic connectivity from spike trains

“…There were many possible sources for the lack of reliability and specificity, such as large fluctuations produced by external signals or higher-order interactions among neurons. Over the years, there have been many attempts to minimize the presence of such spurious connections, by shuffling spike trains 2 , by jittering spike times 3 – 6 , or by taking fluctuating inputs into account 7 – 13 . These, in general, helped eliminate the FPs, but they then tended to be conservative, giving rise to false negatives (FNs), i.e., missing existing connections.…”

“…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 CoNNECT with that of GLMCC, as well as the classical cross-correlogram method 19 , 20 , the Jittering method 4 , 5 , and an extended GLM method 13 . After confirming the performance of the model, we applied CoNNECT to parallel spike signals recorded from three cortical areas of monkeys and obtained estimation of the local neuronal circuitry among many neurons.…”